Translational Oncology最新文献

Liver cancer, encompassing hepatocellular carcinoma (HCC) and hepatoblastoma, the latter of which primarily occurs in early childhood, is the most common malignant tumor arising from liver and is responsible for a significant number of cancer-related deaths worldwide. Targeted drugs have been used for anti-liver cancer treatment in the advanced stage, while their efficacy is greatly compromised by development of drug resistance. Drug resistance is a complicated process regulated by intrinsic and extrinsic signals and has been associated with poorer prognosis in cancer patients. In the current study, online available dataset analysis uncovered that angiopoietin-like protein 3 (ANGPTL3) manifested lower expression in sorafenib-resistant liver cancer cell lines. Additionally, ANGPTL3 was downregulated in HCC tissues, with its expression positively correlated with good prognosis. Functionally, ectopic expression of ANGPTL3 re-sensitized sorafenib-resistant cells, enhancing the sorafenib-induced reduction in cell viability and migration by suppressing zinc finger protein SNAI1 (SNAI1) expression and the protein stability of carnitine O-palmitoyltransferase 1, liver isoform (CPT1A). Clinical correlation analysis revealed that ANGPTL3 was negatively associated with SNAI1 expression. In conclusion, we identify a novel association between ANGPTL3, SNAI1 and CPT1A on sorafenib therapeutic response. Targeting ANGPTL3/SNAI1/CPT1A axis may serve as a therapeutic approach to improve prognosis of liver cancer patients with sorafenib resistance.

Immune-checkpoint inhibitors (ICIs) have revolutionized melanoma treatment, yet approximately half of patients do not respond to these therapies. Identifying prognostic biomarkers is crucial for treatment decisions. Our retrospective study assessed liquid biopsies and tumor tissue analyses for two potential biomarkers: danger-associated molecular pattern (DAMP) S100A8/A9 and its source, neutrophils. In 43 metastatic unresected stage III/IV melanoma patients, elevated serum levels of S100A8/A9 and neutrophils before and during ICI treatment correlated with worse outcomes. Furthermore, in 113 melanoma patients, neutrophil expression in the tumor microenvironment (TME) was associated with relapse and reduced survival. Measuring S100A8/A9 and neutrophils could enhance immunotherapy monitoring by predicting impaired clinical outcomes and non-response to ICIs. Serum S100A8/A9 levels and neutrophil counts at baseline (T0) and during treatment (T3) correlated with reduced progression-free survival (PFS). Elevated S100A8/A9 levels at T0 and T3 negatively impacted overall survival (OS). Notably, neutrophil infiltration was more prevalent in primary melanomas than in nevi and metastases, and its presence in primary melanomas was linked to poorer survival. S100A8/A9 serum levels, neutrophil counts, and tumor-associated neutrophil infiltration represent promising biomarkers for predicting treatment response and clinical outcomes in melanoma patients receiving ICIs. SIGNIFICANCE: These findings underscore the critical need for reliable biomarkers in melanoma research, particularly for predicting responses to immune-checkpoint inhibitors (ICIs). Identifying S100A8/A9 levels and neutrophil infiltration as potential indicators of treatment outcomes offers valuable insights for personalized therapy decisions. By enhancing monitoring and prognosis assessment, these biomarkers contribute to refining treatment strategies, ultimately improving patient care and outcomes. This research bridges gaps in understanding melanoma response mechanisms and highlights avenues for further investigation into immune-related markers, fostering advancements in precision medicine for melanoma patients.

Bispecific antibodies (BsAbs) represent a promising strategy for cancer immunotherapy. Challenges in immunotherapy include inefficient early events in the immune response cycle, such as antigen presentation and T cell priming. Background stimulation of CD40 with agonistic antibodies is a promising strategy to enhance the therapeutic efficacy of immune checkpoint inhibitors (ICIs). Assisted by Alphafold2(AlphaFold-Multimer), we developed a humanized CD40 agonistic antibody that exhibits activation only in the presence of cross-linking. It also demonstrates that the current AlphaFold2(AlphaFold2-Multimer) can predict antibody-antigen complexes. Due to the unique epitope, it demonstrates superior activation compared to APX005M (S267E). Building upon this, we created a novel bispecific antibody (anti-PD-L1/CD40 bispecific antibody, referred to as "BA4415") designed to activate CD40 signaling specifically in the context of PD-L1 while simultaneously blocking PD-1/PD-L1 signaling. Results from functional evaluations using effector cells revealed the superior biological activity of BA4415 compared to the combination of each monoclonal antibody. BA4415 demonstrated the ability to enhance T-cell cytokine release in vitro assays, exhibiting superior functional attributes compared to the anti-PD-L1 antibody. Furthermore, in humanized transgenic mice challenged with huPD-L1-expressing tumor cells, BA4415 induced superior anti-tumor activity. This novel anti-PD-L1/CD40 bispecific antibody holds potential for strong anti-tumor therapeutic efficacy by selectively restricting CD40 stimulation in tumors.

Acute Myeloid Leukemia (AML) is a complex hematological malignancy distinguished by its heterogeneity in genetic aberrations, cellular composition, and clinical outcomes. This diversity complicates the development of effective, universally applicable therapeutic strategies and highlights the necessity for personalized approaches to treatment. In our study, we utilized high-resolution single-cell RNA sequencing from publicly available datasets to dissect the complex cellular landscape of AML. This approach uncovered a diverse array of cellular subpopulations within the bone marrow samples of AML patients. Through meticulous analysis, we identified 156 differentially expressed cytokine-related genes that underscore the nuanced interplay between AML cells and their microenvironment. Leveraging this comprehensive dataset, we constructed a prognostic risk score model based on seven pivotal cytokine-related genes: CCL23, IL2RA, IL3RA, IL6R, INHBA, TNFSF15, and TNFSF18. The mRNA levels of 7 genes in the risk score model have significant different. This model was rigorously validated across several independent AML patient cohorts, showcasing its robust prognostic capability to stratify patients into distinct risk categories. Patients classified under the high-risk category exhibited significantly poorer survival outcomes compared to their low-risk counterparts, underscoring the model's clinical relevance. Additionally, our in-depth investigation into the immune landscape revealed marked differences in immune cell infiltration and cytokine signaling between the identified risk groups, shedding light on potential immune-mediated mechanisms driving disease progression and treatment resistance. This comprehensive analysis not only advances our understanding of the cellular and molecular underpinnings of AML but also introduces a novel, clinically applicable risk score model. This tool holds significant promise for enhancing the precision of prognostic assessments in AML, thereby paving the way for more tailored and effective therapeutic interventions. Our findings represent a pivotal step toward the realization of personalized medicine in the management of AML, offering new avenues for research and treatment optimization in this challenging disease landscape.

Undifferentiated pleomorphic sarcoma (UPS) is the most frequent and the most aggressive sarcoma subtype for which therapeutic options are limited. The identification of new therapeutic strategies is therefore an important medical need. Epigenetic modifiers has been extensively investigated in recent years leading to the development of novel therapeutic agents. Dual BET/EP300 inhibitors have shown synergistic antitumor activity and have recently entered clinical development. To date, no data related to potential of BET/EP300 inhibition as a treatment in UPS have been reported. To investigate the therapeutic potential of BET/EP300 inhibition, we evaluated the antitumor activity of three compounds in vitro via MTT, apoptosis and cell cycle assays. The most potent inhibitor was evaluated in vivo in two animal models and the mechanisms of action were investigated by RNA sequencing, Western blotting and immunofluorescence staining. A CRISPR knockout screen was performed to identify resistance mechanisms. Among the three compounds tested, the dual inhibitor NEO2734 was the most potent, decreased the viability of UPS cells in vitro through a regulation of E2F targets and cell cycle and decreased the tumor growth in vivo. Moreover, we identified GPX4 as a gene involved in resistance and showed synergy between BET inhibition and ferroptosis induction. The present study demonstrated that dual BET/EP300 inhibitors have a relevant antitumor activity in a subgroup of UPS characterized by expression of MYC-targets pathway and identified a potent combination therapeutic strategy that deserves further investigation in the clinical setting.

Importance: Gastric cancer is often diagnosed at an advanced stage and at order age, identification of high-risk population is needed for detection of early-stage gastric cancer.

Objective: To examine whether clonal hematopoiesis of indeterminate potential (CHIP) is a risk factor of gastric cancer.

Design: This cohort study used data from the UK Biobank collected from baseline (2006-2010) to the end of follow-up in March 2024.

Setting: Data on age, sex, race, alcohol consumption, smoking status and type 2 diabetes were collected at baseline interview. Previous and diagnosed cancer or diseases were collected from self-reported and in-hospital records.

Participants: Participants with no previous cancer or hematologic disorders were selected. Participants with gastric cancer cases were aged 60.7 (S.D. 6.62), 71.8 % male; controls were aged 56.1 (S.D. 8.11), 47.4 % male.

Exposures: Whole-exome sequencing was performed on blood samples collected at baseline. A CHIP status was identified based on the mutations on 43 CHIP-related genes.

Main outcomes and measures: Odds ratio (OR) of CHIP with gastric cancer risk was estimated using multivariable logistic regression models. Participants were grouped based on age and CHIP status to examine if there are differences in the cumulative incidence of gastric cancer.

Results: Among 402,253 participants, 1,070 incident gastric cancer cases were identified (mean age, 60.7 ± 6.62 years). The prevalence of CHIP at baseline was associated with an increased risk of gastric cancer (cases: 6.54 % vs. controls 5.14 %; OR without adjustment, 1.29; 95 % CI, 1.004 to 1.63). The stratified OR (95 % CI) of individuals aged ≥ 57 was 1.33 (1.02 to 1.72) for overall CHIP, whereas the OR for younger individuals was 0.79 (0.37 to 1.44). CHIP involving DNMT3A (OR, 1.81; 95 % CI, 1.05 to 2.88; P = 0.0193) and ASXL1 (OR, 2.43; 95 % CI, 0.95 to 4.99; P = 0.032) was associated with an increased risk of gastric cancer. These positive associations remained significantly in sensitivity analyses adjusted by known risk factors. Compared to younger individuals and non-CHIP carriers, older participants with CHIP exhibited a significantly higher cumulative incidence of gastric cancer (P < 0.0001).

Conclusions and relevance: CHIP is associated with gastric cancer in the elderly and contributes to the positive association between DNM3A and ASXL1 mutations and risk of gastric cancer.

Background: Inflammatory breast cancer (IBC) is an aggressive and rare phenotype of breast cancer, which has a poor prognosis. Thus, it is necessary to establish a novel predictive model of high accuracy for the prognosis of IBC patients.

Methods: Clinical information of 1,230 IBC patients from 2010 to 2020 was extracted from the Surveillance, Epidemiology and End Results (SEER) database. Cox analysis was applied to identify clinicopathological characteristics associated with the overall survival (OS) of IBC patients. Random survival forest (RSF) algorithm was adopted to construct an accurate prognostic prediction model for IBC patients. Kaplan-Meier analysis was performed for survival analyses.

Results: Race, N stage, M stage, molecular subtype, history of chemotherapy and surgery, and response to neoadjuvant therapy were identified as independent predictive factors for the OS of IBC patients. The top five significant variables included surgery, response to neoadjuvant therapy, chemotherapy, breast cancer molecular subtypes, and M stage. The C-index of RSF model was 0.7704 and the area under curve (AUC) values for 1, 3, 5 years in training and validation datasets were 0.879-0.955, suggesting the excellent predictive performance of RSF model. IBC patients were divided into high-risk group and low-risk group according the risk score of RSF model, and the OS of patients in the low-risk group was significantly longer than those in the high-risk group.

Conclusion: In this study, we constructed a prognosis prediction model for IBC patients through RSF algorithm, which may potentially serve as a useful tool during clinical decision-making.

The lysine oxidase (LOX) family, consisting of LOX and LOX-like-1-4 (LOXL1-LOXL4), catalyses the cross-linking reaction of collagen and elastin in the extracellular matrix (ECM). Numerous studies have demonstrated that LOX family members are dysregulated in a variety of cancers, including colorectal cancer (CRC), and play a key role in cancer cell migration, proliferation, invasion and metastasis. Targeting LOX family proteins with specific inhibitors has therefore been developed as a new therapeutic strategy for cancer. In this paper, we review the role of LOX enzymes in the development and progression of CRC. In addition, we address recent advances in the development of LOX/LOXL inhibitors, highlighting the potential use of this inhibitor as an effective and complementary treatment for CRC.

Background: Despite advancements with intensity-modulated radiation therapy (IMRT), about 10 % of nasopharyngeal carcinoma (NPC) patients remain resistant to radiotherapy, leading to recurrence and poor prognosis. This study aims to identify radiosensitivity-related genes in NPC and develop a prognostic model to predict patient outcomes.

Methods: We analyzed 179 NPC samples from Fujian Cancer Hospital using RNA sequencing. Differentially expressed genes (DEGs) were identified between radiotherapy-sensitive and resistant samples. Machine learning algorithms and Cox regression were used to construct a prognostic risk model, validated in the GSE102349 dataset. Additional analyses included functional pathway, immune infiltration, and drug sensitivity.

Results: A risk model based on six genes (LCN8, IGSF1, RIMS2, RBP4, TBX10, ETV4) was developed. Kaplan-Meier analysis showed significantly shorter progression-free survival (PFS) in the high-risk group. The model's AUC values were 0.872, 0.807, and 0.802 for 1-year, 3-year, and 5-year predictions. A nomogram including clinical factors was created, and enrichment analysis linked the high-risk group to radiotherapy resistance mechanisms.

Conclusions: This study established a novel radiosensitivity-related prognostic model, offering insights into NPC prognosis and radiotherapy resistance mechanisms.

Background: Taxane-based chemotherapy is the primary treatment for triple-negative breast cancer (TNBC), yet clinical outcomes remain unsatisfactory due to the persistence of chemoresistance. Identifying key factors that contribute to chemoresistance and understanding the associated molecular mechanisms is therefore essential.

Method: The GEO databases were utilized to pinpoint factors related to chemoresistance, which were subsequently validated using clinical tissue samples. The role of UGCG in the malignant progression and chemoresistance of TNBC was assessed through various functional assays. Western blotting, qRT-PCR, and immunohistochemistry were employed to investigate the signaling pathways associated with UGCG in TNBC.

Results: UGCG expression was notably elevated in chemoresistant breast cancer tissues and cells, as identified in GEO databases and confirmed through immunohistochemistry. Additionally, findings from our cohorts indicated that higher levels of UGCG expression correlated with a lower rate of pathological complete response (pCR), suggesting it could serve as an independent predictor of chemotherapy effectiveness. Gain- and loss-of-function experiments demonstrated that UGCG enhanced the proliferation, metastasis, and stemness of breast cancer cells. Furthermore, treatment with paclitaxel or docetaxel resulted in increased UGCG expression, which in turn reduced chemotherapy-induced cell apoptosis and improved drug resistance and metastatic capabilities. Mechanistically, UGCG was found to amplify the activation of NF-κB and Wnt/β-catenin pathways, and the use of inhibitors targeting these pathways diminished the UGCG-induced malignant effects.

Conclusion: Our findings underscore the significant role of UGCG in the chemoresistance and progression of breast cancer, suggesting it as a predictive biomarker and potential therapeutic target to combat chemoresistance in this disease.