From chromosomal abnormalities to the identification of target genes in mouse models of breast cancer

Breast cancer prediction using different machine learning methods applying multi factors

OBJECTIVE

Breast cancer (BC) is a multifactorial disease and is one of the most common cancers globally. This study aimed to compare different machine learning (ML) techniques to develop a comprehensive breast cancer risk prediction model based on features of various factors.

METHODS

The population sample contained 810 records (115 cancer patients and 695 healthy individuals). 45 attributes out of 85 were selected based on the opinion of experts. These selected attributes are in genetic, biochemical, biomarker, gender, demographic and pathological factors. 13 Machine learning models were trained with proposed attributes and coefficient of attributes and internal relationships were calculated.

RESULT

Compared to other methods random forest (RF) has higher performance (accuracy 99.26%, precision 99%, and area under the curve (AUC) 99%). The results of assessing the impact and correlation of variables using the RF method based on PCA indicated that pathology, biomarker, biochemistry, gene, and demographic factors with a coefficient of 0.35, 0.23, 0.15, 0.14, and 0.13 respectively, affected the risk of BC (r2 = 0.54).

CONCLUSION

Breast cancer has several risk factors. Medical experts use these risk factors for early diagnosis. Therefore, identifying related risk factors and their effect can increase the accuracy of diagnosis. Considering the broad features for predicting breast cancer leads to the development of a comprehensive prediction model. In this study, using RF technique a breast cancer prediction model with 99.3% accuracy was developed based on multifactorial features.

Multi compartmental 3D breast cancer disease model–recapitulating tumor complexity in in-vitro

Breast cancer is the most common ailment among women. In 2020, it had the highest incidence of any type of cancer. Many Phase II and III anti-cancer drugs fail due to efficacy, durability, and side effects. Thus, accelerated drug screening models must be accurate. In-vivo models have been used for a long time, but delays, inconsistent results, and a greater sense of responsibility among scientists toward wildlife have led to the search for in-vitro alternatives. Stromal components support breast cancer growth and survival. Multi-compartment Transwell models may be handy instruments. Co-culturing breast cancer cells with endothelium and fibroblasts improves modelling. The extracellular matrix (ECM) supports native 3D hydrogels in natural and polymeric forms. 3D Transwell cultured tumor spheroids mimicked in-vivo pathological conditions. Tumor invasion, migration, Trans-endothelial migration, angiogenesis, and spread are studied using comprehensive models. Transwell models can create a cancer niche and conduct high-throughput drug screening, promising future applications. Our comprehensive shows how 3D in-vitro multi compartmental models may be useful in producing breast cancer stroma in Transwell culture.

Breast cancer animal models and applications

Breast cancer is the most common malignancy in women. Basic and translational breast cancer research relies heavily on experimental animal models. Ideally, such models for breast cancer should have commonality with human breast cancer in terms of tumor etiology, biological behavior, pathology, and response to therapeutics. This review introduces current progress in different breast cancer experimental animal models and analyzes their characteristics, advantages, disadvantages, and potential applications. Finally, we propose future research directions for breast cancer animal models.

Prognostic significance of an invasive leader cell-derived mutation cluster on chromosome 16q

BACKGROUND

Intratumoral heterogeneity is defined by subpopulations with varying genotypes and phenotypes. Specialized, highly invasive leader cells and less invasive follower cells are phenotypically distinct subpopulations that cooperate during collective cancer invasion. Because leader cells are a rare subpopulation that would be missed by bulk sequencing, a novel image-guided genomics platform was used to precisely select this subpopulation. This study identified a novel leader cell mutation signature and tested its ability to predict prognosis in non-small cell lung cancer (NSCLC) patient cohorts.

METHODS

Spatiotemporal genomic and cellular analysis was used to isolate and perform RNA sequencing on leader and follower populations from the H1299 NSCLC cell line, and it revealed a leader-specific mutation cluster on chromosome 16q. Genomic data from patients with lung squamous cell carcinoma (LUSC; n = 475) and lung adenocarcinoma (LUAD; n = 501) from The Cancer Genome Atlas were stratified by 16q mutation cluster (16qMC) status (16qMC+ vs 16qMC-) and compared for overall survival (OS), progression-free survival (PFS), and gene set enrichment analysis (GSEA).

RESULTS

Poorer OS, poorer PFS, or both were found across all stages and among early-stage patients with 16qMC+ tumors within the LUSC and LUAD cohorts. GSEA revealed 16qMC+ tumors to be enriched for the expression of metastasis- and survival-associated gene sets.

CONCLUSIONS

This represents the first leader cell mutation signature identified in patients and has the potential to better stratify high-risk NSCLC and ultimately improve patient outcomes.

Development and characterization of mammary intraductal (MIND) spontaneous metastasis models for triple-negative breast cancer in syngeneic mice

Triple-negative breast cancer (TNBC) has a more aggressive phenotype and higher metastasis and recurrence rates than other breast cancer subtypes. TNBC currently lacks a transplantation model that is suitable for clinical simulations of the tumor microenvironment. Intraductal injection of tumor cells into the mammary duct could mimic the occurrence and development of breast cancer. Herein, we injected 4T1 cells into the mammary ducts of BALB/C mice to build a preclinical model of TNBC and optimized the related construction method to observe the occurrence and spontaneous metastasis of tumors. We compared the effects of different cell numbers on tumorigenesis rates, times to tumorigenesis, and metastases to determine the optimal number of cells for modelling. We demonstrated that 4T1-MIND model mice injected with 20,000 cells revealed a suitable tumor formation rate and time, thus indicating a potential treatment time window after distant metastasis. We also injected 20,000 cells directly into the breast fat pad or breast duct for parallel comparison. The results still showed that the 4T1-MIND model provides sufficient treatment time for lung metastases in mice and that it is a more reliable model for early tumor development. The 4T1-MIND model requires continuous improvement and optimization. A suitable and optimized model for translational research and studies on the microenvironment in TNBC should be developed.

Association between Cyclin D1 G870A (rs9344) polymorphism and cancer risk in Indian population: meta-analysis and trial sequential analysis

Introduction: Association between Cyclin D1 (CCND1) single nucleotide polymorphism (SNP) rs9344 and cancer risk is paradoxical. Thus, we performed a meta-analysis to explore the association between CCND1 variant and overall cancer risk in Indian population. Methods: Data from 12 published studies including 3739 subjects were collected using Pubmed and Embase. RevMan (Review Manager) 5.3 was used to perform the meta-analysis. OR with 95%CI were calculated to establish the association. Results: Overall, the cumulative findings demonstrated that CCND1 polymorphism (rs9344) was not significantly associated with cancer risk in all the genetic models studied (dominant model: GG vs GA+AA: OR (95%CI) = 0.81 (0.60-1.09), P=0.17; recessive model: GG+GA vs AA: OR (95%CI) = 1.23 (0.96-1.59), P=0.11; co-dominant model: GG vs AA: OR (95%CI) = 1.35 (0.93-1.97), P=0.12; co-dominant model: (GG vs GA: OR (95%CI) = 1.16 (0.85-1.59), P=0.34; allelic model: A vs G: OR (95%CI) = 1.20 (1.14-2.85), P=0.23; allelic model: G vs A: OR (95%CI) = 0.83 (0.62-1.12), P=0.23). Subgroup analysis according to cancer types presented significant association of CCND1 polymorphism and increased breast cancer risk in dominant model (GG vs GA+AA: OR = 2.75, 95%CI = 1.54-4.90, P=0.0006) and allelic model (G vs A: OR = 1.63, 95%CI = 1.22-2.19, P=0.001). An increased esophageal cancer risk in recessive model (GG+GA vs AA: OR = 1.51, 95%CI = 1.05-2.16, P=0.03) and co-dominant model (GG vs AA: OR = 2.51, 95%CI = 1.10-5.71, P=0.03) was detected. A higher risk for colorectal cancer was detected under both the co-dominant models (GG vs AA: OR = 2.46, 95%CI = 1.34-4.51, P=0.004 and GG vs GA: OR = 1.74, 95%CI = 1.14-2.67, P=0.01). However, in case of cervical cancer risk a non-significant association was reported under the recessive model (GG+GA vs AA: OR = 1.52, 95%CI = 0.60-3.90, P=0.38) with reference to CCND1 polymorphism (rs9344). The trial sequential analysis (TSA) showed that the cumulative Z-curve neither crossed the trial sequential monitoring boundary nor reached the required information size (RIS). Thus, present meta-analysis remained inconclusive due to insufficient evidence. Conclusion:CCND1 polymorphism rs9344 may not have a role in overall cancer susceptibility in Indian population. However, this polymorphism acts as a crucial risk factor for breast, esophageal, and colorectal cancer but not for cervical cancer. Future studies with larger sample size are required to draw a reliable conclusion.

An epigenetic perspective on tumorigenesis: Loss of cell identity, enhancer switching, and NamiRNA network

Various tumorigenic theories have been proposed in the past century, which contribute to the prevention and treatment of cancer clinically. However, the underlying mechanisms of the initiation of cancer, drug resistance, neoplasm relapse, and metastasis are still challenging to be panoramically addressed. Based on the abundant evidence provided by others and us, we postulate that Tumor Initiated by Loss of Cell Identity (LOCI), which is an inevitable initiating event of tumorigenesis. As a result, normal cells are transformed into the cancerous cell. In this process, epigenetic regulatory program, especially NamiRNA (Nuclear activating miRNA)-enhancer-gene activation network, is vital for the cell identity. The disorganization of NamiRNA-enhancer-gene activation network is a causal predisposition to the cell identity loss, and the altered cell identity is stabilized by genetic variations of the NamiRNA-enhancer-gene activation network. Furthermore, the additional genetic or epigenetic abnormities confer those cells to carcinogenic characteristics, such as growth advantage over normal cells, and finally yield cancer. In this review, we literally explain our tumor initiation hypothesis based on the corresponding evidence, which will not only help to refresh our understanding of tumorigenesis but also bring benefits to developing "cell identity reversing" based therapies.

An epigenetic perspective on tumorigenesis: Loss of cell identity, enhancer switching, and NamiRNA network

Various tumorigenic theories have been proposed in the past century, which contribute to the prevention and treatment of cancer clinically. However, the underlying mechanisms of the initiation of cancer, drug resistance, neoplasm relapse, and metastasis are still challenging to be panoramically addressed. Based on the abundant evidence provided by others and us, we postulate that Tumor Initiated by Loss of Cell Identity (LOCI), which is an inevitable initiating event of tumorigenesis. As a result, normal cells are transformed into the cancerous cell. In this process, epigenetic regulatory program, especially NamiRNA (Nuclear activating miRNA)-enhancer-gene activation network, is vital for the cell identity. The disorganization of NamiRNA-enhancer-gene activation network is a causal predisposition to the cell identity loss, and the altered cell identity is stabilized by genetic variations of the NamiRNA-enhancer-gene activation network. Furthermore, the additional genetic or epigenetic abnormities confer those cells to carcinogenic characteristics, such as growth advantage over normal cells, and finally yield cancer. In this review, we literally explain our tumor imitation hypothesis based on the corresponding evidence, which will not only help to refresh our understanding of tumorigenesis but also bring benefits to developing "cell identity reversing" based therapies.

A Transcriptome-Level Study Identifies Changing Expression Profiles for Ossification of the Ligamentum Flavum of the Spine

Ossification of the ligamentum flavum (OLF) is a common spinal disorder that causes myelopathy and radiculopathy. Non-coding RNAs (ncRNAs) are involved in numerous pathological processes; however, very few ncRNAs have been identified to be correlated with OLF. Here we compared the expression of lncRNA, mRNA, circRNA, and microRNA in OLF tissues from OLF patients and healthy volunteers through mRNA, lncRNA, and circRNA microarrays and microRNA sequencing. A total of 2,054 mRNAs, 2,567 lncRNAs, 627 circRNAs, and 28 microRNAs (miRNAs) were altered during the process of OLF. qPCR confirmed the differential expression of selected mRNAs and ncRNAs. An lncRNA-mRNA co-expression network, miRNA-mRNA target prediction network, and competing endogenous RNA (ceRNA) network of circRNA-miRNA-mRNA were constructed based on a correlation analysis of the differentially expressed RNA transcripts. Subsequently, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses for the differentially expressed mRNAs and the predicted miRNAs target genes were performed. In addition, a deregulated miRNA-19b-3p-based miRNA-circRNA-lncRNA-mRNA network was confirmed, by gain-of-function and loss-of-function experiments, to function in the process of ossification. Taken together, this study provides a systematic perspective on the potential function of ncRNAs in the pathogenesis of OLF.

Association of CCND1 Gene c.870G>A Polymorphism with Breast Cancer Risk: A Case-ControlStudy and a Meta-Analysis

Cyclin D1 (CCND1) plays an essential role in regulating the progress of the cell cycle from G1 to S phase. There is a common c.870G>A polymorphism in the CCND1 gene. The aim of this study was to investigate the association of CCND1 gene c.870G>A polymorphism with breast cancer risk in a case-control study, which followed by a meta-analysis and an in silico analysis. Three hundred and thirty-five subjects composed of 174 women with breast cancer and 161 healthy controls were included in the case-control study. CCND1 gene c.870G>A genotyping was performed by PCR-RFLP. Meta-analysis was done for 14 studies composed of 7281 cases and 6820 controls. Some bioinformatics tools were applied to investigate the effects of c.870G>A on the mRNA splicing and structure. Our data obtained from case-control study revealed that GA genotype (OR: 1.89, 95%CI: 1.12-3.17, p = 0.017), AA genotype (OR: 1.95, 95%CI: 1.08-3.53, p = 0.027), and A allele (OR: 1.44, 95%CI: 1.06-1.95, p = 0.019) were significantly associated with breast cancer risk. The results of meta-analysis showed a significant association between CCND1 c.870G>A polymorphism and breast cancer risk, especially in Caucasian population. In silico analysis revealed that c.870G>A transition affect CCND1 mRNA splicing and secondary structure.

The landscape of chromosomal aberrations in breast cancer mouse models reveals driver-specific routes to tumorigenesis

Aneuploidy and copy-number alterations (CNAs) are a hallmark of human cancer. Although genetically engineered mouse models (GEMMs) are commonly used to model human cancer, their chromosomal landscapes remain underexplored. Here we use gene expression profiles to infer CNAs in 3,108 samples from 45 mouse models, providing the first comprehensive catalogue of chromosomal aberrations in cancer GEMMs. Mining this resource, we find that most chromosomal aberrations accumulate late during breast tumorigenesis, and observe marked differences in CNA prevalence between mouse mammary tumours initiated with distinct drivers. Some aberrations are recurrent and unique to specific GEMMs, suggesting distinct driver-dependent routes to tumorigenesis. Synteny-based comparison of mouse and human tumours narrows critical regions in CNAs, thereby identifying candidate driver genes. We experimentally validate that loss of Stratifin (SFN) promotes HER2-induced tumorigenesis in human cells. These results demonstrate the power of GEMM CNA analysis to inform the pathogenesis of human cancer.

Genetically engineered mouse models of cancer are useful in identifying oncogenes and tumour suppressors. Here, the authors use gene expression profiles to generate a catalogue of copy number aberrations in 45 mouse models, and compare mouse and human tumours to identify additional drivers of tumorigenesis.

Cytogenetic Assessment of the Rat Cell Line CLS-ACI-1: An in vitro Cell Model for Mycn Overexpression

Breast cancer is a complex and heterogeneous disease, and the establishment of cell models in order to properly study the disease at the molecular and cellular level is of utmost importance. Here, we present the cytogenetic characterization and gene expression analysis of the tumoral mammary rat cell line CLS-ACI-1. The use of banding and molecular cytogenetic techniques allowed the description of the complex CLS-ACI-1 karyotype and the identification of breakpoints in clonal chromosome rearrangements. Moreover, a Mycn and Erbb2 comparative expression analysis by RT-qPCR was performed, revealing a high expression level of Mycn in CLS-ACI-1 cells. Moreover, a considerable number of putative mutated genes and chromosome alterations detected through cytogenetic analysis seem to be in the MYCN biological network. Therefore, the CLS-ACI-1 cell line is presented as a promising cell model for the study of the role of MYCN in breast cancer and also as a tool for developing appropriate cancer therapies, namely for Mycn targeting.