Clinical & Translational Oncology最新文献

Over the past several decades, chimeric antigen receptor (CAR)-T cell therapy has revolutionized cancer immunotherapy, particularly for hematological cancers. However, its effectiveness against solid tumors is often hindered by numerous barriers. Advances in modern epigenomic profiling technologies have deepened our understanding of how epigenetic regulation shapes CAR-T cell behavior. Epigenetic mechanisms act as an unseen force that governs CAR-T cell fate by modulating key genes involved in metabolism, differentiation, phenotype, migration, persistence, and overall function. Recent innovations in epigenetic monitoring and manipulation have provided new strategies to address the challenges that restrict CAR-T cell performance. In this review, we examine the epigenetic landscape of CAR-T cells and discuss potential epigenetic-based strategies to enhance CAR-T cell function and overcome limitations in CAR-T cell therapy. Trial registration number: not applicable.

Purpose: Colorectal cancer liver metastases (CRLM) worsen the prognosis of patients. High-intensity focused ultrasound (HIFU) is a non-invasive treatment for CRLM, but reliable prognostic tools for HIFU patients are lacking. This study aimed to develop and validate a nomogram to predict the risk of 1-year all-cause mortality (ACM) in patients with CRLM receiving HIFU.

Methods: This retrospective cohort study enrolled patients with CRLM who received HIFU between January 2014 and December 2023 at two medical centers. Data from Chongqing Medical University's Second Affiliated Hospital were used as the training set, while data from Suining Central Hospital were used as the external validation set. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) regression to identify relevant predictors and establish a nomogram. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

Results: Among 162 patients (121 in the training set, 41 in the validation set), the 1 year ACM rates were 31.4% and 24.4%, respectively. LASSO regression identified six predictors: sex, number of liver metastases, extrahepatic metastases status, number of extrahepatic organs involved, alkaline phosphatase level (ALP), and albumin level (ALB). The nomogram demonstrated robust performance, with an area under the curve (AUC) of 0.886 (95% CI: 0.816-0.956) in the training set and 0.706 (95% CI: 0.495-0.918) in the validation set. Calibration curves showed good agreement, and DCA confirmed its clinical utility.

Conclusion: A nomogram was developed and externally validated, which provides clinicians with an accurate and reliable tool for individualized prognostic assessment and treatment optimization in patients with CRLM undergoing HIFU.

Background: Bacterial and fungal infections pose significant challenges in the management of patients with haematological malignancies (HM), substantially affecting the impact on clinical outcomes, including prolonged hospitalization and reduced life expectancy. Increased susceptibility to bacterial and/or fungal pathogens can occur due to immunosuppression, either inherent to the haematological disorder or induced by specific therapeutic strategies.

Methods: In this study, we quantified the plasma levels of four cytokines (interferon-γ, interleukin-5, interleukin-17, and transforming growth factor-β) in a cohort of 75 adult HM patients who developed bacterial and/or fungal infections, using a newly modified enzyme-linked immunosorbent assay platform.

Results: We observed significant changes in the plasma cytokine levels at the infection onset. The median concentration levels of T helper 1 (IFN-γ), T helper 2 (IL-5), and T helper 17 (IL-17) cytokines were significantly higher as compared to those during pre-infection and post-infection (P < 0.05). In contrast, the level of regulatory T cells (TGF-β) exhibited a significant decrease at the time of infection compared to the pre-infection time point (P < 0.05). Further analysis revealed an imbalance in cytokine ratios, including Th1/Th2, Th1/Th17, Th1/Treg, Th2/Th17, and Th2/Treg, at the onset of infection, with increased Th1 and Th2-associated cytokines dominating these ratios.

Conclusion: Our results highlighted significant changes in the expression levels of several cytokines at the infection onset of HM patients, reflecting a strong correlation between the inflammatory state and cytokine expression, characterised by a predominance of Th1 and Th2-associated cytokines.

Objective: This study aimed to isolate different cancer cell populations and characterize their secretome profiles to better understand their functional roles in metastasis and tumor progression. For this purpose, we analyzed the secretomes of CD133 and CD326 (EpCAM) positive subpopulations derived from the A549 cell line.

Methods: CD133 positive (cancer stem cell marker) and CD326 positive (pluripotent stem cell marker) cells were isolated from the A549 non-small cell lung cancer cell line using magnetic cell separation. Secretome proteins from these subpopulations, along with parental A549 cells, were analyzed using bottom-up proteomics via liquid chromatography-tandem mass spectrometry (LC-MS/MS). The resulting datasets were further evaluated through bioinformatics analyses.

Results: CD133 positive cells were associated with angiogenesis, mesenchymal stem cell differentiation, and enhanced cell migration. In contrast, CD326 positive cells demonstrated pluripotent characteristics linked to epithelial-mesenchymal transition, neuronal differentiation, and placental morphogenesis, indicating a potential role in metastatic processes. Additionally, SERPINE2 and ADAM10 were identified as potential biomarkers for lung cancer, while YWHAZ and TRIM28 were associated with pluripotent cancer stem cell phenotypes.

Conclusion: These findings support the existence of distinct cancer stem cell subtypes exhibiting multipotent and pluripotent properties. Secretome profiling provides valuable insights into tumor heterogeneity and highlights novel biomarker candidates, offering potential avenues for improved diagnosis and targeted therapeutic strategies in lung cancer.

Lung cancer is the most prevalent cancer and cause of death; most patients present themselves at an advanced stage and continuously acquire resistance to targeted agents, antibody-drug conjugates, chemotherapy, and immune checkpoint inhibitors. In addition to secondary mutations, epigenetically driven cellular plasticity, including DNA methylation, histone modification, chromatin remodeling, RNA (m 6A)-marks, and non-coding RNAs, facilitates resistance coordination, EMT/drug-tolerant persisters, lineage switching (e.g., NSCLC to NSCLC), bypass signaling, and immune evasion by tumor cells. These states can be therapeutically rewired by epigenetic drugs: low-dose DNMT/HDAC priming restores silenced tumor-suppressor and antigen-presentation genes and activates viral-mimicry interferon signaling to augment checkpoint blockade; EZH2 and LSD1 inhibitors target plasticity and neuroendocrine programs; BET inhibition suppresses adaptive transcription; CBP/p300 modulators suppress NRF2-dependent redox survival; Combination therapies exploiting synthetic lethality through PRMT5 inhibition, applied rationally with TKIs, ICIs, chemotherapy, and antibody-drug conjugates (ADCs), are currently under clinical investigation. Biomarker-directed patient selection (e.g., MTAP loss clustering, EZH2/LSD1 activity, methylation and chromatin signatures, and liquid biopsy dynamics of methylation or ctDNA) will be critical to enrich for patients most likely to benefit. In the future, better optimized sequencing using short priming windows, intermittent dosing, and future readouts of prospective pharmacodynamics could transform transient re-sensitization into lasting control. This study aims to critically appraise mechanistic and clinical evidence linking epigenetic plasticity to therapy resistance in advanced lung cancer and to propose biomarker-directed epigenetic combination and sequencing strategies to restore drug sensitivity.

Aim: Four-dimensional computed tomography (4D-CT) is the gold standard for radiotherapy planning in non-small cell lung cancer (NSCLC), yet its use in radiomics remains underexplored. This study proposes a reproducible, scalable methodology for assessing radiomic feature (RF) stability in 4D-CT and evaluates whether image filtering identifies additional stable RFs compared to unfiltered images.

Methods: Early-stage NSCLC patients treated with SBRT with 4D-CT were included. Gross tumor volumes (GTVs) were re-segmented on all available phases. RFs were extracted using PyRadiomics. Features with near-zero variance in > 85% of patients were excluded. RF stability was evaluated using two complementary approaches: (i) coefficient of variation (COV), quantifying the magnitude of inter-phase variability, and (ii) repeated-measures modeling, assessing the presence of a statistically significant association between RF values and respiratory phase. RFs with COV < 5% and 5-10% were considered highly stable and stable, respectively. Repeated-measures analyses were performed separately for expiratory (0-40%) and inspiratory (50-90%) phases.

Results: Seventy patients met the inclusion criteria. 1892 RFs were analyzable. Based on COV, about 21% (397/1892) of RFs were highly stable, and 18% (338/1892) were stable, while the remaining showed intermediate or high variability. The largest proportion of highly stable RFs derived from lbp-3D (25%) and log-sigma (12%) filtered images. Repeated measures analysis showed that only a limited subset of RFs had a statistically-significant dependence on respiratory phase, with 1747 and 1744 RFs remaining time-independent across expiratory and inspiratory phases, respectively.

Conclusion: Radiomic features extracted from 4D-CT images in early-stage NSCLC patients show heterogeneous stability across respiratory phases. Radiomic features extracted from 4D-CT images in early-stage NSCLC exhibit heterogeneous quantitative variability across respiratory phases. However, only a minority of features show statistically significant time dependence. The study provides a reproducible methodological framework to identify stable radiomic features from 4D-CT, enabling their more reliable use in lung cancer radiomic studies.

Thyroid cancer is the seventh most prevalent cancer worldwide, with a growing incidence and mortality rate. This malignancy encompasses distinct subtypes with diverse biological behaviors, clinical outcomes, and therapeutic vulnerabilities. While most thyroid cancer cases are associated with a favorable prognosis and respond well to conventional therapies, such as surgery and radioactive iodine therapy, advanced, recurrent, or dedifferentiated subtypes have a bad prognosis and limited therapeutic options, necessitating the development of more efficacious therapeutic strategies. Despite the remarkable progress of immunotherapeutic strategies across multiple malignancies, the translation of these approaches into thyroid cancer has been comparatively slow and inconsistent. Except for immune checkpoint inhibitor (ICI) therapy, other types of immunotherapy in thyroid cancer have not yet been advanced beyond early-stage clinical trials. This is attributed to the intrinsic nature of thyroid tumors, which are widely considered cold tumors with low mutational burden and a high immunosuppressive tumor microenvironment (TME). Nonetheless, recent studies indicate that by understanding the immunobiology of different subtypes of thyroid cancers, immunotherapeutic strategies can be adapted to their unique molecular, cellular, and microenvironmental characteristics, thereby improving therapeutic efficacy. This review aims to discuss the progress and pitfalls of various immunotherapy approaches for thyroid cancer, including ICI therapies, adoptive cell therapies (CAR-T cell, TCR-T cell, and TIL therapy), oncolytic virotherapy, and macrophage/myeloid-modulating immunotherapies. A deeper understanding of subtype-specific immunobiology and precise tailoring of immunotherapeutic interventions may ultimately enable more effective and durable clinical responses for patients with advanced thyroid cancer.

Background: Lung cancer, especially small-cell lung cancer (SCLC), is a widespread and deadly disease often detected at advanced stages, resulting in low five-year survival rates. This study aims to identify new genetic targets to enhance understanding of the genetic drivers of SCLC progression.

Methods: Data from 215 samples (82 normal, 133 tumor) across four datasets were retrieved from the GEO database. Using R software, we normalized and analyzed the data to assess correlations between differentially expressed genes (DEGs) and SCLC. Techniques included differential expression, expression quantitative trait loci (eQTL), and Mendelian randomization (MR) analyses. Functional and pathway analyses utilized Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Machine learning was applied to develop predictive models for disease diagnosis and progression.

Results: Analysis of 129 samples revealed 369 upregulated and 529 downregulated genes. Six genes with shared regions were significantly linked to SCLC. GO and KEGG analyses highlighted their roles in vital processes like organic hydroxy compound biosynthesis. CIBERSORT analysis emphasized immune cell variations in SCLC patients. Machine learning identified key genes, with survival analysis showing significant differences for COLEC12 and MUC1, validated by GSEA and qPCR.

Conclusion: COLEC12 and MUC1 are novel diagnostic markers and therapeutic targets for SCLC, offering potential for targeted treatments and future research.

Chimeric antigen receptor (CAR) T-cell therapy has revolutionized hematologic oncology but remains largely ineffective against solid tumors, which evade immune attack through antigen heterogeneity, a suppressive tumor microenvironment (TME), and physical barriers. This review critically examines next-generation engineering strategies designed to overcome these formidable obstacles. We focus on the development of multi-specific and logic-gated CARs to prevent antigen escape and enhance precision, alongside "armored" constructs that secrete immunomodulatory payloads (e.g., cytokines, enzymes) or express dominant-negative receptors to reprogram the immunosuppressive stroma. Furthermore, we explore cooperative strategies that directly target and remodel the TME, including cancer-associated fibroblasts, the fibrotic extracellular matrix, abnormal vasculature, and suppressive myeloid cells. Early clinical signals are encouraging, but translation to solid tumors remains constrained by safety and manufacturing challenges and by limited predictive biomarkers. Here we synthesize advances in multispecific/logic-gated receptors, armored payloads, and TME-reprogramming strategies, highlighting translational priorities and pragmatic design principles for safer, manufacturable clinical candidates. Here, we argue that the most realistic path to meaningful clinical impact in solid tumors is a staged, biomarker-driven deployment of integrated platforms that (1) prioritize antigen breadth and safety in early clinical testing, (2) pair focused stromal remodeling with localized payload delivery, and (3) reserve the most complex synthetic circuits for settings where validated predictive biomarkers support risk-benefit tradeoffs.

Background: The prognostic relevance of ultra-rare TP53 and KRAS variants in advanced solid tumours treated with immune-checkpoint inhibitors (ICI) is unclear.

Methods: We performed a retrospective cohort study using the Memorial Sloan Kettering Cancer Center MSK-IMPACT clinical-genomic dataset (cBioPortal study ID: tmb_mskcc_2018). TP53 and KRAS alterations were stratified by rarity (ultra-rare vs common) and analysed for overall survival (OS). Kaplan-Meier analyses and multivariable Cox proportional hazards models were fitted, adjusting for tumour type, tumour mutational burden (TMB), tumour purity, histology, age and sex.

Results: In the overall cohort, 51.4% of tumours harboured TP53 or KRAS mutations. Ultra-rare variants were independently associated with worse OS (HR 1.34, 95% CI 1.14-1.56; p < 0.001), with a median OS of 14.0 months versus 22.0 months in common/wild-type patients. Tumour-specific analyses suggested heterogeneity: non-small-cell lung cancer showed shorter OS for ultra-rare variants (approximately 10-13 months vs 17 months for wild type), whereas melanoma showed improved outcomes (HR 0.59, 95% CI 0.44-0.78; p < 0.001).

Conclusion: Ultra-rare TP53 and KRAS variants provide prognostic information in ICI-treated advanced solid tumours, but the direction and magnitude of effect vary by tumour type. These findings support tumour-specific interpretation and motivate mechanistic and therapeutic studies in this underserved molecular subgroup.