Tumor Biology最新文献

BackgroundTumour biomarkers have become increasingly important in oncology, shaping cancer diagnostics, classification, and patient management. Despite their potential, the use of cancer biomarkers in clinical settings remains limited.ObjectiveThis paper aims to outline biomarker development, from classical, serum protein markers to emerging tumour biomarkers, including meta-biomarkers, to show their diversity and point out the challenges in their development, reporting, and implementation in clinical practice as well as their relevance in evidence-based pathology and cancer classification.MethodsA literature-based analysis, incorporating insights from our ongoing research, is presented.ResultsAlthough numerous potential biomarkers, biomarker signatures, and meta-biomarkers, are being discovered, existing innovations are often not supported by sufficiently rigorous research methodologies and standardised reporting practices to enable their translation into clinical practice.ConclusionsTo ensure that biomarker discoveries are both scientifically sound and clinically useful, improved research and validation methods, along with adherence to established reporting standards, are essential. We propose the use of the Hierarchy of Evidence for Tumour Pathology as a framework to evaluate and map existing evidence and identify knowledge gaps and research priorities.

BackgroundImmune checkpoint inhibitors (ICIs) provide a significant survival benefit in non-small cell lung cancer (NSCLC) patients; however, accurately predicting which patients will benefit remains a challenge. As previously shown, the STOP model, a machine learning model based on serum tumor markers, is capable of identifying non-responders after 6 weeks of ICIs.ObjectiveThis study aims to externally validate this model and to assess the predictive value in combination with radiological response assessment using RECIST criteria.MethodsIn a cohort of 242 metastatic NSCLC patients, CYFRA, CEA, and NSE were measured before start and after 6 weeks of ICI treatment. The ability of the STOP model to predict no durable benefit (NDB; progressive disease, death within 6 months or disease control of less than 6 months) was assessed using specificity and positive predictive value (PPV). Moreover, a combination of the STOP model with RECIST after 6-8 weeks of ICIs was investigated.ResultsThe STOP model achieved a specificity of 96% (95% CI 95%-97%) and a PPV of predicting NDB of 88.1% (95% CI 85.9%-90.3%). Combining the STOP model with RECIST improved specificity and PPV to 100% and predicted NDB on average 11.6 weeks (IQR 1.8-18.0 weeks) prior to developing radiologically defined progression.ConclusionsAfter 6 weeks of ICIs, the blood-based STOP model was capable of accurately predicting NDB in metastatic NSCLC patients, earlier than conventional radiological assessment. The combined serological and radiological response assessment creates an early opportunity to safely stop ICI treatment in patients who will not benefit, although the clinical utility of the assay is limited since the high specificity comes at the cost of a lower sensitivity.

BackgroundIn recent years, the significance of sirtuins in cancer biology has become increasingly evident, but their molecular mechanisms and prognostic impacts remain elusive.ObjectiveThe present study aimed to investigate the differential expression of the sirtuin gene family across cancers and to evaluate their prognostic value.MethodsWe used various bioinformatics databases and methodologies, including Oncomine, GEPIA, OncoDB, cBioPortal, R2 Kaplan-Meier Scanner, STRING, etc., to determine the expression pattern of the sirtuin family genes, along with their mutations and prognostic values in human cancers.ResultsIn the current study, SIRT1, SIRT2, SIRT4, and SIRT5 were downregulated in lymphoma, whereas SIRT6 and SIRT7 were overexpressed. In breast cancer, SIRT3, SIRT5, and SIRT7 were overexpressed, and in terms of kidney cancer, higher expression of SIRT2, SIRT3, and SIRT5 was observed. In contrast, for leukemia, bladder, and brain cancers, most sirtuin family members showed reduced expression. We found that most mutations occurred in uterine cancer, chRCC (chromophobe renal cell carcinoma), DLBCL (diffuse large B-cell lymphoma), melanoma, pRCC (papillary renal cell carcinoma), and esophageal cancer. Moreover, we identified the relevant functional proteins through protein-protein interaction analysis to evaluate copy number alterations (CNAs) in sirtuins. The most frequent alterations were amplifications and deep deletions. Survival analysis demonstrated that SIRT1 and SIRT2 overexpression correlated with improved overall survival in low-grade glioma but predicted poorer outcomes in ovarian cancer. Downregulation of SIRT1, SIRT3, and SIRT5 was associated with better prognosis in DLBCL, while SIRT3 and SIRT4 upregulation predicted favorable survival in testicular germ cell tumors. SIRT6 overexpression was linked to favorable prognosis in esophageal carcinoma and sarcoma, while unfavorable outcomes were observed in hepatocellular carcinoma and cholangiocarcinoma. SIRT7 upregulation was significantly associated with reduced survival in esophageal, liver, and uterine cancers, but surprisingly correlated with improved outcomes in urothelial carcinoma and cervical squamous cell carcinoma.ConclusionsTogether, this multi-omics analysis reveals the correlation and prognostic values of sirtuins across multiple types of human cancers and suggests that sirtuins may serve as promising biomarkers for different cancers.

BackgroundOsteopontin is a glycophosphoprotein aberrantly expressed in several tumor types, which exhibits several isoforms generated by post-translational and post-transcriptional mechanisms, including alternative splicing. Among total osteopontin (tOPN), the osteopontin-c (OPN-c) splice variant has been the most explored with an oncogenic role described for a range of tumor types. Especially in ovarian cancer (OC) cells, OPN-c is found overexpressed, presenting both diagnostic and prognostic implications.ObjectiveIn this review article, we aim to outline OPN-c roles in cancer, particularly in OC, in which it has been reported as a diagnostic biomarker.MethodsWe used PubMed search, and experimental procedures were summarized at the Figure legends.ResultsWe identified cytoplasmic, perinuclear, and nuclear OPN-c in OC cells that overexpress this OPN splice variant. Moreover, we report that OPN-c splicing isoform is found highly expressed in endometrioid OC patients' samples, compared to non-neoplastic ovarian tissues. Also, OPN-c expression levels have been associated with worse overall survival and worse progression-free survival in patients with both endometrioid and serous OC. Furthermore, OPN-c may be involved in a wide range of tumor features evoked by signaling pathways, such as AKT, ERK, and FAK.ConclusionsTherefore, a better comprehension of OPN-c roles in OC can further contribute to its application as a biomarker as well as a target for putative treatment strategies, especially those aiming to sensitize tumor cells to chemotherapeutic agents currently used in the OC treatment.

BackgroundMigrating strategies of the triple-negative breast cancer (TNBC) together with its role in the establishment of tumor microenvironment (TME), supporting metastasis, have been extensively studied. Extracellular matrix (ECM) is a major player for the TME, establishing the 3D spatial networks with interconnected pores necessary for the mechano-physiological function of the cells. Certain collagen aligners and cross-linkers which are necessary for the formation and the stabilization of ECM networks, however, have not been studied either in normal or in abnormal tissues. Complexities in cell-cell and cell-matrix interactions, and different in types and ratios of ECM proteins in a TME challenge to reveal the precise function of a particular protein that is exhibited by special cells and if specifically present in insignificant amount. Cancer-associated fibroblasts (CAFs) predominantly occupy the major stroma of a solid tumor where they deposit extracellular proteins in the excessive amount compared to other tumor-associated cells. For example, the TNBC tumor itself is positive for asporin (ASPN) since CAFs are major ASPN exhibitors. However, the TNBC cells express it insignificantly.ObjectiveThe increase in ECM and its networks suppresses the metastasis.MethodsHere, we studied the expression of collagen type I and ASPN in CAFS and MDA-MB-231 (MM231), and evaluated the role of ASPN in collagen alignment and crosslinking.ResultsTNBC cells have an insignificant expression of ASPN and scanty collagen fibers, some of which aggregate to form the stiff deranged fibers, forming large-size pores in ECM of cancer-cell-dominant outer core of TNBC that support cancer cell invasion and metastasis. Exogenous ASPN and fibroblast-ASPN supported for the collagen alignment and crosslinking that established the small-size pores in the ECM, inhibiting the cancer cell invasion.ConclusionsThe collagen aligner and the cross-linker, ASPN increases the ECM networks and decreases the migration, and this preliminary study provides the hope that ASPN might be used as an anti-metastatic drug after its confirmation through extensive studies in animal, and positive outcomes through preclinical trials.

Background: Lung cancer is a major burden to global health and is still among the most frequent and most lethal malignant diseases. Macrophage migration inhibitory factor (MIF) is a proinflammatory cytokine involved in a variety of processes including tumorigenesis, formation of a tumor microenvironment and metastasis. It is therefore a potential prognostic biomarker in malignant diseases.

Objective: In this study, we investigated the applicability of MIF in serum samples as a biomarker in lung cancer.

Methods: In a retrospective approach, we analyzed the sera of 79 patients with non-small-cell lung cancer (NSCLC) and 14 patients with small-cell lung cancer (SCLC) before the start of chemotherapy, as well as before the second and third chemotherapy cycle, respectively. Serum MIF levels were measured using a sandwich immunoassay with a sulfo-tag-labelled detection antibody, while pro-gastrin releasing peptide (proGRP) levels were determined with an enzyme-linked immunosorbent assay.

Results: No difference in serum MIF levels between responders and non-responders to chemotherapy was observed at all time points, while proGRP levels were significantly lower in responders before the second chemotherapy cycle (p = 0.012). No differences in biomarker levels depending on the histopathological classification of NSCLC patients was found. Moreover, in ROC curve analyses MIF was not able to distinguish between responders and non-responders to therapy. proGRP could differentiate between responders and non-responders before the second chemotherapy cycle (p = 0.015) with sensitivities of 43% at 90% and 95% specificity, respectively. Likewise, proGRP yielded significantly longer survival times of patients with low proGRP concentrations before the second chemotherapy cycle (p = 0.015) in Kaplan-Meier analyses, yet MIF showed no significant differences in survival times at all time points. Comparison with the biomarkers CEA and CYFRA 21-1 in the same cohort showed that these established biomarkers clearly performed superior to MIF and proGRP.

Conclusions: From the present results, there is no indication that serum MIF may serve as a biomarker in prognosis and monitoring of response to therapy in lung cancer. Limitations of this study include its retrospective design, the inclusion of a larger NSCLC and a smaller SCLC subgroup, the classical chemotherapeutic treatment, the use of a non-diagnostic immunoassay (RUO-test) for MIF measurement and the lack of a validation cohort. Strengths of the study are its highly standardized procedures concerning sample collection, preanalytic treatment, measurements and quality control of the laboratory assays.

Background: Serum tumor markers (STM) may complement imaging and provide additional clinical information for patients with non-small cell lung cancer (NSCLC).

Objective: To determine whether STMs can predict outcomes in patients with stable disease (SD) after initial treatment.

Methods: This single-center, prospective, observational trial enrolled 395 patients with stage III/IV treatment-naïve NSCLC; of which 263 patients were included in this analysis. Computed Tomography (CT) scans were performed and STMs measured before and after initial treatment (two cycles of chemotherapy and/or an immune checkpoint inhibitor or tyrosine kinase inhibitor); analyses were based on CT and STM measurements obtained at first CT performed after cycle 2 only PFS and OS were analyzed by Kaplan-Meier curves and Cox-proportional hazard models.

Results: When patients with SD (n = 100) were split into high- and low-risk groups based on CYFRA 21-1, CEA and CA 125 measurements using an optimized cut-off, a 4-fold increase risk of progression or death was estimated for high- vs low-risk SD patients (PFS, HR 4.17; OS, 3.99; both p < 0.0001). Outcomes were similar between patients with high-risk SD or progressive disease (n = 35) (OS, HR 1.17) and between patients with low-risk SD or partial response (n = 128) (PFS, HR 0.98; OS, 1.14).

Conclusions: STMs can provide further guidance in patients with indeterminate CT responses by separating them into high- and low-risk groups for future PFS and OS events.

Blood-based diagnostics for lung cancer support the diagnosis, estimation of prognosis, prediction, and monitoring of therapy response in lung cancer patients. The clinical utility of serum tumor markers has considerably increased due to developments in serum protein tumor markers analytics and clinical biomarker studies, the exploration of preanalytical and influencing conditions, the interpretation of biomarker combinations and individual biomarker kinetics, as well as the implementation of biostatistical models. In addition, circulating tumor DNA (ctDNA) and other liquid biopsy markers are playing an increasingly prominent role in the molecular tumor characterization and the monitoring of tumor evolution over time. Thus, modern lung cancer biomarkers may considerably contribute to an individualized companion diagnostics and provide a sensitive guidance for patients throughout the course of their disease. In this special edition on Tumor Markers in Lung Cancer, experts summarize recent developments in clinical laboratory diagnostics of lung cancer and give an outlook on future challenges and opportunities.