Similarities and Differences in Gene Expression Networks Between the Breast Cancer Cell Line Michigan Cancer Foundation-7 and Invasive Human Breast Cancer Tissues

Breast cancer preclinical models: a vital resource for comprehending disease mechanisms and therapeutic development

A significant obstacle in translating innovative breast cancer treatments from bench to bed side is demonstrating efficacy in preclinical settings prior to clinical trials, as the heterogeneity of breast cancer can be challenging to replicate in the laboratory. A significant number of potential medicines have not progressed to clinical trials because preclinical models inadequately replicate the complexities of the varied tumor microenvironment. Consequently, the variety of breast cancer models is extensive, and the selection of a model frequently depends on the specific inquiry presented. This review aims to present an overview of the existing breast cancer models, highlighting their advantages, limitations, and challenges in the context of innovative drug discovery, thereby offering insights that may be advantageous to future translational studies. Conventional monolayer cultures are critical for elucidating the different breast cancer types and their behavior, have limitations in adequately replicating tumor environments. The 3D models such as patient-derived xenografts, cell-derived xenografts and genetically engineered models offer better insights by maintaining tumor microenvironments and cellular heterogeneity. Results can be further enhanced when compared with breast epithelial cells, a negative control to determine early stages by investigating differences between healthy and cancerous mammary cells. While cell lines such as MCF-7, MDA-MB-231 etc are useful in vitro models, they exhibit genetic variations that may affect drug responses over time. Additionally, animal models, particularly rodents, are instrumental in breast cancer research due to their biological resemblances to humans and the relative ease of genetic modification, however, witness a low occurrence of tumors. This review thus concludes that different preclinical models have their associated benefits and pitfalls. Therefore, specific preclinical models can be created by altering the gene expression at the genetic level or could be selected as per specific experimental needs which will enable successful translation of preclinical findings into clinical trials can be possible. See also the graphical abstract(Fig. 1).

Transcriptomics indicate nuclear division and cell adhesion not recapitulated in MCF7 and MCF10A compared to luminal A breast tumours

Breast cancer (BC) cell lines are useful experimental models to understand cancer biology. Yet, their relevance to modelling cancer remains unclear. To better understand the tumour-modelling efficacy of cell lines, we performed RNA-seq analyses on a combined dataset of 2D and 3D cultures of tumourigenic MCF7 and non-tumourigenic MCF10A. To our knowledge, this was the first RNA-seq dataset comprising of 2D and 3D cultures of MCF7 and MCF10A within the same experiment, which facilitates the elucidation of differences between MCF7 and MCF10A across culture types. We compared the genes and gene sets distinguishing MCF7 from MCF10A against separate RNA-seq analyses of clinical luminal A (LumA) and normal samples from the TCGA-BRCA dataset. Among the 1031 cancer-related genes distinguishing LumA from normal samples, only 5.1% and 15.7% of these genes also distinguished MCF7 from MCF10A in 2D and 3D cultures respectively, suggesting that different genes drive cancer-related differences in cell lines compared to clinical BC. Unlike LumA tumours which showed increased nuclear division-related gene expression compared to normal tissue, nuclear division-related gene expression in MCF7 was similar to MCF10A. Moreover, although LumA tumours had similar cell adhesion-related gene expression compared to normal tissues, MCF7 showed reduced cell adhesion-related gene expression compared to MCF10A. These findings suggest that MCF7 and MCF10A cell lines were limited in their ability to model cancer-related processes in clinical LumA tumours.

FunHoP analysis reveals upregulation of mitochondrial genes in prostate cancer

Mitochondrial activity in cancer cells has been central to cancer research since Otto Warburg first published his thesis on the topic in 1956. Although Warburg proposed that oxidative phosphorylation in the tricarboxylic acid (TCA) cycle was perturbed in cancer, later research has shown that oxidative phosphorylation is activated in most cancers, including prostate cancer (PCa). However, more detailed knowledge on mitochondrial metabolism and metabolic pathways in cancers is still lacking. In this study we expand our previously developed method for analyzing functional homologous proteins (FunHoP), which can provide a more detailed view of metabolic pathways. FunHoP uses results from differential expression analysis of RNA-Seq data to improve pathway analysis. By adding information on subcellular localization based on experimental data and computational predictions we can use FunHoP to differentiate between mitochondrial and non-mitochondrial processes in cancerous and normal prostate cell lines. Our results show that mitochondrial pathways are upregulated in PCa and that splitting metabolic pathways into mitochondrial and non-mitochondrial counterparts using FunHoP adds to the interpretation of the metabolic properties of PCa cells.

Experimental implementation of a neural network optical channel equalizer in restricted hardware using pruning and quantization

The deployment of artificial neural networks-based optical channel equalizers on edge-computing devices is critically important for the next generation of optical communication systems. However, this is still a highly challenging problem, mainly due to the computational complexity of the artificial neural networks (NNs) required for the efficient equalization of nonlinear optical channels with large dispersion-induced memory. To implement the NN-based optical channel equalizer in hardware, a substantial complexity reduction is needed, while we have to keep an acceptable performance level of the simplified NN model. In this work, we address the complexity reduction problem by applying pruning and quantization techniques to an NN-based optical channel equalizer. We use an exemplary NN architecture, the multi-layer perceptron (MLP), to mitigate the impairments for 30 GBd 1000 km transmission over a standard single-mode fiber, and demonstrate that it is feasible to reduce the equalizer’s memory by up to 87.12%, and its complexity by up to 78.34%, without noticeable performance degradation. In addition to this, we accurately define the computational complexity of a compressed NN-based equalizer in the digital signal processing (DSP) sense. Further, we examine the impact of using hardware with different CPU and GPU features on the power consumption and latency for the compressed equalizer. We also verify the developed technique experimentally, by implementing the reduced NN equalizer on two standard edge-computing hardware units: Raspberry Pi 4 and Nvidia Jetson Nano, which are used to process the data generated via simulating the signal’s propagation down the optical-fiber system.