scholarly journals Raman spectroscopy reveals phenotype switches in breast cancer metastasis

2021 ◽  
Author(s):  
Santosh Kumar Paidi ◽  
Joel Rodriguez Troncoso ◽  
Mason G. Harper ◽  
Zhenhui Liu ◽  
Khue G. Nguyen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Raman Spectroscopy ◽  
Random Forest ◽  
Cell Lines ◽  
Primary Tumor ◽  
Cancer Metastasis ◽  
Breast Cancer Cell Lines ◽  
Metastatic Phenotype ◽  
Phenotypic Switch

AbstractThe accurate analytical characterization of metastatic phenotype at primary tumor diagnosis and its evolution with time are critical for controlling metastatic progression of cancer. Here, we report a label-free optical strategy using Raman spectroscopy and machine learning to identify distinct metastatic phenotypes observed in tumors formed by isogenic murine breast cancer cell lines of progressively increasing metastatic propensities. Our Raman spectra-based random forest analysis provided evidence that machine learning models built on spectral data can allow the accurate identification of metastatic phenotype of independent test tumors. By silencing genes critical for metastasis in highly metastatic cell lines, we showed that the random forest classifiers provided predictions consistent with the observed phenotypic switch of the resultant tumors towards lower metastatic potential. Furthermore, the spectral assessment of lipid and collagen content of these tumors was consistent with the observed phenotypic switch. Overall, our findings indicate that Raman spectroscopy may offer a novel strategy to evaluate metastatic risk during primary tumor biopsies in clinical patients.

scholarly journals Detection of acquired radioresistance in breast cancer cell lines using Raman spectroscopy and machine learning

The Analyst ◽  
2021 ◽  
Author(s):  
Kevin Saruni Tipatet ◽  
Liam Davison-Gates ◽  
Thomas Johann Tewes ◽  
Emmanuel Kwasi Fiagbedzi ◽  
Alistair Elfick ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Raman Spectroscopy ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Tumour Recurrence ◽  
Breast Cancer Cell Lines ◽  
Development Of Resistance ◽  
Radiotherapy Failure

Radioresistance—a living cell’s response to, and development of resistance to ionising radiation—can lead to radiotherapy failure and/or tumour recurrence. We used Raman spectroscopy and machine learning to characterise biochemical changes...

scholarly journals Multi-Omics Characterization of the Spontaneous Mesenchymal–Epithelial Transition in the PMC42 Breast Cancer Cell Lines

2019 ◽  
Vol 8 (8) ◽  
pp. 1253 ◽  
Author(s):  
Sugandha Bhatia ◽  
James Monkman ◽  
Tony Blick ◽  
Pascal HG Duijf ◽  
Shivashankar H. Nagaraj ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Phenotypic Plasticity ◽  
Cell Line ◽  
Cell Lines ◽  
Daughter Cell ◽  
Cancer Metastasis ◽  
Expression Profiles ◽  
Breast Cancer Cell Lines ◽  
Mesenchymal Transition ◽  
Mesenchymal Epithelial Transition

Epithelial–mesenchymal plasticity (EMP), encompassing epithelial–mesenchymal transition (EMT) and mesenchymal–epithelial transition (MET), are considered critical events for cancer metastasis. We investigated chromosomal heterogeneity and chromosomal instability (CIN) profiles of two sister PMC42 breast cancer (BC) cell lines to assess the relationship between their karyotypes and EMP phenotypic plasticity. Karyotyping by GTG banding and exome sequencing were aligned with SWATH quantitative proteomics and existing RNA-sequencing data from the two PMC42 cell lines; the mesenchymal, parental PMC42-ET cell line and the spontaneously epithelially shifted PMC42-LA daughter cell line. These morphologically distinct PMC42 cell lines were also compared with five other BC cell lines (MDA-MB-231, SUM-159, T47D, MCF-7 and MDA-MB-468) for their expression of EMP and cell surface markers, and stemness and metabolic profiles. The findings suggest that the epithelially shifted cell line has a significantly altered ploidy of chromosomes 3 and 13, which is reflected in their transcriptomic and proteomic expression profiles. Loss of the TGFβR2 gene from chromosome 3 in the epithelial daughter cell line inhibits its EMT induction by TGF-β stimulus. Thus, integrative ‘omics’ characterization established that the PMC42 system is a relevant MET model and provides insights into the regulation of phenotypic plasticity in breast cancer.

scholarly journals Divergent organ-specific isogenic metastatic cell lines identified using multi-omics exhibit differential drug sensitivity

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242384
Author(s):  
Paul T. Winnard ◽  
Farhad Vesuna ◽  
Sankar Muthukumar ◽  
Venu Raman
Keyword(s):  
Breast Cancer ◽  
Cell Lines ◽  
Metastatic Disease ◽  
Primary Tumor ◽  
Metastatic Cancer ◽  
Tumor Biology ◽  
Treatment Strategies ◽  
Breast Cancer Cell Lines ◽  
Visceral Organs ◽  
Organ Specific

Background Monitoring and treating metastatic progression remains a formidable task due, in part, to an inability to monitor specific differential molecular adaptations that allow the cancer to thrive within different tissue types. Hence, to develop optimal treatment strategies for metastatic disease, an important consideration is the divergence of the metastatic cancer growing in visceral organs from the primary tumor. We had previously reported the establishment of isogenic human metastatic breast cancer cell lines that are representative of the common metastatic sites observed in breast cancer patients. Methods Here we have used proteomic, RNAseq, and metabolomic analyses of these isogenic cell lines to systematically identify differences and commonalities in pathway networks and examine the effect on the sensitivity to breast cancer therapeutic agents. Results Proteomic analyses indicated that dissemination of cells from the primary tumor sites to visceral organs resulted in cell lines that adapted to growth at each new site by, in part, acquiring protein pathways characteristic of the organ of growth. RNAseq and metabolomics analyses further confirmed the divergences, which resulted in differential efficacies to commonly used FDA approved chemotherapeutic drugs. This model system has provided data that indicates that organ-specific growth of malignant lesions is a selective adaptation and growth process. Conclusions The insights provided by these analyses indicate that the rationale of targeted treatment of metastatic disease may benefit from a consideration that the biology of metastases has diverged from the primary tumor biology and using primary tumor traits as the basis for treatment may not be ideal to design treatment strategies.

scholarly journals A cell-to-patient machine learning transfer approach uncovers novel basal-like breast cancer prognostic markers amongst alternative splice variants

2020 ◽  
Author(s):  
Jean-Philippe Villemin ◽  
Claudio Lorenzi ◽  
Andrew Oldfield ◽  
Marie-Sarah Cabrillac ◽  
William Ritchie ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cell Lines ◽  
Cell Invasion ◽  
Triple Negative ◽  
Splice Variants ◽  
Breast Cancer Cell Lines ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Alternative Splice Variants

ABSTRACTBackgroundBreast cancer is amongst the 10 first causes of death in women worldwide. Around 20% of patients are misdiagnosed leading to early metastasis, resistance to treatment and relapse. Many clinical and gene expression profiles have been successfully used to classify breast tumours into 5 major types with different prognosis and sensitivity to specific treatments. Unfortunately, these profiles have failed to subclassify breast tumours into more subtypes to improve diagnostics and survival rate. Alternative splicing is emerging as a new source of highly specific biomarkers to classify tumours in different grades. Taking advantage of extensive public transcriptomics datasets in breast cancer cell lines (CCLE) and breast cancer tumours (TCGA), we have addressed the capacity of alternative splice variants to subclassify highly aggressive breast cancers.ResultsTranscriptomics analysis of alternative splicing events between luminal, basal A and basal B breast cancer cell lines identified a unique splicing signature for a subtype of tumours, the basal B, whose classification is not in use in the clinic yet. Basal B cell lines, in contrast with luminal and basal A, are highly metastatic and express epithelial-to-mesenchymal (EMT) markers, which are hallmarks of cell invasion and resistance to drugs. By developing a semi-supervised machine learning approach, we transferred the molecular knowledge gained from these cell lines into patients to subclassify basal-like triple negative tumours into basal A- and basal B-like categories. Changes in splicing of 25 alternative exons, intimately related to EMT and cell invasion such as ENAH, CD44 and CTNND1, were sufficient to identify the basal-like patients with the worst prognosis. Moreover, patients expressing this basal B-specific splicing signature also expressed newly identified biomarkers of metastasis-initiating cells, like CD36, supporting a more invasive phenotype for this basal B-like breast cancer subtype.ConclusionsUsing a novel machine learning approach, we have identified an EMT-related splicing signature capable of subclassifying the most aggressive type of breast cancer, which are basal-like triple negative tumours. This proof-of-concept demonstrates that the biological knowledge acquired from cell lines can be transferred to patients data for further clinical investigation. More studies, particularly in 3D culture and organoids, will increase the accuracy of this transfer of knowledge, which will open new perspectives into the development of novel therapeutic strategies and the further identification of specific biomarkers for drug resistance and cancer relapse.

scholarly journals The evaluation of Melatonin and EGF interaction on breast cancer metastasis

2021 ◽  
Author(s):  
Maryam Akbarzadeh
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Metastasis ◽  
Migration And Invasion ◽  
Breast Cancer Cell Lines ◽  
Breast Cancer Patients ◽  
Mcf7 Cells ◽  
And Migration

Abstract Background Breast cancer is currently one of the most common types of cancer in women, and metastasis is the first cause of death in breast cancer patients. The epidermal growth factor (EGF) increases the invasion, growth, and migration of cancer cells. In the present study, melatonin, as a natural hormone, in EGF-induced tumor metastasis, was investigated. Methods First, MDA-MB-231 and MCF7 cells were cultured, and then the effects of melatonin on cell viability were determined by MTT assay. Transwell invasion assay was employed to identify the invasiveness of these breast cancer cell lines. Real-time RT-PCR then investigated the expression of MMP9 and MMP2. Cell proliferation was also determined under EGF and melatonin treatment using Ki67 assessment by flow cytometry. Results The rate of invasion and migration of EGF-treated cells increased in both groups, in which melatonin caused increased invasion by EGF in MCF7 cells. MMP9 and MMP2 expression increased significantly in both cell lines under EGF treatment, and melatonin increased these genes' expression in both cell lines (p <0.05). EGF increased the MMP9 and MMP2 gene expression, and melatonin increases EGF-induced expression(p <0.05). The EGF reduced the expression of the Ki67 protein in the MCF7 cell line, which was negatively affected by Melatonin and EGF. In contrast, along with Melatonin, EGF did not affect the proliferation of the MDA-MB-231 cell line. Conclusions Our results show that melatonin, as a natural compound, can increase the effects of EGF in the proliferation, migration, and invasion of cancer cells at low dosages.

scholarly journals Integrated Transcriptomics, Proteomics, and Glycomics Reveals the Association between Up-regulation of Sialylated N-glycans/Integrin and Breast Cancer Brain Metastasis

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Wenjing Peng ◽  
Rui zhu ◽  
Shiyue Zhou ◽  
Parvin Mirzaei ◽  
Yehia Mechref
Keyword(s):  
Breast Cancer ◽  
Brain Metastasis ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Metastasis ◽  
Cancer Cell Line ◽  
Breast Cancer Metastasis ◽  
Breast Cancer Cell Lines ◽  
Breast Cancer Brain Metastasis

AbstractBreast cancer brain metastasis has been recognized as one of the central issues in breast cancer research. The elucidation of the processes and pathways that mediate this step will provide important clues for a better understanding of breast cancer metastasis. Increasing evidence suggests that aberrant glycosylation patterns greatly contribute to cell invasion and cancer metastasis. Herein, we combined next-generation RNA sequencing with liquid chromatography-tandem mass spectrometry-based proteomic and N-glycomic analysis from five breast cancer cell lines and one brain cancer cell line to investigate the possible mechanisms of breast cancer brain metastasis. The genes/proteins associated with cell movement were highlighted in breast cancer brain metastasis. The integrin signaling pathway and the up-regulation of α-integrin (ITGA2, ITGA3) were associated with the brain metastatic process. 12 glycogenes showed unique expression in 231BR, which could result in an increase of sialylation during brain metastasis. In agreement with the changes of glycogenes, 60 out of 63 N-glycans that were identified exhibited differential expression among cell lines. The correlation between glycogenes and glycans revealed the importance of sialylation and sialylated glycans in breast cancer brain metastasis. Highly sialylated N-glycans, which were up-regulated in brain-seeking cell line 231BR, likely play a role in brain metastasis.

scholarly journals Machine Learning Model for Lymph Node Metastasis Prediction in Breast Cancer Using Random Forest Algorithm and Mitochondrial Metabolism Hub Genes

2021 ◽  
Vol 11 (7) ◽  
pp. 2897
Author(s):  
Byung-Chul Kim ◽  
Jingyu Kim ◽  
Ilhan Lim ◽  
Dong Ho Kim ◽  
Sang Moo Lim ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Random Forest ◽  
Prediction Model ◽  
Cancer Metastasis ◽  
Breast Cancer Metastasis ◽  
Hub Genes ◽  
Hub Gene ◽  
Gene Set ◽  
Machine Learning Model

Breast cancer metastasis can have a fatal outcome, with the prediction of metastasis being critical for establishing effective treatment strategies. RNA-sequencing (RNA-seq) is a good tool for identifying genes that promote and support metastasis development. The hub gene analysis method is a bioinformatics method that can effectively analyze RNA sequencing results. This can be used to specify the set of genes most relevant to the function of the cell involved in metastasis. Herein, a new machine learning model based on RNA-seq data using the random forest algorithm and hub genes to estimate the accuracy of breast cancer metastasis prediction. Single-cell breast cancer samples (56 metastatic and 38 non-metastatic samples) were obtained from the Gene Expression Omnibus database, and the Weighted Gene Correlation Network Analysis package was used for the selection of gene modules and hub genes (function in mitochondrial metabolism). A machine learning prediction model using the hub gene set was devised and its accuracy was evaluated. A prediction model comprising 54-functional-gene modules and the hub gene set (NDUFA9, NDUFB5, and NDUFB3) showed an accuracy of 0.769 ± 0.02, 0.782 ± 0.012, and 0.945 ± 0.016, respectively. The test accuracy of the hub gene set was over 93% and that of the prediction model with random forest and hub genes was over 91%. A breast cancer metastasis dataset from The Cancer Genome Atlas was used for external validation, showing an accuracy of over 91%. The hub gene assay can be used to predict breast cancer metastasis by machine learning.

scholarly journals Malic enzyme 1 may be a novel target of breast cancer metastasis

2019 ◽  
Author(s):  
Chang Liu ◽  
Shuchen Lin ◽  
Yannan Zhao ◽  
Jun Cao ◽  
Zhonghua Tao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cell Lines ◽  
Malic Enzyme ◽  
Prognostic Value ◽  
Cancer Metastasis ◽  
Breast Cancer Metastasis ◽  
Breast Cancer Cell Lines ◽  
Public Database ◽  
The Public ◽  
Mcf 7

Abstract Background Malic enzyme 1 (ME1) catalyzes malate to pyruvate and thus promotes glycolysis, playing a part in the Warburg effect. It also has a potential role in tumor progression, but its function in breast cancer remains to be fully clarified. This work aimed to investigate the prognostic value of ME1 and its possible mechanism in breast cancer.Methods We evaluated ME1 expression in 220 early breast cancer patients with tissue microarray-based immunohistochemistry and explored the relationships between ME1 expression and clinicopathological features. Survival analyses were further performed to determine its prognostic value. The public database was used to confirm tissue microarray results. Moreover, we profiled ME1 expression in breast cancer cell lines via western blotting, and then assessed it in cell viability and motility via Cell counting kit-8 (CCK-8), colony formation, transwell migration and invasion assays. Reactive oxygen species (ROS) was detected by dihydroethidium (DHE) and 2’,7’-Dichlorodihydrofluorescein diacetate (DCFH-DA).Results In breast cancer tissues, high ME1 expression was significantly associated with larger tumor size, more lymph node metastasis and more extensive lymph-vascular invasion. Survival analysis showed high ME1 expression was significantly correlated with worse recurrence free survival (RFS). Multivariate analysis further identified high ME1 expression as an independent prognostic factor for RFS, which was confirmed by mRNA results in the public database. In vitro , human epidermal growth factor receptor-2 positive and triple negative breast cancer cell lines showed higher expression of ME1, while Luminal cell lines showed lower expression of ME1. Upregulation of ME1 by transfecting MCF-7 cells with virus vector remarkably enhanced viability and motility, epithelial-mesenchymal transition (EMT), and decreased ROS levels, whereas knockdown of this gene in MDA-MB-468 cells produced totally opposite effects as expected. More important, when pretreated with hydrogen peroxide, an oxidizing agent, MCF-7 cells overexpressing ME1 lost its motility, whereas MDA-MB-468 cells with knock-down of ME1 restored its motility when pretreated with N-acetyl cysteine, an antioxidant.Conclusions To our knowledge, these clinical and experiment work first suggested that ME1 may be a potential therapeutic target for breast cancer metastasis, and its biological effect is mainly controlled by manipulating ROS.

Sign in / Sign up

Export Citation Format

Share Document