Cancer Medicine最新文献

{"title":"Beyond the Monolayer: Leveraging 3D Culture Systems to Decode Mechanisms of Drug Resistance and Therapeutic Vulnerabilities in Colorectal Cancer","authors":"Sarah J. Harmych,&nbsp;Bhuminder Singh","doi":"10.1002/cam4.72305","DOIUrl":"10.1002/cam4.72305","url":null,"abstract":"<p>Two-dimensional (2D) monolayer cultures remain the workhorse of colorectal cancer (CRC) research and high-throughput screening (HTS), yet they poorly recapitulate tissue architecture, extracellular matrix (ECM) cues, metabolic gradients, and multicellular heterogeneity that govern drug response in vivo. Over the past decade, three-dimensional (3D) culture systems—including spheroids, patient-derived organoids (PDOs), tissue-originated spheroids (CTOS), and collagen-embedded models—have revealed striking 2D vs. 3D discordance in proliferation, signaling, epithelial-mesenchymal transition (EMT), plasticity, and sensitivity to standard chemo- and targeted therapies. This review synthesizes how 3D CRC models are used to decode mechanisms of primary and acquired resistance to anti-CRC therapies, from metabolic phenotypes and ECM remodeling to EMT, polarity loss, and rare invasive trajectories identified by imaging and quantification of morphologic features of CRC cells grown in 3D. Parallel advances in PDO-based functional precision oncology demonstrate that organoid drug screens can prospectively predict patient response in advanced CRC, while 3D combination screens uncover format-specific synergies that are invisible in 2D. Building on this foundation, we highlight recent technical innovations and discuss how integrating morphometric, metabolic, and trajectory-level readouts can move CRC drug discovery “beyond live/dead.” Rather than propose a single best model, we outline practical guidelines for matching 3D platform choice (cell lines vs. PDO/CTOS, collagen vs. Matrigel, static vs. live-imaging HTS) to mechanistic questions and therapeutic goals in CRC. We propose an integrated roadmap—matrix-embedded 3D cultures, high-content morphological endpoints, trajectory analytics, and rigorous HTS validation—to decode resistance mechanisms and prioritize clinically actionable combinations in CRC.</p>","PeriodicalId":139,"journal":{"name":"Cancer Medicine","volume":"15 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13627246/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149101557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing the ER Cutoff in HER2-Positive Breast Cancer: ER ≥ 50% Predicts Low pCR Rates and Resistance to Antibody-Drug Conjugates in the Neoadjuvant Setting","authors":"Tian Du,&nbsp;Min Lin,&nbsp;Gehao Liang,&nbsp;Yan Wang,&nbsp;Hao Wu,&nbsp;Zixuan Zhao,&nbsp;Luhao Sun,&nbsp;Jun Tang","doi":"10.1002/cam4.72331","DOIUrl":"10.1002/cam4.72331","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Estrogen receptor (ER)-positive/HER2-positive breast cancer demonstrates significantly lower rates of pathological complete response (pCR) to neoadjuvant HER2-targeted therapies compared to ER-negative/HER2-positive disease. However, the optimal ER positivity cutoff for clinically meaningful patient stratification remains undefined.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We analyzed a retrospective cohort of 741 HER2-positive breast cancer patients treated with neoadjuvant chemotherapy plus dual HER2 blockade (trastuzumab and pertuzumab) at Sun Yat-sen University Cancer Center. The optimal ER cutoff for predicting pCR was determined by ROC analysis with Youden index and validated by bootstrap resampling (1000 iterations). Transcriptomic data from TCGA and SCAN-B cohorts were analyzed to characterize biological differences between ER subgroups. Drug response was predicted using the oncoPredict algorithm, and cancer dependency was assessed using DepMap CRISPR screening data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>ER ≥ 50% positivity was identified as the optimal predictive cutoff (bootstrap 95% CI: 25.0%–77.5%) and was an independent predictor of significantly lower pCR rates in multivariate analysis (OR = 0.27; 95% CI: 0.19–0.40; <i>p</i> &lt; 0.001). Transcriptomic analysis revealed that ER ≥ 50% tumors are characterized by activated estrogen response signaling, downregulated cell cycle and immune pathways. Predicted resistance to trastuzumab, T-DM1, and T-DXd was consistently enriched in this subgroup, consistent with lower HER2 and CD16A expression observed in ER ≥ 50% tumors. CCND1, a canonical transcriptional target of ESR1, was significantly upregulated in ER ≥ 50% tumors across both cohorts, and ER + /HER2 + cell lines exhibited significantly higher CCND1 and CDK4 dependency scores in DepMap CRISPR screening (<i>p</i> &lt; 0.05), supporting activation of the ESR1-CCND1-CDK4/6 axis in this subgroup.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>ER ≥ 50% positivity defines a clinically and biologically distinct HER2-positive subgroup with poor response to standard neoadjuvant therapy and predicted resistance to ADCs, consistent with lower HER2 and CD16A expression in this subgroup. The convergent transcriptomic and functional evidence for ESR1-CCND1 axis activation provides mechanistic support for combining CDK4/6 inhibitors with endocrine and anti-HER2 therapies to improve outcomes in this resistant subgroup.</p>\u0000 </section>\u0000 </div>","PeriodicalId":139,"journal":{"name":"Cancer Medicine","volume":"15 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13624533/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149079351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Impact of SF3B1 Mutations on the Tumor Microenvironment and Response to Immunotherapy","authors":"Kyung-Jin Cho,&nbsp;Byungho Lim","doi":"10.1002/cam4.72318","DOIUrl":"10.1002/cam4.72318","url":null,"abstract":"<p>Cancer genomes shape the immune landscape within the tumor microenvironment (TME), thereby influencing host antitumor immunity. Identifying somatic mutations that modulate the TME is critical for selecting patients most likely to benefit from immunotherapy. Here, by integrating a transcriptome-based predictive model with comprehensive genomic analyses, we identified <i>SF3B1</i> splicing factor mutations associated with the response to immune checkpoint inhibitors (ICIs). We systematically evaluated the effects of <i>SF3B1</i> mutations on the TME and ICI response through <i>in silico</i>, in vitro, and in vivo approaches. Comparative analyses of patient genotypes, together with loss- and gain-of-function studies, revealed that <i>SF3B1</i> mutations drive transcriptional reprogramming that enhances ICI responsiveness. To validate these findings in vivo, we established a syngeneic mouse model using immune-resistant B16F10 melanoma cells. SF3B1-mutant tumors, compared with wild-type counterparts, exhibited TME features and immune profiles indicative of an enhanced antitumor immune response, including increased proportions of CD4⁺ and CD8⁺ T cells and dendritic cells, accompanied by the upregulation of immune effectors. Consistently, mice bearing SF3B1-mutant tumors demonstrated significantly greater tumor regression following anti-PD-1 treatment compared with their wild-type counterparts. These findings establish <i>SF3B1</i> mutations as predictive biomarkers for guiding rational immunotherapy selection in cancer patients.</p>","PeriodicalId":139,"journal":{"name":"Cancer Medicine","volume":"15 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13620285/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149072809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Center Evaluation of Opioid Administration or Prescription in Pediatric Oncology Patients Using the OMOP CDM","authors":"Yuqing Feng,&nbsp;Martin Yi,&nbsp;Catherine C. Aftandilian,&nbsp;Melissa P. Beauchemin,&nbsp;Christine Cunningham,&nbsp;David DeStephano,&nbsp;Rhea K. Khurana,&nbsp;Emily Larimer,&nbsp;Jennifer Oberg,&nbsp;Benjamin L. May,&nbsp;Keith Morse,&nbsp;Karthik Natarajan,&nbsp;David Noyd,&nbsp;Victoria Soucek,&nbsp;Yujie Chen,&nbsp;Adam Yan,&nbsp;Lin Lawrence Guo,&nbsp;Lillian Sung","doi":"10.1002/cam4.72310","DOIUrl":"10.1002/cam4.72310","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>Opioid use in pediatric oncology is poorly characterized. The primary objective was to describe opioid administration or prescription patterns in pediatric oncology patients.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In this multi-center retrospective study conducted across three sites, we included patients aged 0–19 years with a malignant neoplastic disease who received their first chemotherapy between January 2019 and March 2024. Data were extracted from each site's Observational Medical Outcomes Partnership Common Data Model. We described patient characteristics based on: (1) first opioid administration or prescription, overall and for each specific opioid; and (2) all opioid administrations or prescriptions, overall and for each specific opioid. We also evaluated variability in opioid and naloxone use.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Of 2903 pediatric patients with cancer, 2707 (93.2%) were administered or prescribed at least one opioid. Among these patients, fentanyl was the most common (92.4%), followed by morphine (68.7%), hydromorphone (55.3%), and oxycodone (38.6%). Across centers, the proportion of patients receiving fentanyl ranged from 90.9% to 94.9%, morphine from 34.0% to 89.2%, oxycodone from 0.9% to 76.1%, and naloxone from 2.4% to 29.5%. Overall, 10.9% were administered or prescribed naloxone, but among 169 methadone patients, 43.5% were administered or prescribed it. Of the 153,849 total opioid administrations or prescriptions, 22.4% occurred more than 12 months after initiating chemotherapy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Among pediatric patients with cancer who receive chemotherapy, 93.2% were administered or prescribed at least one opioid; fentanyl was the most common. There was heterogeneity in opioid choice and naloxone use across sites. These findings lay preliminary groundwork for understanding opioid use patterns and identifying potential areas for practice improvement in pediatric oncology.</p>\u0000 </section>\u0000 </div>","PeriodicalId":139,"journal":{"name":"Cancer Medicine","volume":"15 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13620299/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149072820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CCNE1: A Cell Cycle Regulator That Influences Tumor Progression","authors":"Liang Yan,&nbsp;Siyi Liu,&nbsp;Yanling Li,&nbsp;Qian He,&nbsp;Chen Long,&nbsp;Ning Tang,&nbsp;Yanhong Zhou","doi":"10.1002/cam4.72336","DOIUrl":"10.1002/cam4.72336","url":null,"abstract":"<p>Cyclin E1 (CCNE1), a pivotal member of the cyclin family, governs the G1/S phase transition of the cell cycle by binding to and activating cyclin-dependent kinase 2 (CDK2), thereby initiating DNA replication and driving S-phase entry. In a broad spectrum of human malignancies—including breast, ovarian, gastric, and non-small cell lung cancers—amplification or overexpression of CCNE1 disrupts the orderly regulation of cell cycle progression, leading to uncontrolled proliferation, impaired DNA damage repair, and genomic instability. These oncogenic consequences are intimately associated with increased tumor aggressiveness, poor patient prognosis, and diminished therapeutic efficacy. This review provides an integrated examination of CCNE1 biology, spanning its physiological regulation in the cell cycle, the molecular mechanisms by which its dysregulation drives tumorigenesis and drug resistance, the clinical implications of CCNE1 amplification as both a prognostic biomarker and a predictor of therapeutic response, and the emerging strategies to target CCNE1-driven vulnerabilities. By synthesizing findings across diverse cancer types and regulatory layers, we evaluate the translational potential of CCNE1 as a diagnostic marker and therapeutic target, and highlight key challenges and opportunities for future investigation.</p>","PeriodicalId":139,"journal":{"name":"Cancer Medicine","volume":"15 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13620288/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149072766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Predictions in Non-Small Cell Lung Cancer Using Joint Modeling of Longitudinal and Time-To-Event Outcomes Data","authors":"Christopher R. Pretz,&nbsp;Sara Wienke,&nbsp;Carin R. Espenschied,&nbsp;Samantha I. Liang,&nbsp;Jiemin Liao,&nbsp;Christopher R. Cabanski,&nbsp;Jim Hayes,&nbsp;Victoria Ngo,&nbsp;Enjun Yang,&nbsp;Amar K. Das","doi":"10.1002/cam4.72308","DOIUrl":"10.1002/cam4.72308","url":null,"abstract":"<p>Joint modeling (JM) of longitudinal and time-to-event (TTE) data is a powerful statistical technique that elucidates how temporal changes in a biomarker relate to TTE outcomes while accounting for study confounders. The growing use of next-generation sequencing (NGS) in precision oncology, particularly in capturing circulating tumor DNA (ctDNA) methylation via liquid biopsy, offers new opportunities to apply JM for patient prognostication. In this study, we used JM to analyze a prospective cohort of 251 non-small cell lung cancer patients receiving standard-of-care immune checkpoint inhibitor treatment regimens. The JM is comprised of two sub-models—a hierarchical cubic spline random effects model for the longitudinal biomarker component and a Cox proportional hazards model for the TTE outcome component. Each sub-model incorporated baseline factors of age, gender, smoking status, and cancer stage. The main objective was to establish whether the evolution of a methylation-derived tumor fraction (TF) is associated with patient outcomes. Our investigation revealed that TF is strongly associated with both real-world overall survival and real-world progression-free survival. These results enable the generation of various patient-level predictions that capture the dynamic interplay between TF and patient outcomes, offering a path toward real-time, personalized prognostication. Although validation in larger, multi-center cohorts is needed before routine adoption, this work represents an important step toward integrating dynamic predictions into serial liquid biopsy monitoring. This approach demonstrates the potential of JM to provide more nuanced and adaptive disease evolution monitoring, offering better clinical decision guidance compared to static models or imaging alone.</p>","PeriodicalId":139,"journal":{"name":"Cancer Medicine","volume":"15 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13616821/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149069000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cancer and Hypertension: Shared Mechanisms, Treatment Interactions, and Clinical Management","authors":"Peizhi Jia,&nbsp;Daxin Chen,&nbsp;Jingting Wang,&nbsp;Yupei Guo,&nbsp;Jinhong Liu,&nbsp;Jiumao Lin","doi":"10.1002/cam4.72319","DOIUrl":"10.1002/cam4.72319","url":null,"abstract":"<p>Cancer and hypertension (HTN) frequently coexist, and their relationship is increasingly recognized as a clinically relevant intersection between oncology and cardiovascular medicine. However, the extent to which this association reflects shared risk factors, common biological mechanisms, or treatment-related effects remains incompletely understood. In this narrative review, we focus specifically on the cancer–HTN interface and summarize epidemiological evidence while considering the potential influence of age, obesity, metabolic syndrome, dietary patterns, and psychosocial factors. We further discuss overlapping mechanisms, including chronic inflammation, renin–angiotensin–aldosterone system activation, calcium signaling abnormalities, activation of the sympathetic nervous system, metabolic dysregulation, dysbiosis of the gut microbiota and genetic and epigenetic alterations. Particular attention is given to the bidirectional interactions between cancer therapies and HTN, as well as the heterogeneous evidence concerning potential associations between antihypertensive medications and cancer outcomes. We also summarize the clinical assessment and management of cancer therapy-related HTN, including Common Terminology Criteria for Adverse Events grading, blood pressure monitoring, cardiovascular risk assessment, and current recommendations from the European Society of Hypertension (ESH) and American Heart Association. Emerging issues, including immune checkpoint inhibitor-associated cardiovascular toxicity, the potential role of cardiac biomarkers such as troponin and natriuretic peptides, and the prognostic significance of treatment-induced HTN across different tumor types, are also discussed. Overall, the available evidence supports an important clinical interplay between cancer and HTN but does not establish a uniform causal relationship. Future prospective studies integrating tumor biology, cardiovascular risk factors, treatment exposure, and longitudinal blood pressure (BP) assessment are needed to clarify these interactions and improve individualized cardio-oncology care.</p>","PeriodicalId":139,"journal":{"name":"Cancer Medicine","volume":"15 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13616819/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149068910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pan-Cancer Analysis of the Immunomodulatory Roles and Prognostic Value of PDXP and Experimental Verification in SKCM","authors":"Yingjian Hu,&nbsp;Xinzhu Zeng,&nbsp;Wei Cao,&nbsp;Jianhao Jiao,&nbsp;Yuan Zhao,&nbsp;Jun Wang","doi":"10.1002/cam4.72311","DOIUrl":"10.1002/cam4.72311","url":null,"abstract":"<p>Multiple public databases, including TCGA, GEO, and TARGET, were utilized to comprehensively analyze pyridoxal phosphatase (PDXP) expression profiles across 34 cancer types. We further explored the associations of PDXP expression with clinicopathological features, tumor immune microenvironment, genomic heterogeneity, functional signaling pathways, and therapeutic response. In addition, in vitro and in vivo experiments were performed to preliminarily validate the potential mechanism linking PDXP expression to melanoma progression. PDXP exhibited aberrant differential expression in most cancer types and tended to be highly expressed in malignant tumor cells. PDXP expression was correlated with overall survival outcomes in 14 cancer types. In most investigated tumors, PDXP expression was significantly associated with the expression of immunoregulatory and immune checkpoint genes, as well as the general landscape of the tumor immune microenvironment. Correlation analysis of genomic characteristics indicated that PDXP may serve as a potential immune-related biomarker in skin cutaneous melanoma (SKCM). Functional enrichment and signaling pathway analysis showed that elevated PDXP expression in SKCM was correlated with enhanced tumor cell proliferative activity. In vitro and in vivo functional experiments demonstrated that PDXP knockdown significantly suppressed colony formation and migration of melanoma cells, induced G2/M phase cell cycle arrest, and increased cell apoptosis. These findings suggest that PDXP is associated with malignant phenotypes of melanoma cells, which are accompanied by changes in protein kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling activity. Moreover, we established a prognostic model with moderate predictive performance for melanoma using least absolute shrinkage and selection operator (LASSO)-Cox regression, providing a preliminary prognostic reference for SKCM patients.</p>","PeriodicalId":139,"journal":{"name":"Cancer Medicine","volume":"15 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13616823/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149068916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pragmatic Screening for Sarcopenia in Non-Small Cell Lung Cancer: Development and Internal Validation of a Prospective Risk Model Integrating T12 CT Indices and Routine Clinical Variables","authors":"Zhuorui You,&nbsp;Wei Dai,&nbsp;Hechen Shen,&nbsp;Xing Wei,&nbsp;Cheng Lei,&nbsp;Jia Liao,&nbsp;Jieming Cao,&nbsp;Minxian Li,&nbsp;Jia Wang,&nbsp;Mengqi Shao,&nbsp;Gang Li","doi":"10.1002/cam4.72230","DOIUrl":"https://doi.org/10.1002/cam4.72230","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Sarcopenia or low muscle mass is common in non-small cell lung cancer and predicts poorer outcomes, but its assessment typically relies on CT-based skeletal muscle index at the L3 vertebral level. Standard chest CTs often omit the L3 level, leaving many patients without a straightforward muscle mass measure, so sarcopenia frequently goes undetected. We therefore evaluated whether thoracic CT measurements could reliably substitute for L3 SMI and developed a simple, accurate clinical tool to identify patients at risk of CT-defined low muscle mass.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In retrospective (<i>n</i> = 192) and prospective (<i>n</i> = 177) cohorts of NSCLC patients, cross-sectional muscle area was quantified on CT at L3, T12, and T4. Deming regression and Bland–Altman analysis were used to assess correlations and agreement between T4- or T12-derived SMI and reference L3 SMI. Candidate clinical predictors were selected based on univariate associations and clinical plausibility, and machine-learning methods identified five routine variables, which were combined into the Lung Cancer Patients' Sarcopenia Risk Model (LSRM). Model performance was evaluated in terms of discrimination, calibration, and risk stratification and compared with established clinical indices like BMI and the advanced lung cancer inflammation index (ALI).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>T12-derived SMI showed a strong correlation and minimal bias versus L3, outperforming T4-derived SMI. A conversion equation was established to estimate L3 SMI from T12. The LSRM, incorporating age, body mass index, carcinoembryonic antigen, C-reactive protein, and lymphocyte count, demonstrated good discrimination, satisfactory calibration, and clear three-tier risk stratification. It outperformed BMI and ALI in identifying CT-defined low muscle mass.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>T12 measurements on routine chest CT can replace L3 for muscle assessment. The LSRM provides a practical bedside tool for screening patients at risk of CT-defined low muscle mass in NSCLC without additional imaging, supporting earlier risk identification and integration into routine care.</p>\u0000 </section>\u0000 </div>","PeriodicalId":139,"journal":{"name":"Cancer Medicine","volume":"15 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cam4.72230","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149048323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lanreotide, a Somatostatin Analogue, in Advanced Merkel Cell Carcinoma: A Prospective Single-Arm Phase II Study","authors":"Julie Charles,&nbsp;Aude Belbezier,&nbsp;Stéphane Mouret,&nbsp;Brigitte Dreno,&nbsp;Stéphane Dalle,&nbsp;Céleste Lebbe,&nbsp;Ewa Hainaut-Wierzbicka,&nbsp;Andreea Stefan,&nbsp;Julie de Quatrebarbes,&nbsp;Olivier Dereure,&nbsp;Ouidad Zehou,&nbsp;Sandrine Mansard,&nbsp;Alain Dupuy,&nbsp;Caroline Dutriaux,&nbsp;Florent Grange,&nbsp;Caroline Gaudy-Marqueste,&nbsp;Henri Montaudié,&nbsp;Jean-Philippe Arnault,&nbsp;Yannick Le Corre,&nbsp;Thibault Kervarrec,&nbsp;Jean-Louis Quesada,&nbsp;Mahtab Samimi,&nbsp;Marie-Thérèse Leccia,&nbsp;The Group of Cutaneous Oncology of the French Society of Dermatology","doi":"10.1002/cam4.72295","DOIUrl":"https://doi.org/10.1002/cam4.72295","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Merkel Cell Carcinoma (MCC) is a rare, aggressive neuroendocrine and epithelial skin cancer. Somatostatin analogues, such as lanreotide, have shown efficacy in managing other neuroendocrine tumors. Retrospective studies suggest that lanreotide may induce partial response or disease stabilization in patients with advanced MCC. To test this hypothesis, we conducted a Phase II, non-randomized, open trial investigating lanreotide in MCC patients with advanced disease (ClinicalTrials.gov identifier: NCT02351128).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Patients with Stage IIIB-IV MCC received lanreotide monotherapy (120 mg every 28 days) at any line of systemic therapy, until disease progression or unacceptable toxicity. The primary endpoint was disease control rate (DCR) (defined as the proportion of patients with complete response, partial response, and stable disease) at 3 months. Secondary endpoints included progression-free survival (PFS), overall survival (OS), somatostatin receptor (SSTR) tumor expression, and treatment safety.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Between April, 2015, and November, 2016, 35 patients received lanreotide, with 12 evaluable at 3 months. The DCR at 3 months was 33.3% (4) among evaluable patients (all with stable disease) and 11.4% for the full cohort. Statistical analyses showed no significant difference from the expected 20% response rate (<i>p</i> = 0.205 for intention-to-treat; <i>p</i> = 0.939 for per-protocol). Kaplan–Meier analysis revealed median OS and PFS of 3.1 months (95% CI, 2–5.5) and 3.5 months (95% CI, 2.3–5.5), respectively. SSTR tumor expression did not correlate with treatment response.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Although limited by its implementation before the era of immunotherapy, this non-randomized study shows the limited effectiveness of lanreotide as monotherapy in treating patients with advanced MCC.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Trial Registration</h3>\u0000 \u0000 <p>Identifier: NCT02351128</p>\u0000 </section>\u0000 </div>","PeriodicalId":139,"journal":{"name":"Cancer Medicine","volume":"15 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cam4.72295","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149048325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}