Cancer Biomarkers最新文献

BackgroundBreast cancer is the leading malignancy among women and the lack of ideal early biomarkers hampers diagnosis and treatment monitoring. Genomic instability, central to breast cancer development, makes DNA damage a potential biomarker for these purposes.ObjectiveThis study aims to evaluate the predictive value of DNA damage for diagnosis, and treatment monitoring in breast cancer, with CA 15-3, a conventional cancer biomarker, included for comparison to assess the added value of DNA damage measurement.MethodsDNA damage was measured in peripheral blood lymphocytes of 58 breast cancer patients, and 31 healthy controls, employing comet assay, both before and after treatment. Serum CA 15-3 levels were assessed at the same time points for comparison.ResultsDNA damage levels were significantly higher in cancer patients compared to healthy controls, with the most elevated levels observed in patients with advanced-stage disease, irrespective of age, sex, lifestyle, or genetic status. Post-treatment assessments showed a significant rise in DNA damage. In comparison, CA 15-3 showed less consistent relevance for diagnostic and monitoring.ConclusionsThis study underscores the greater potential of DNA damage as a consistent and reliable biomarker for breast cancer, with CA 15-3 providing complementary but less consistent data for clinical decision-making.

BackgroundOropharyngeal cancer rates continue to rise with no effective screening method. Persistent oral oncogenic human papillomavirus (HPV), antibodies to HPV16 early (E) oncoproteins, and circulating tumor HPV DNA (ctHPVDNA) are biomarkers that show promise for use in HPV-related cancer screening.ObjectiveTo assess the prevalence of biomarkers for HPV-related cancer and their agreement in middle-aged men.MethodsMen aged 50-64 years from the general population provided oral rinse and blood samples as well as information about demographics, tobacco/alcohol exposure, sexual behavior, and HPV-related disease history. Oral rinse was tested for HPV16 DNA and plasma was tested for HPV16 E antibodies and ctHPVDNA using a droplet digital PCR (ddPCR)-based assay that measures circulating tumor tissue modified viral (TTMV)-HPV DNA (NavDx, Naveris, Inc.). We calculated frequency distributions of variables of interest and agreement between the biomarkers.ResultsWe enrolled 1045 subjects between April 2017 and April 2024. The 954 subjects with results for all three biomarkers were included in the analysis. The prevalence was 4.9% for oral HPV16 DNA, 0.7% for HPV16 E antibodies, and 0.5% for TTMV-HPV DNA.ConclusionsThe low prevalence of all three biomarkers shows their potential to identify high-risk individuals eligible for further clinical HPV-related cancer screening.

ObjectiveStudy aims to develop diagnostic and prognostic models for lung adenocarcinoma (LUAD) using Machine learning（ML）algorithms, aiming to enhance clinical decision-making accuracy.MethodsData from The Cancer Genome Atlas (TCGA) for LUAD patients were split into training (n = 196) and test sets (n = 133). Feature selection (Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Support Vector Machine (SVM)) identified miRNAs distinguishing stage I LUAD. Six ML algorithms predicted pulmonary node classification. Model performance was evaluated using Receiver Operating Characteristic (ROC) curve, Precision-Recall (PR) curves, and Error Rates (CE). A prognostic model was constructed using Lasso Cox regression. Risk score plots were generated, and model performance was assessed using Kaplan-Meier (K-M) and time-dependent ROC curves. Functional enrichment analyses investigated miRNA function and mechanism.ResultsThe feature selection results identified five miRNA molecules as distinguishing characteristics between early-stage LUAD and adjacent non-cancerous tissues. A prognostic model using 13 miRNAs predicted poorer outcomes for patients with higher risk scores, supported by time-dependent ROC curves and a nomogram. Functional enrichment analysis identified cancer-related signaling pathways for the biomarkers.ConclusionML identified a diagnostic five-miRNA signature and a prognostic 13-miRNA model for LUAD, both robust and reliable.

Pulmonary nodules are ubiquitously found on computed tomography (CT) imaging either incidentally or via lung cancer screening and require careful diagnostic evaluation and management to both diagnose malignancy when present and avoid unnecessary biopsy of benign lesions. To engage in this complex decision-making, clinicians must first risk stratify pulmonary nodules to determine what the best course of action should be. Recent developments in imaging technology, computer processing power, and artificial intelligence algorithms have yielded radiomics-based computer-aided diagnosis tools that use CT imaging data including features invisible to the naked human eye to predict pulmonary nodule malignancy risk and are designed to be used as a supplement to routine clinical risk assessment. These tools vary widely in their algorithm construction, internal and external validation populations, intended-use populations, and commercial availability. While several clinical validation studies have been published, robust clinical utility and clinical effectiveness data are not yet currently available. However, there is reason for optimism as ongoing and future studies aim to target this knowledge gap, in the hopes of improving the diagnostic process for patients with pulmonary nodules.

BACKGROUNDLiquid biopsy (LB) is used to detect epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer (NSCLC) and has been demonstrated to have prognostic and predictive value.OBJECTIVETo associate the rates of EGFR and T790M mutations detected by LB during disease progression after first- or second-generation EGFR-TKIs with clinical characteristics and survival outcomes.METHODSFrom January 2018 to December 2021, 295 patients with advanced EGFR mutant (EGFRm) NSCLC treated with first- or second-generation EGFR-TKIs were retrospectively analyzed. LB was collected at the time of progression. The frequency of EGFR${}^{T790M}$ mutations, overall survival (OS), and the clinical characteristics associated with LB positivity were determined.RESULTSThe prevalence of EGFR${}^{T790M}$ mutation detected using LB was 44%. In patients with negative vs. positive LB, the median OS was 45.0 months vs. 25.0 months ($p=$ 0.0001), respectively. Patients with a T790M mutation receiving osimertinib had a median OS of 44 months (95% CI [33.05-54.99]). Clinical characteristics associated with positive LB at progression extra-thoracic involvement, $>$ 3 metastatic sites, and bone metastases.CONCLUSIONSOur findings showed that LB positivity was associated with worse survival outcomes and specific clinical characteristics. This study also confirmed the feasibility and detection rate of T790M mutation in a Latin American population.

BackgroundRecent technologies enabling the study of spatial biology include multiple high-dimensional spatial imaging methods that have rapidly emerged with different capabilities evaluating tissues at different resolutions for different sample formats. Platforms like Xenium (10x Genomics) and PhenoCycler-Fusion (Akoya Biosciences) enable single-cell resolution analysis of gene and protein expression in archival FFPE tissue slides. However, a key limitation is the absence of systematic methods to ensure tissue quality, marker integrity, and data reproducibility.ObjectiveWe seek to optimize the technical methods for spatial work by addressing preanalytical challenges with various tissue and tumor types, including a decalcification protocol for processing FFPE bone marrow core specimens to preserve nucleic acids for effective spatial proteomics and transcriptomics. This study characterizes a multicancer tissue microarray (TMA) and a molecular- and protein-friendly decalcification protocol that supports downstream spatial biology investigations.MethodsWe developed a multi-cancer tissue microarray (TMA) and processed bone marrow core samples using a molecular- and protein-friendly decalcification protocol. PhenoCycler high-plex immunohistochemistry (IHC) generated spatial proteomics data, analyzed with QuPath and single-cell analysis. Xenium provided spatial transcriptomics data, analyzed via Xenium Explorer and custom pipelines.ResultsResults showed that PhenoCycler and Xenium platforms applied to TMA sections of tonsil and various tumor types achieved good marker concordance. Bone marrow decalcification with our optimized protocol preserved mRNA and protein markers, allowing Xenium analysis to resolve all major cell types while maintaining tissue morphology.ConclusionsWe have shared our preanalytical verification of tissues and demonstrate that both the PhenoCycler-Fusion high-plex spatial proteomics and Xenium spatial transcriptomics platforms work well on various tumor types, including marrow core biopsies decalcified using a molecular- and protein-friendly decalcificationprotocol. We also demonstrate our laboratory's methods for systematic quality assessment of the spatial proteomic and transcriptomic data from these platforms, such that either platform can provide orthogonal confirmation for the other.

BACKGROUNDContinued improvement in deep learning methodologies has increased the rate at which deep neural networks are being evaluated for medical applications, including diagnosis of lung cancer. However, there has been limited exploration of the underlying radiological characteristics that the network relies on to identify lung cancer in computed tomography (CT) images.OBJECTIVEIn this study, we used a combination of image masking and saliency activation maps to systematically explore the contributions of both parenchymal and tumor regions in a CT image to the classification of indeterminate lung nodules.METHODSWe selected individuals from the National Lung Screening Trial (NLST) with solid pulmonary nodules 4-20 mm in diameter. Segmentation masks were used to generate three distinct datasets; 1) an Original Dataset containing the complete low-dose CT scans from the NLST, 2) a Parenchyma-Only Dataset in which the tumor regions were covered by a mask, and 3) a Tumor-Only Dataset in which only the tumor regions were included.RESULTSThe Original Dataset significantly outperformed the Parenchyma-Only Dataset and the Tumor-Only Dataset with an AUC of 80.80 $\pm$ 3.77% compared to 76.39 $\pm$ 3.16% and 78.11 $\pm$ 4.32%, respectively. Gradient-weighted class activation mapping (Grad-CAM) of the Original Dataset showed increased attention was being given to the nodule and the tumor-parenchyma boundary when nodules were classified as malignant. This pattern of attention remained unchanged in the case of the Parenchyma-Only Dataset. Nodule size and first-order statistical features of the nodules were significantly different with the average malignant and benign nodule maximum 3d diameter being 23 mm and 12 mm, respectively.CONCLUSIONWe conclude that network performance is linked to textural features of nodules such as kurtosis, entropy and intensity, as well as morphological features such as sphericity and diameter. Furthermore, textural features are more positively associated with malignancy than morphological features.

Lung cancer is the leading cause of cancer death world-wide. Along the entire timeline of lung cancer identification, diagnosis and treatment, clinicians and patients face challenges in clinical decision-making that could be aided by useful biomarkers. In this review, we discuss the development of biomarkers and qualities that are ideal in a biomarker candidate, types of biospecimens that can be utilized for biomarker development in lung cancer, and how biomarkers could be clinically useful at various points along lung cancer timeline. We then review biomarkers that have been validated and are clinically available to assist with the management of lung nodules and diagnosis of lung cancer, which includes blood-based biomarkers to assist with decision-making prior to an invasive diagnostic procedure, as well as specimens obtained during a bronchoscopy and applied in cases of an inconclusive biopsy result. Finally, we discuss challenges in biomarker application and recent publications relevant to future lung cancer biomarker development.

Background and Aim: Malignant pleural effusion (MPE) is a common clinical problem. Management options are mainly pleurodesis and drainage, and have remained unchanged for years. Novel therapies that target the molecules responsible for fluid formation are needed to reduce the need for invasive procedures. The aim of this study is to investigate the potential role of MCP-1 in the development of MPE in patients with metastatic pleural malignancies. Methods: Pleural effusion samples (8-10 ml) were collected from 100 patients who were divided into three groups: Group 1 (MPE, n = 56), Group 2 (benign exudate, n = 27) and Group 3 (transudate, n = 17). The collected effusions were promptly centrifuged at 4°C, and the supernatants were stored at -80°C. MCP-1 levels were determined by ELISA kit (USCN, Wuhan). Results: Median MCP-1 levels were found to be significantly different between the three groups (Group 1: 1303 pg/ml, Group 2: 926 pg/ml, Group 3: 211 pg/ml) (p < 0.001). MCP-1 levels were markedly higher but similar in Group 1 and Group 2, as compared to Group 3. When patients from Group 1 and Group 2 were combined, a positive correlation was observed between pleural fluid MCP-1 and LDH levels (r = 0.38; p = 0.001). Additionally, MCP-1 levels were observed to increase significantly as the volume of pleural fluid increased (p = 0.007). Conclusion: MCP-1 levels were found to be similarly high in both Group 1 (MPE) and Group 2 (Benign exudate), indicating that inflammation accompanying the tumor could play a role in the formation of pleural effusion. This suggests that the development of biological therapies targeting MCP-1 could be a promising approach in the future management of MPE.

BACKGROUNDBiomarkers predicting clinical outcomes of treating non-small cell lung cancer (NSCLC) with combination of immune checkpoint inhibitors (ICIs) and chemotherapy would be valuable.OBJECTIVEThis study aims to seek predictors of combination of ICI/chemotherapy response in NSCLC patients using peripheral blood samples.METHODSPatients diagnosed with advanced NSCLC between July 2019 and May 2021 receiving combination of ICI/chemotherapy were included and assessed for partial responses (PR), stable disease (SD) or progressive disease (PD). We measured circulating immune cells, plasma cytokines and chemokines.RESULTSNineteen patients were enrolled. The proportions of circulating natural killer (NK) cells within CD45⁺ cells, programmed death 1 (PD-1)⁺ Tim-3⁺ T cells within CD4⁺ cells, and the amount of chemokine C-X-C ligand (CXCL10) in the plasma were significantly elevated in PR relative to SD/PD patients (median 8.1%-vs-2.1%, $P=$ 0.0032; median 1.2%-vs-0.3%, $P=$ 0.0050; and median 122.6 pg/ml-vs-76.0 pg/ml, $P=$ 0.0125, respectively). Patients with 2 or 3 elevated factors had longer progression-free survival than patients with 0 or only one (not reached-vs-5.6 months, $P=$ 0.0002).CONCLUSIONSWe conclude that NK cells, CD4⁺ PD-1⁺ Tim-3⁺ T cells, and CXCL10 levels in pre-treatment peripheral blood may predict the efficacy of combination of ICI/chemotherapy in NSCLC.