scholarly journals High BMI1 Expression with Low CD8+ and CD4+ T Cell Activity Could Promote Breast Cancer Cell Survival: A Machine Learning Approach

2021 ◽  
Vol 11 (8) ◽  
pp. 739
Author(s):  
Yumin Chung ◽  
Kyueng-Whan Min ◽  
Dong-Hoon Kim ◽  
Byoung Kwan Son ◽  
Sung-Im Do ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Immune Response ◽  
T Cell ◽  
Network Analysis ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Survival Rates ◽  
Cd4 T Cell ◽  
Bmi1 Expression

BMI1 is known to play a key role in the regulation of stem cell self-renewal in both endogenous and cancer stem cells. High BMI1 expression has been associated with poor prognosis in a variety of human tumors. The aim of this study was to reveal the correlations of BMI1 with survival rates, genetic alterations, and immune activities, and to validate the results using machine learning. We investigated the survival rates according to BMI1 expression in 389 and 789 breast cancer patients from Kangbuk Samsung Medical Center (KBSMC) and The Cancer Genome Atlas, respectively. We performed gene set enrichment analysis (GSEA) with pathway-based network analysis, investigated the immune response, and performed in vitro drug screening assays. The survival prediction model was evaluated through a gradient boosting machine (GBM) approach incorporating BMI1. High BMI1 expression was correlated with poor survival in patients with breast cancer. In GSEA and in in silico flow cytometry, high BMI1 expression was associated with factors indicating a weak immune response, such as decreased CD8+ T cell and CD4+ T cell counts. In pathway-based network analysis, BMI1 was directly linked to transcriptional regulation and indirectly linked to inflammatory response pathways, etc. The GBM model incorporating BMI1 showed improved prognostic performance compared with the model without BMI1. We identified telomerase inhibitor IX, a drug with potent activity against breast cancer cell lines with high BMI1 expression. We suggest that high BMI1 expression could be a therapeutic target in breast cancer. These results could contribute to the design of future experimental research and drug development programs for breast cancer.

scholarly journals Learning deep features for dead and living breast cancer cell classification without staining

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gisela Pattarone ◽  
Laura Acion ◽  
Marina Simian ◽  
Emmanuel Iarussi
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Drug Treatment ◽  
Cancer Cells ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Cancer Biology ◽  
Breast Cancer Cells ◽  
Bright Field ◽  
Cell Classification

AbstractAutomated cell classification in cancer biology is a challenging topic in computer vision and machine learning research. Breast cancer is the most common malignancy in women that usually involves phenotypically diverse populations of breast cancer cells and an heterogeneous stroma. In recent years, automated microscopy technologies are allowing the study of live cells over extended periods of time, simplifying the task of compiling large image databases. For instance, there have been several studies oriented towards building machine learning systems capable of automatically classifying images of different cell types (i.e. motor neurons, stem cells). In this work we were interested in classifying breast cancer cells as live or dead, based on a set of automatically retrieved morphological characteristics using image processing techniques. Our hypothesis is that live-dead classification can be performed without any staining and using only bright-field images as input. We tackled this problem using the JIMT-1 breast cancer cell line that grows as an adherent monolayer. First, a vast image set composed by JIMT-1 human breast cancer cells that had been exposed to a chemotherapeutic drug treatment (doxorubicin and paclitaxel) or vehicle control was compiled. Next, several classifiers were trained based on well-known convolutional neural networks (CNN) backbones to perform supervised classification using labels obtained from fluorescence microscopy images associated with each bright-field image. Model performances were evaluated and compared on a large number of bright-field images. The best model reached an AUC = 0.941 for classifying breast cancer cells without treatment. Furthermore, it reached AUC = 0.978 when classifying breast cancer cells under drug treatment. Our results highlight the potential of machine learning and computational image analysis to build new diagnosis tools that benefit the biomedical field by reducing cost, time, and stimulating work reproducibility. More importantly, we analyzed the way our classifiers clusterize bright-field images in the learned high-dimensional embedding and linked these groups to salient visual characteristics in live-dead cell biology observed by trained experts.

scholarly journals Regulating BMI1 expression via miRNAs promote Mesenchymal to Epithelial Transition (MET) and sensitizes breast cancer cell to chemotherapeutic drug

PLoS ONE ◽  
2018 ◽  
Vol 13 (2) ◽  
pp. e0190245 ◽  
Author(s):  
Nibedita Patel ◽  
Koteswara Rao Garikapati ◽  
Venkata Krishna Kanth Makani ◽  
Ayikkara Drishya Nair ◽  
Namratha Vangara ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Chemotherapeutic Drug ◽  
Mesenchymal To Epithelial Transition ◽  
Bmi1 Expression

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...

Genetically Modified Her2-Specific T Cells Recognize Low and High Her2 Expressing Breast Cancer Cells.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 5540-5540
Author(s):  
Nabil M. Ahmed ◽  
Maheshika Ratnayake ◽  
Martin Pule ◽  
Cliona M. Rooney ◽  
Helen E. Heslop ◽  
...  
Keyword(s):  
Breast Cancer ◽  
T Cells ◽  
T Cell ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Cancer Cell Lines ◽  
Breast Cancer Cell Lines ◽  
Ifn Γ ◽  
Low Levels

Abstract Background: Only patients with breast cancers expressing high levels of Her2 benefit from anti-Her2 monoclonal antibodies such as trastuzumab. In the present study we investigated in vitro, if chimeric T-cell receptors (TCR), which combine the antigen-specificity of monoclonal antibodies with the effector function of T cells can overcome this limitation. Material and Methods: T cells from healthy donors were transduced with retroviral vectors containing the Her2-specific chimeric TCR gene with a CD28-zeta signaling domain (Her2-CD28-zeta). The specificity of the genetically modified T cells was determined by their ability 1) to kill breast cancer cell lines in cytoxicity assays, and 2) to proliferate and secrete cytokines (IFN-γ and IL-2) after stimulation with breast cancer cell lines. The following panel of cell lines was used: autologous PHA blasts, MDA-MB-468 (both Her2-negative), MCF-7 (Her2-low), Her218 and SKBR-3 (both Her2-high). Results: Her2-CD28-zeta expressing T cells killed low and high Her2-positive breast cancer cell lines in cytotoxicity assays, where as Her2-negative T cells were not killed. Stimulation of T cells with breast cancer cell lines expressing both high and low levels of Her2 resulted in T-cell proliferation and secretion of IFN-γ and IL-2 in a HER2 dependent manner. Discussion: We demonstrate that breast cancer cells with low levels of expression of Her2 can effectively activate T cells expressing Her2-specific chimeric T cell receptor, induce T-cell proliferation, and the production of IL-2, an important T-cell growth factor. These results indicate that T cells expressing chimeric TCRs could possibly extend the application of Her2 targeted therapies to malignancies expressing low levels of Her2.

Sign in / Sign up

Export Citation Format

Share Document