This study aimed to evaluate the associations of baseline income, cumulative income exposure, and income volatility with the incidence of pancreatic and biliary tract cancers in a nationwide Korean cohort. We analyzed 3,361,091 adults aged 30-65 years who underwent the 2012 National Health Insurance Service (NHIS) health screening. Income level was derived from insurance premium data assessed over the five years preceding baseline (2008-2012) and categorized into baseline income quartiles, cumulative exposure to low or high income, and income volatility based on annual percentage changes. Incident pancreatic and biliary tract cancers were identified using diagnostic codes and the copayment reduction registry. Associations were evaluated using Cox proportional hazards models with adjustment for demographic, lifestyle, and clinical covariates, and cumulative incidence was compared using Kaplan-Meier curves. During a median follow-up of 9.6 years, 14,469 pancreatic cancers and 6,647 biliary tract cancers were newly diagnosed. Lower baseline income was associated with a higher risk of pancreatic and biliary tract cancers, whereas sustained high-income exposure was associated with reduced risk. Cumulative low-income exposure showed a positive linear trend with pancreatic cancer incidence. Income volatility was modestly associated with pancreatic cancer and was positively associated with biliary tract cancer in the fully adjusted model. These associations were generally consistent across subgroups, with a stronger inverse association between prolonged high-income exposure and pancreatic cancer among individuals without diabetes. Income level and income stability were significantly associated with the incidence of pancreatic and biliary tract cancers. Lower baseline income was associated with higher risk, whereas sustained high-income exposure was protective. Income volatility was associated with increased cancer risk, particularly for biliary tract cancer. These findings highlight the importance of incorporating income dynamics into cancer prevention strategies and addressing socioeconomic instability among vulnerable populations.
Cancer is a leading cause of death in China, and its epidemiological profile has shifted markedly in recent years. This review summarizes contemporary trends in cancer incidence and mortality, delineates the major modifiable risk factors, and highlights recent national efforts to ease the burden of cancer. In 2022, China recorded 4.8 million new cancer cases (crude rate: 341.7 per 100,000) and 2.5 million cancer deaths (182.3 per 100,000). Lung, colorectal, thyroid, liver, stomach, and female breast cancers accounted for 65% of all diagnoses, while lung, liver, stomach, colorectal, and esophageal cancers constituted 67.5% of cancer deaths. Notable shifts in sex-specific rankings underscored the rising mortality burden of prostate, female breast, and cervical cancers. China has made measurable progress in cancer control. Between 2000 and 2018, the age-standardized mortality rate for all cancers declined by approximately 1.3% annually, and the age-standardized 5-year relative survival improved from 30.9% in 2003-2005 to 43.7% in 2019-2021. According to Global Burden of Disease (GBD) 2023, nearly half of cancer deaths and disability-adjusted life years (DALYs) were attributable to modifiable risk factors such as tobacco, air pollution, high alcohol use, dietary risks, and unsafe sex. In response, the government has implemented a suite of prevention-oriented policies, including expansion of human papillomavirus (HPV) vaccination, strengthened tobacco control, sustained air pollution reduction, enhanced health education, and broadened cancer screening coverage. Collectively, these initiatives demonstrate a sustained national commitment to reducing the cancer burden.
Immune checkpoint inhibitor-related pneumonitis (ICIP) is a common and potentially life-threatening adverse event with non-specific symptoms. It is of significance to identify high-risk population of ICIP. However, existing prediction models for ICIP are often limited by their reliance on clinically inaccessible variables and homogeneous methodologies, hindering their clinical utility. This study aimed to develop a clinical risk-prediction model for ICIP in patients with gastrointestinal (GI) cancer based on four machine learning (ML) methods. We conducted a retrospective analysis of data from GI cancer patients who received immune checkpoint inhibitors (ICIs) between 2018 and 2022 in Beijing Cancer Hospital. For each patient, 36 clinical indicators associated with pneumonia risk were gathered. The dataset was split into training and testing sets in a ratio of 7:3. Variable selection was first performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression. Subsequently, four ML algorithms: logistic regression (LR), random forest (RF), Support vector machine (SVM), and Adaptive Boosting (AdaBoost), were employed to develop and validate ICIP prediction models. The models' performance was assessed using sensitivity, specificity, precision, F1-score, and the area under the receiver operating characteristic curve (AUC) value. The optimal cutoff point for the best model was determined and a web-based tool was developed based on it. We collected medical data from 1,101 GI cancer patients. Ten predictive variables were identified as significant: gender, age, treatment line, smoking index, drinking history, lung metastasis, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, hemoglobin, and albumin. After constructing and comparing four ML models, the RF model demonstrated best performance with an AUC of 0.899. The web-based tool for ICIP risk prediction is available at https://healthy.aistarfish.com/business/pneumonia-prediction/#/home. We analyzed 36 clinical predictors of ICIP in 1,101 patients treated with ICIs, and 10 variables were included. The smoking index, albumin and hemoglobin emerged as novel predictors specific to GI cancers. Among the models constructed using four ML methods, the RF model showed the best performance. Additionally, a web-based tool was developed to facilitate the early clinical identification of populations at high risk of ICIP. Future directions include external validation of the model to enhance clinical usability.
Lung cancer is the most lethal malignancy worldwide, largely due to its late detection after its progression to advanced stages. Over the last decade, artificial intelligence (AI) applications have shown significant potential in transforming lung cancer diagnostics by improving the speed, accuracy, and personalization of early detection strategies. This review provides a comprehensive overview of current AI application landscape in early lung cancer diagnosis, encompassing medical imaging, histopathology, liquid biopsy, natural language processing of electronic health records, and genomic profiling. We explain how machine learning, deep learning, and transformer-based models are employed in lung cancer diagnosis, and summarize recent cutting-edge advances, including multimodal AI platforms and Food and Drug Administration (FDA)-approved computer-aided diagnosis/detection (CAD) systems. Furthermore, we evaluate the challenges that impede clinical translation, including data heterogeneity, interpretability, and privacy, and present prospective directions such as federated learning and multi-omics integration. Through a comprehensive analysis of the dynamic evolution of AI applications in oncology, we aim to inform researchers, clinicians, and policymakers about its diagnostic potential and translational relevance in clinical practice.
Breast cancer represents a significant and growing public health challenge in China, marked by a rising incidence and distinct variations across age groups and geographical regions. This review synthesizes recent evidence regarding the epidemiology, early detection, and early treatment in the Chinese context. We outline current patterns of disease burden and the spectrum of risk factors-both modifiable and non-modifiable. We note ongoing shifts linked to reproductive trends, lifestyle changes, and an aging population. Screening practices are increasingly evolving towards stratified, risk-adapted pathways. These approaches often combine mammography with adjunct imaging modalities such as ultrasound, digital breast tomosynthesis, or magnetic resonance imaging for selected populations, while artificial intelligence is under active investigation to enhance image interpretation and streamline workflow. Contemporary early management strategies emphasize breast-conserving surgery and selective axillary surgery, alongside the expanded application of hypofractionated and precision-targeted radiotherapy. Systemic therapy is increasingly guided by tumor subtype. In the adjuvant setting, molecular profiling and multigene assays are now routinely utilized to tailor treatment intensity to individual tumor biology, facilitating both treatment escalation or de-escalation where appropriate. Concurrently, in the neoadjuvant setting, research efforts within China and globally are focused on evaluating novel therapeutic regimens and biomarker-driven strategies to improve pathologic complete response rates and inform subsequent postoperative care. A consolidated understanding of these evolving themes is crucial for shaping effective clinical practice and health policies, ultimately supporting the goals of earlier diagnosis and improved patient outcomes in China.
Plasma cell-free DNA (cfDNA) methylation has shown potential in the detection and prognostic testing of multiple cancers. Here, we comprehensively investigate the performance of cfDNA methylation for gastric cancer (GC) detection and prognosis. GC-specific differentially methylated regions (DMRs) were identified by sequencing 56 GC tissues and 59 normal adjacent tissues (NATs). We then performed targeted bisulfite sequencing of cfDNA from 294 GC and 446 non-gastric cancer (NGC) plasma samples, identifying 179 DMRs that overlapped with those in tissue samples. The efficacy of plasma cfDNA methylation markers for GC detection and prognosis was evaluated. Based on the 179 DMRs overlapping with those in tissue samples, the random forest (RF) model using 28 DMRs achieved an area under the curve (AUC) of 0.998 in the training cohort, whereas further refinement to the top 6 DMRs resulted in an AUC of 0.985. Consistent results were obtained in the validation cohort (28 DMR AUC: 0.985; 6 DMR AUC: 0.988). Support vector machine (SVM) and logistic regression (LR) models also demonstrated robust performance. Additionally, an 11-DMR signature was developed for prognostic prediction, successfully identifying high-risk GC patients with significantly shorter overall survival. Our study highlights the potential utility of cfDNA methylation markers for both the detection and prognostication of GC.
The National Health Commission of the People's Republic of China Guidelines for Diagnosis and Treatment of Colorectal Cancer (2025 edition), based on evidence-based medicine, integrates cutting-edge international advances with Chinese clinical practice, and supplements and completes the previous versions. This version of the guidelines, retains the core diagnostic and treatment framework, highlights new contents such as "Surgical treatment of anal canal cancer" and "New technologies and advances in diagnosis and treatment", and systematically summarizes the core points in the surgical treatment, medical oncology treatment, radiation oncology treatment, imaging, and pathology treatment. It is designed to help clinicians quickly grasp the key points of the guidelines and promote the standardization, precision, and consistence of colorectal cancer diagnosis and treatment.
Cisplatin-based chemotherapy is a cornerstone for bladder cancer treatment, but the development of resistance remains a major clinical challenge. Curcumol, a bioactive sesquiterpenoid derived from Curcumae Rhizoma, has shown anti-tumor potential. This study investigated the efficacy of curcumol in overcoming cisplatin resistance and elucidated its underlying molecular mechanisms in bladder cancer progression. Clinical correlation was assessed in patients receiving neoadjuvant chemotherapy with or without Curcumae Rhizoma. The anti-tumor effects of curcumol were evaluated in both cisplatin-sensitive and cisplatin-resistant bladder cancer cells. Multi-omics approaches, including RNA sequencing, proteomics and metabolomics, were employed. Key mechanisms involving H3K9 lactylation (H3K9la) were explored via Western blotting, immunohistochemistry, and cleavage under targets and tagmentation (CUT&Tag) assays. The role of the identified target ORC6 was validated through genetic knockout and overexpression. Finally, ferroptosis was confirmed by measuring lipid peroxidation [malondialdehyde (MDA)], total iron levels, and ferroptosis-related protein markers in vitro. Clinical data indicated that patients administered Curcumae Rhizoma exhibited enhanced responses to neoadjuvant chemotherapy. In addition, curcumol suppressed the proliferation, migration, and invasion of both bladder cancer cells and cisplatin-resistant cells. Mechanistically, proteomic analysis and non-targeted metabolomics revealed that curcumol suppresses glycolysis and lactate production. Subsequently, Western blotting analysis demonstrated a marked reduction in H3K9la levels in both T24 and 5637 cells following curcumol treatment. This decrease in H3K9la was also observed in patient tumor tissues via immunohistochemistry staining. CUT&Tag analysis identified that H3K9la is enriched with the highest number of reads at the ORC6 promoter region. Combined in vitro and in vivo experiments indicated that OCR6 exerted a tumor-promoting effect on bladder cancer. Its knockout induced G0/G1 phase arrest and enhanced apoptosis, while its expression contributed to cancer progression by enhancing invasive and migratory capabilities. Furthermore, ORC6 overexpression correlated with ferroptosis scores and ferroptosis-related genes. In vitro, OCR6 knockout promoted ferroptosis via DNA damage, characterized by elevated MDA content, decreased expression of core ferroptosis-related proteins (GPX4 and SLC7A11), increased percentage of γH2AX-positive cells and longer DNA tails. Finally, we performed rescue experiments using a ferroptosis inhibitor in ORC6 knockout cells, which indicated that ferroptosis inhibitor could weaken the effect of ORC6 knockout on the invasive, migratory, and proliferative capacities. Our findings demonstrated that curcumol effectively counteracted cisplatin resistance and inhibited bladder cancer progression by targeting the glycolysis-H3K9la-ORC6 axis to induce ferroptosis. This study established a critical link between metabolic reprogramming, histone lactylation, and ferroptosis, providing a novel therapeutic avenue for treating chemoresistant bladder cancer.
This study aimed to clarify the predictive value of the neutrophil percentage to albumin ratio (NPAR) for postoperative survival in patients with locally advanced gastric cancer (LAGC) and to develop a prognostic model on the basis of these findings. Patients with LAGC who underwent radical surgery at Peking University Cancer Hospital between June 2010 and January 2021 were retrospectively screened. Three analytical approaches, percentile classification, cutoff value analysis, and restricted cubic splines (RCS), were utilized to evaluate the association between the NPAR and 3-year disease-free survival (DFS) rates. Survival curves were generated using the Kaplan-Meier method. Univariate and multivariate Cox regression analyses were used to identify the risk factors for 3-year DFS. A novel prognostic nomogram was then constructed and validated using both internal validation (via a 7:3 random split) and temporal external validation (stratified by the surgery dates before and after December 2017). A total of 348 patients were enrolled. All three analytical approaches consistently identified a high NPAR as a robust predictor of an unfavorable prognosis. Specifically, NPAR>1.70 (75th percentile) was associated with significantly lower DFS rates [hazard ratio (HR)=2.506; 95% confidence interval (95% CI): 1.964-3.198]. The optimal cutoff analysis confirmed that NPAR of 1.60 exhibited good discriminative ability (HR=3.639; 95% CI: 2.640-5.016), whereas the RCS analysis revealed a non-linear dose-response relationship between an elevated NPAR and low DFS rates. Multivariate Cox regression revealed the NPAR as an independent risk factor, along with pathological stage, differentiation type, and incomplete postoperative chemotherapy. The resulting prognostic nomogram demonstrated excellent and stable predictive performance across both internal and external temporal validation. The NPAR was strongly associated with 3-year DFS rates in LAGC patients. Nomogram based on the NPAR exhibits high predictive accuracy and represents a practical tool for assessing DFS in clinical practice.
Given the improved sensitivity of magnetic resonance imaging (MRI) for detecting ductal carcinoma in situ (DCIS), the omission of routine mammography (MG) or digital breast tomosynthesis (DBT) in high-risk breast cancer screening is under consideration. We aim to conduct a systematic review and meta-analysis to compare the screening sensitivity of MRI, MG and DBT for detecting DCIS in high-risk females. PubMed, Embase, and Web of Science were searched for studies reporting the sensitivity of detecting DCIS in high-risk females up to July 02, 2025. Study quality was assessed with quality assessment of diagnostic accuracy studies-2 (QUADAS-2). Pooled sensitivity was estimated using a random-effects model, overall and stratified by age (<40 and ≥40 years old) and BRCA status (BRCA1 and BRCA2). Meta-regression was used to compare modalities. Seventeen studies (18,348 participants, 211 with DCIS) were included. MRI showed significantly higher pooled sensitivity [85%, 95% confidence interval (95% CI): 74%-94%] than MG (36%, 95% CI: 23%-50%; P<0.001). No DBT data were available. Combined MRI and MG yielded the highest sensitivity (99%, 95% CI: 97%-100%), but offered no significant gain over MRI alone in females <40 years old (P=0.091) and in BRCA1 mutation carriers (P=0.143). MRI is more sensitive than MG for DCIS detection in high-risk females. In females <40 years old and BRCA1 mutation carriers, adding MG to MRI provides no additional diagnostic value. Considering the potential trade-offs, the routine use of MG in these subgroups should be carefully reconsidered.
Non-diagnostic thyroid nodules (Bethesda I) account for 5%-20% of all thyroid nodules. Accurate differentiation of benign and malignant nodules can reduce unnecessary surgeries and repeat biopsies. Herein we evaluated the diagnostic efficacy of multigene testing in non-diagnostic thyroid nodules and developed a predictive model integrating molecular and clinical data. In this prospective cohort study, 1,175 patients with thyroid nodules were evaluated for inclusion, of which 218 patients with Bethesda I nodules met our inclusion criteria. The primary outcome was diagnostic accuracy of molecular testing, and the secondary outcome was the performance of a predictive model integrating molecular and clinical data. Final histopathology identified 165 benign and 53 malignant nodules. Molecular testing detected 10 distinct point mutations and seven gene fusions. Among benign nodules, 147 tested negative and 18 tested positive, whereas 44 malignant nodules tested positive and nine tested negative. In nodules with ultrasound grades 4-5 and fine-needle aspiration cytology (FNAC) results categorized as non-diagnostic, molecular testing achieved sensitivity of 83.00%, specificity of 89.00%, positive predictive value (PPV) of 71.00%, negative predictive value (NPV) of 94.20%, and overall accuracy of 87.60%. The predictive model incorporated 18 clinical and 19 molecular features. Eleven non-zero predictors were selected via least absolute shrinkage and selection operator (LASSO), and the model achieved area under curve (AUC) of 0.95 in the training set and 0.96 in the testing set. Decision curve analysis indicated greater net benefit compared with conventional diagnostic approaches. Molecular testing significantly improved diagnostic accuracy for Bethesda I thyroid nodules. Integrating molecular and clinical data enabled the development of a robust predictive model, facilitating precise, individualized patient management and reducing the need for repeat FNAC and unnecessary surgeries.
Neoadjuvant therapy (NAT) has become the standard treatment option for patients with locally advanced breast cancer. How to non-invasively screen out patients with pathological complete response (pCR) after NAT has become an urgent world-wide clinical problem. Our work aims to the assessment of neoadjuvant treatment response in breast cancer patients for higher accuracy prediction using innovative artificial intelligence system. In this study, we retrospectively collected longitudinal (pre-NAT and post-NAT) multi-parametric magnetic resonance imaging (MRI) and clinicopathologic data of a total of 1,315 breast cancer patients (clinical stage I-III) who had undergone NAT followed by standard surgery and treated across 5 independent medical centers from January 2010 to January 2023. We used radiomics, 3D convolutional neural network technology and clinical data statistical analysis methods to extract and screen multimodal features, and then developed and validated a Clinical-Radiomics-Deep-Learning (CRDL) model to predict patients' pCR outcomes based on multimodal fusion features. We use the area under the receiver operating characteristic curve (AUC) in the primary cohort (PC) and 3 external validation cohorts (VC1-3) to evaluate the model performance. The results showed that the AUC in the PC composed of 2 medical centers was 0.947 [95% confidence interval (95% CI): 0.931-0.960], and the AUC values in VC1-3 were 0.857 (95% CI: 0.810-0.901), 0.883 (95% CI: 0.841-0.918) and 0.904 (95% CI: 0.860-0.941), respectively. The CRDL model demonstrated high accuracy and robustness in predicting pCR to NAT using multimodal fusion data. This study provides a strong foundation for non-invasive assessment of pCR status in breast cancer patients following NAT and offers critical insights to guide clinical decision-making in post-NAT treatment planning.
Esophageal cancer (EC) is one of the most common malignancies in China, accounting for over half of the world's new cases and deaths, and posing a severe threat to national health. Currently, the prevention and control of EC primarily rely on secondary prevention through screening, early diagnosis and early treatment to reduce EC-specific mortality. Upper gastrointestinal endoscopy with Lugol's iodine or optical staining followed by histopathological diagnosis of biopsy samples, serves as the gold standard for EC screening and has been widely implemented in numerous national public health programs for early cancer detection and treatment. Nevertheless, traditional pathology-centered diagnostic and surveillance strategies face significant challenges in current screening practices. Relying on large-scale population-based screening cohorts, including the Efficacy of Endoscopic Screening for Esophageal Cancer in China (ESECC) trial (ClinicalTrials.gov, identifier NCT01688908), a series of investigations into the diagnosis and surveillance of EC screening provided critical new insights and evidence for optimizing endoscopic screening strategies for EC in China. Summarizing relevant research findings from the above investigations, this article provides a systematic review of the epidemiology, current screening diagnostic and surveillance strategies, existing challenges, and key innovations related to EC. These insights offer valuable guidance for public health practice and clinical decision-making in the prevention and screening of EC and other cancers.
Microscopically positive resection margins (R1) in gastric cancer have been associated with poor outcomes, but evidence regarding its prognostic significance across different stages remains inconsistent. This study investigated the impact of R1 resection on survival outcomes and evaluated the prognostic significance of detailed pathological characteristics of margin involvement. This retrospective study analyzed 10,165 patients who underwent curative-intent gastrectomy for gastric cancer between 2007 and 2021. Propensity score matching was performed at a 1:3 ratio between R1 (n=45) and R0 (n=130) cases. For R1 margins, detailed pathological assessment included involvement length, proportion, depth, and histological features. Survival outcomes were evaluated across all stages, and the impact of subsequent resection was analyzed. After propensity score matching, R1 resection showed significantly lower 5-year overall survival rates compared to R0 resection across all stages (stage I: 60.0% vs. 90.9%, P=0.008; stage II: 40.0% vs. 83.3%, P=0.001; stage III: 20.0% vs. 35.4%, P<0.001). In R1 cases, tumor involvement length ≤1 cm (P<0.001), proportion ≤10% (P=0.012), and mucosal-only involvement (P=0.004) were associated with better survival. Patients who underwent subsequent resection to achieve R0 status showed better survival than those with persistent R1 resection (53.8% vs. 26.7%, P<0.001) and comparable survival to matched R0 cases (53.8% vs. 46.9%, P=0.320). R1 resection significantly impairs survival across all stages of gastric cancer, with the extent and depth of microscopic involvement influencing prognosis. When R1 status is discovered postoperatively, subsequent resection should be considered to improve survival outcomes.
With the advancement of surgical techniques and enhanced management of early gastric cancer (EGC), minimally invasive function-preserving surgical approaches have emerged as a common goal for patients and clinicians. Laparoscopic-endoscopic cooperative surgery combined with sentinel lymph node navigation surgery (LECS-SNNS) has drawn increasing interest because of its dual benefits of minimal invasiveness and organ function preservation. However, robust evidence-based support for guiding clinical implementation remains limited. To address this gap, we systematically evaluated available studies on the clinical application of LECS-SNNS in EGC and integrated expert insights to formulate 20 recommendations. These included preoperative assessment, surgical techniques, intraoperative endoscopic procedures, pathological evaluation, postoperative care, and follow-up. This consensus aimed to provide comprehensive guidance for the standardized application of LECS-SNNS, thereby advancing precise, minimally invasive, and function-preserving treatment for EGC.
Although distance from the inferior tumor edge to the anal verge (DTAV) is a key predictor for sphincter-preserving surgery (SPS) in mid-low rectal cancer, its utility is limited in the "decision-gray zone" (DTAV, 3-8 cm). Therefore, this study aimed to develop and validate a multiparametric magnetic resonance imaging-based nomogram for individualized preoperative prediction of SPS feasibility. This dual-center retrospective study included 335 patients with rectal adenocarcinoma (DTAV 3-8 cm). Patients were divided into training (n=263) and external validation (n=72) cohorts, and predictors were identified using multivariate logistic regression analysis. Model discrimination was assessed using area under the receiver operating curve (AUC) and calibration via the Hosmer-Lemeshow test. Subgroup analyses were performed across DTAV strata. Four independent predictors were identified: larger DTAV [odds ratio (OR) =5.00, P<0.001)], larger pubococcygeal overlap distance (PCOD) (OR=1.08, P=0.001), transverse diameter of mesorectal fat (TMS) (OR=1.07, P=0.017), and subcutaneous adipose tissue thickness (SAT) (OR=0.94, P=0.016). The Sphincter Preservation Assessment in Rectal Cancer (SPARC) nomogram achieved an AUC of 0.928 [95% confidence interval (95% CI): 0.890-0.956] in the training cohort, outperforming DTAV alone (AUC=0.884, P=0.031) and maintaining an AUC of 0.916 (95% CI: 0.827-0.969) in external validation. Subgroup analysis showed notably improved predictions in the 5-8 cm DTAV subgroup. Decision curve analysis demonstrated a pronounced net clinical benefit across a wide range of threshold probabilities. Interobserver agreement was excellent (intraclass correlation coefficient, 0.890-0.997). The SPARC nomogram reliably predicted SPS feasibility by integrating tumor location with pelvic anatomy and fat distribution. This provides valuable and evidence-based preoperative guidance, especially within the DTAV 3-8 cm gray zone.
Stage III non-small cell lung cancer (NSCLC) is traditionally viewed as a locally advanced disease based on anatomic imaging. It is increasingly recognized as a systemic illness with localized manifestations, as evidenced by high rates of distant recurrence despite aggressive local therapy. Recent landmark trials (e.g., ADAURA, ALINA, PACIFIC, and KEYNOTE 671) have demonstrated that prolonged systemic treatment, including adjuvant targeted therapy in oncogene-driven NSCLC and consolidation immune checkpoint inhibition after chemoradiotherapy, significantly improves survival outcomes. In contrast, intensifying local therapy alone has exhibited limited benefit or even potential harm. Supporting this shift, detection of circulating tumor DNA and circulating tumor cells indicates the presence of micrometastatic disease at the time of diagnosis. We propose an integrated "sandwich" therapeutic framework encompassing three sequential phases: 1) induction with biomarker-guided systemic therapy (e.g., targeted agents or chemoimmunotherapy) to control micrometastases; 2) local consolidation with surgery or radiotherapy tailored to the post-induction tumor extent; and 3) systemic consolidation with prolonged maintenance therapy to eradicate residual disease. This approach underscores the necessity of treating stage III NSCLC as a systemic disease from the outset, integrating prolonged, biomarker-directed systemic strategies within a multimodal curative-intent framework to address both the local and systemic components of the disease.
Gastric cancer (GC) is heterogeneous, and current mismatch repair (MMR)-based classifications incompletely predict response to immune checkpoint inhibitors (ICIs). RNA sequencing (RNA-seq) and immune infiltration profiles from 189 resected GC were used to derive four refined immune-MMR subtypes (R1-R4) by integrating MMR status, survival, and tumor microenvironment (TME) features. Multi-omics profiling and pathway analysis defined subtype biology. External transcriptomic cohorts and an ICI-treated cohort were classified with Nearest Template Prediction (NTP). Immune response-associated genes were identified from responder vs. non-responder comparisons within the ICI-sensitive subtype and validated by multiplex immunohistochemistry (mIHC). R1 showed the best prognosis and highest immunotherapy response with objective response rate (ORR) 54.5%, while R4 had the worst prognosis. R2 represented an immune-unresponsive deficient mismatch repair (dMMR) subset, and R3 captured an immune-active proficient mismatch repair (pMMR) subgroup with moderate therapy sensitivity. Multi-omics integration revealed subtype-specific pathways (e.g., ECM remodeling in R1, metabolic reprogramming in R2). Reclassification of pMMR tumors based on transcriptional similarity to R1 identified a New R3 subset with enhanced immune features and higher ICI response. Eight immune response-associated genes (e.g., CXCL10, CXCL11, ELN, GAD1, IL32, MT1E, OR2I1P, SLC3A1) were identified and validated by mIHC for predictive relevance. This immune-based molecular framework refines risk stratification beyond conventional MMR categories, identifies ICI-sensitive subsets among both dMMR and pMMR tumors, and proposes candidate biomarkers for patient selection.
Chemotherapy-based regimens remain the standard first- and second-line treatment options for patients with driver gene-negative non-small cell lung cancer (NSCLC). However, in real-world settings, certain patients cannot tolerate chemotherapy or opt to decline it. Immune checkpoint inhibitors (ICIs) constitute the preferred chemotherapy-free alternative. To enhance patient prognosis, this study aimed to examine the efficacy of ICIs combined with anlotinib in real-world scenarios. This prospective, multicenter, real-world study evaluated the efficacy and safety of ICIs combined with anlotinib in patients with advanced NSCLC. Patients undergoing first- or second-line treatment were enrolled. The primary endpoint was progression-free survival (PFS), while the secondary endpoints included overall survival (OS), objective response rate (ORR), disease control rate (DCR), and safety. In total, 242 patients were enrolled from 28 centers. The median PFS for the entire cohort was 7.8 [95% confidence interval (95% CI), 7.0-9.5] months, OS events occurred in 112 (46.3%) patients, with a current median OS of 17.0 (95% CI, 15.1-19.4) months. The ORR and DCR were 36.0% (95% CI, 30.2%-42.2%) and 97.9% (95% CI, 95.3%-99.1%), respectively. The median PFS was 9.8 (95% CI, 7.4-12.5) months for first-line therapy and 6.9 (95% CI, 6.0-8.3) months for second-line therapy. Treatment-related adverse events (AEs) occurred in 198 (81.8%) patients, with grade 3-4 AEs reported in 22 (9.1%) patients. This multicenter, real-world study demonstrates that the anlotinib-ICI combination regimen exhibits clinically meaningful efficacy and tolerability as a chemotherapy-free alternative for advanced NSCLC, offering viable evidence to guide treatment for patients who are unsuitable for conventional chemotherapy.
Endocrine resistance occurs in nearly all patients with hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR+/HER2-) early breast cancer (EBC), which can develop local and distant recurrence. The situation highlights the need to explore biomarkers for the efficacy of endocrine therapy (ET). We performed digital spatial profiling on postoperative tumor samples from 20 HR+/HER2- EBC patients receiving adjuvant tamoxifen therapy, based on a designated panel comprising 235 ET-related genes. Paired patients were stratified into resistant and sensitive groups based on ET response. A total of 111 regions in three tissue compartments defined by morphology markers [tumor (PANCK+), leucocytes (CD45+), and nonimmune stroma (CD45-/PANCK-)] were investigated for immune and transcriptomic biomarkers. A total of 27, 13, and 5 differentially expressed genes (DEGs) were identified in PANCK+, CD45+, and CD45-/PANCK- regions. In the PANCK+ regions, mRNA expression of fourteen DEGs was significantly associated with disease-free survival (DFS), among which seven DEGs were further selected to construct a model for DFS. In the model, patients with low-risk scores had a median DFS of 55.77 months, significantly longer than 21.67 months among those with high-risk scores [P=2.1e-4, hazard ratio (HR)=6.73, 95% confidence interval (95% CI) =2.20-20.60)]. The area under curve for 1-year, 3-year, and 5-year DFS was 0.98, 0.95, and 0.91, indicating its superior efficacy. ET-sensitive patients had significantly higher non-classical monocyte infiltration in the CD45+ regions (P=0.03), whereas ET-resistant patients had significantly higher plasma cell infiltration in the CD45-/PANCK- regions (P=0.01). Our study has first demonstrated the spatial transcriptomic landscapes of patients with different responses to ET, which may help stratify patients who are responsive to ET and motivate the exploration of the molecular mechanisms of endocrine resistance.