Tumor xenograft modeling identifies an association between TCF4 loss and breast cancer chemoresistance

Unveiling heterogeneity and prognostic markers in ductal breast cancer through single-cell RNA-seq

BACKGROUND

Breast cancer (BC) is a heterogeneous disease, with the ductal subtype exhibiting significant cellular diversity that influences prognosis and response to treatment. Single-cell RNA sequencing data from the GEO database were utilized in this study to investigate the underlying mechanisms of cellular heterogeneity and to identify potential prognostic markers and therapeutic targets.

METHODS

Bioinformatics analysis was conducted using R packages to analyze the single-cell sequencing data. The presence of highly variable genes and differences in malignant potency within the same BC samples were examined. Differential gene expression and biological function between Type 1 and Type 2 ductal epithelial cells were identified. Lasso regression and Cox proportional hazards regression analyses were employed to identify genes associated with patient prognosis. Experimental validation was performed in vitro and in vivo to confirm the functional relevance of the identified genes.

RESULTS

The analysis revealed notable heterogeneity among BC cells, with the presence of highly variable genes and differences in malignant behavior within the same samples. Significant disparities in gene expression and biological function were identified between Type 1 and Type 2 ductal epithelial cells. Through regression analyses, CYP24A1 and TFPI2 were identified as pivotal genes associated with patient prognosis. Kaplan-Meier curves demonstrated their prognostic significance, and experimental validation confirmed their inhibitory effects on malignant behaviors of ductal BC cells.

CONCLUSION

This study highlights the cellular heterogeneity in ductal subtype breast cancer and delineates the differential gene expressions and biological functions between Type 1 and Type 2 ductal epithelial cells. The genes CYP24A1 and TFPI2 emerged as promising prognostic markers and therapeutic targets, exhibiting inhibitory effects on BC cell malignancy in vitro and in vivo. These findings offer the potential for improved BC management and the development of targeted treatment strategies.

Protein stabilization of ITF2 by NF-κB prevents colitis-associated cancer development

Chronic colonic inflammation is a feature of cancer and is strongly associated with tumorigenesis, but its underlying molecular mechanisms remain poorly understood. Inflammatory conditions increased ITF2 and p65 expression both ex vivo and in vivo, and ITF2 and p65 showed positive correlations. p65 overexpression stabilized ITF2 protein levels by interfering with the binding of Parkin to ITF2. More specifically, the C-terminus of p65 binds to the N-terminus of ITF2 and inhibits ubiquitination, thereby promoting ITF2 stabilization. Parkin acts as a E3 ubiquitin ligase for ITF2 ubiquitination. Intestinal epithelial-specific deletion of ITF2 facilitated nuclear translocation of p65 and thus increased colitis-associated cancer tumorigenesis, which was mediated by Azoxymethane/Dextran sulfate sodium or dextran sulfate sodium. Upregulated ITF2 expression was lost in carcinoma tissues of colitis-associated cancer patients, whereas p65 expression much more increased in both dysplastic and carcinoma regions. Therefore, these findings indicate a critical role for ITF2 in the repression of colitis-associated cancer progression and ITF2 would be an attractive target against inflammatory diseases including colitis-associated cancer.

RBM24 exacerbates bladder cancer progression by forming a Runx1t1/TCF4/miR-625-5p feedback loop

RNA–binding motif protein 24 (RBM24) acts as a multifunctional determinant of cell fate, proliferation, apoptosis, and differentiation during development by regulating premRNA splicing and mRNA stability. It is also implicated in carcinogenesis, but the functions of RBM24 in bladder cancer (BC) remain unclear. In the present study, we revealed that RBM24 was upregulated in BC tissues. Importantly, we found that a higher level of RBM24 was correlated with poor prognosis in BC patients. Overexpression of RBM24 promoted BC cell proliferation, while depletion of RBM24 inhibited BC cell proliferation in vivo and in vitro. Mechanistically, RBM24 positively regulated Runx1t1 expression in BC cells by binding to and enhancing Runx1t1 mRNA stability. Furthermore, Runx1t1 in turn promoted RBM24 expression by interacting with the transcription factor TCF4 and suppressing the transcription of miR-625-5p, which directly targets RBM24 and suppresses RBM24 expression. RBM24-regulated BC cell proliferation was moderated via the Runx1t1/TCF4/miR-625-5p feedback loop. These results indicate that the RBM24/Runx1t1/TCF4/miR-625-5p positive feedback loop participates in BC progression. Disruption of this pathway may be a potential therapeutic strategy for BC treatment.

A protein called RBM24 promotes progression of bladder cancer (BC) by forming a positive feedback loop with a specific transcription factor, driving cancer cell proliferation. Survival rates for BC are low, and the current imperfect understanding of the underlying mechanisms makes it difficult to treat. Ping-Ying Guo at Hebei Medical University in Shijiazhuang, China, and co-workers investigated the role of RBM24, known to be involved in other cancers, and found increased levels in BC tissues. Higher levels were associated with a poor prognosis. Further investigation revealed that RBM24 boosts levels of the transcription factor, which suppresses a molecule that in turn suppresses RBM24, forming a positive feedback loop promoting BC cell proliferation. Interrupting the feedback loop decreased tumor size in a mouse model. These results may help identify better treatments for BC.

Functional and genomic characterization of patient-derived xenograft model to study the adaptation to mTORC1 inhibitor in clear cell renal cell carcinoma

Resistance to the mechanistic target of rapamycin (mTOR) inhibitors, which are a standard treatment for advanced clear cell renal cell carcinoma (ccRCC), eventually develops in most cases. In this study, we established a patient-derived xenograft (PDX) model which acquired resistance to the mTOR inhibitor temsirolimus, and explored the underlying mechanisms of resistance acquisition. Temsirolimus was administered to PDX model mice, and one cohort of PDX models acquired resistance after repeated passages. PDX tumors were genetically analyzed by whole-exome sequencing and detected several genetic alterations specific to resistant tumors. Among them, mutations in ANKRD12 and DNMT1 were already identified in the early passage of a resistant PDX model, and we focused on a DNMT1 mutation as a potential candidate for developing the resistant phenotype. While DNMT1 expression in temsirolimus-resistant tumors was comparable with the control tumors, DNMT enzyme activity was decreased in resistant tumors compared with controls. Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9-mediated heterozygous knockdown of DNMT1 in the temsirolimus-sensitive ccRCC (786-O) cell line was shown to result in a temsirolimus-resistant phenotype in vitro and in vivo. Integrated gene profiles using methylation and microarray analyses of PDX tumors suggested a global shift for the hypomethylation status including promotor regions, and showed the upregulation of several molecules that regulate the mTOR pathway in temsirolimus-resistant tumors. Present study showed the feasibility of PDX model to explore the mechanisms of mTOR resistance acquisition and suggested that genetic alterations, including that of DNMT1, which alter the methylation status in cancer cells, are one of the potential mechanisms of developing resistance to temsirolimus.

High-throughput meta-analysis and validation of differentially expressed genes as potential biomarkers of ionizing radiation-response

BACKGROUND AND PURPOSE

The high-throughput analysis of gene expression in ionizing radiation (IR)-exposed human peripheral white blood cells (WBC) has emerged as a novel method for biodosimetry markers detection. We aimed to detect IR-exposure differential expressed genes (DEGs) as potential predictive biomarkers for biodosimetry and radioinduced-response.

MATERIALS AND METHODS

We performed a meta-analysis of raw data from public microarrays of ex vivo low linear energy transfer-irradiated human peripheral WBC. Functional enrichment and transcription factors (TF) detection from resulting DEGs were assessed. Six selected DEGs among studies were validated by qRT-PCR on mRNA from human peripheral blood samples from nine healthy human donors 24 h after ex vivo X-rays-irradiation.

RESULTS

We identified 275 DEGs after IR-exposure (parameters: |lfc| ≥ 0.7, q value <0.05), enriched in processes such as regulation after IR-exposure, DNA damage checkpoint, signal transduction by p53 and mitotic cell cycle checkpoint. Among these DEGs, DRAM1, NUDT15, PCNA, PLK2 and TIGAR were selected for qRT-PCR validation. Their expression levels significantly increased at 1-4 Gy respect to non-irradiated controls. Particularly, PCNA increased dose dependently. Curiously, TCF4 (Entrez Gene: 6925), detected as overrepresented TF in the radioinduced DEGs set, significantly decreased post-irradiation.

CONCLUSION

These six DEGs show potential to be proposed as candidates for IR-exposure biomarkers, considering their observed molecular radioinduced-response. Among them, TCF4, bioinformatically detected, was validated herein as an IR-responsive gene.

A Biobank of Colorectal Cancer Patient-Derived Xenografts

Colorectal cancer (CRC) is a challenging disease, with a high mortality rate and limited effective treatment options, particularly for late-stage disease. Patient-derived xenografts (PDXs) have emerged as an informative, renewable experimental resource to model CRC architecture and biology. Here, we describe the generation of a biobank of CRC PDXs from stage I to stage IV patients. We demonstrate that PDXs within our biobank recapitulate the histopathological and mutation features of the original patient tumor. In addition, we demonstrate the utility of this resource in pre-clinical chemotherapy and targeted treatment studies, highlighting the translational potential of PDX models in the identification of new therapies that will improve the overall survival of CRC patients.

A Novel Role for the Tumor Suppressor Gene ITF2 in Tumorigenesis and Chemotherapy Response

Despite often leading to platinum resistance, platinum-based chemotherapy continues to be the standard treatment for many epithelial tumors. In this study we analyzed and validated the cytogenetic alterations that arise after treatment in four lung and ovarian paired cisplatin-sensitive/resistant cell lines by 1-million microarray-based comparative genomic hybridization (array-CGH) and qRT-PCR methodologies. RNA-sequencing, functional transfection assays, and gene-pathway activity analysis were used to identify genes with a potential role in the development of this malignancy. The results were further explored in 55 lung and ovarian primary tumors and control samples, and in two extensive in silico databases. Long-term cell exposure to platinum induces the frequent deletion of ITF2 gene. Its expression re-sensitized tumor cells to platinum and recovered the levels of Wnt/β-catenin transcriptional activity. ITF2 expression was also frequently downregulated in epithelial tumors, predicting a worse overall survival. We also identified an inverse correlation between ITF2 and HOXD9 expression, revealing that Non-small cell lung cancer (NSCLC) patients with lower expression of HOXD9 had a better overall survival rate. We defined the implication of ITF2 as a molecular mechanism behind the development of cisplatin resistance probably through the activation of the Wnt-signaling pathway. This data highlights the possible role of ITF2 and HOXD9 as novel therapeutic targets for platinum resistant tumors.

Patient-Derived Xenograft Models of Breast Cancer and Their Application

Recently, patient-derived xenograft (PDX) models of many types of tumors including breast cancer have emerged as a powerful tool for predicting drug efficacy and for understanding tumor characteristics. PDXs are established by the direct transfer of human tumors into highly immunodeficient mice and then maintained by passaging from mouse to mouse. The ability of PDX models to maintain the original features of patient tumors and to reflect drug sensitivity has greatly improved both basic and clinical study outcomes. However, current PDX models cannot completely predict drug efficacy because they do not recapitulate the tumor microenvironment of origin, a failure which puts emphasis on the necessity for the development of the next generation PDX models. In this article, we summarize the advantages and limitations of current PDX models and discuss the future directions of this field.

SALMON: Survival Analysis Learning With Multi-Omics Neural Networks on Breast Cancer

Improved cancer prognosis is a central goal for precision health medicine. Though many models can predict differential survival from data, there is a strong need for sophisticated algorithms that can aggregate and filter relevant predictors from increasingly complex data inputs. In turn, these models should provide deeper insight into which types of data are most relevant to improve prognosis. Deep Learning-based neural networks offer a potential solution for both problems because they are highly flexible and account for data complexity in a non-linear fashion. In this study, we implement Deep Learning-based networks to determine how gene expression data predicts Cox regression survival in breast cancer. We accomplish this through an algorithm called SALMON (Survival Analysis Learning with Multi-Omics Neural Networks), which aggregates and simplifies gene expression data and cancer biomarkers to enable prognosis prediction. The results revealed improved performance when more omics data were used in model construction. Rather than use raw gene expression values as model inputs, we innovatively use eigengene modules from the result of gene co-expression network analysis. The corresponding high impact co-expression modules and other omics data are identified by feature selection technique, then examined by conducting enrichment analysis and exploiting biological functions, escalated the interpretation of input feature from gene level to co-expression modules level. Our study shows the feasibility of discovering breast cancer related co-expression modules, sketch a blueprint of future endeavors on Deep Learning-based survival analysis. SALMON source code is available at https://github.com/huangzhii/SALMON/.