Live-single-cell phenotypic cancer biomarkers-future role in precision oncology?

Sant, Grannum R.; Knopf, Kevin; Albala, David

doi:10.1038/s41698-017-0025-y

Cited by 26 publications

(23 citation statements)

References 88 publications

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“…Measures of cortical mechanical properties could serve as a minimally invasive technique to assess oocyte developmental potential for assisted reproductive technology, as already explored for tumors (for reviews, see refs. 53,54 ).…”

Section: Discussionmentioning

confidence: 99%

Artificially decreasing cortical tension generates aneuploidy in mouse oocytes

et al. 2020

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Human and mouse oocytes' developmental potential can be predicted by their mechanical properties. Their development into blastocysts requires a specific stiffness window. In this study, we combine live-cell and computational imaging, laser ablation, and biophysical measurements to investigate how deregulation of cortex tension in the oocyte contributes to early developmental failure. We focus on extra-soft cells, the most common defect in a natural population. Using two independent tools to artificially decrease cortical tension, we show that chromosome alignment is impaired in extra-soft mouse oocytes, despite normal spindle morphogenesis and dynamics, inducing aneuploidy. The main cause is a cytoplasmic increase in myosin-II activity that could sterically hinder chromosome capture. We describe here an original mode of generation of aneuploidies that could be very common in oocytes and could contribute to the high aneuploidy rate observed during female meiosis, a leading cause of infertility and congenital disorders.

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Section: Discussionmentioning

confidence: 99%

Artificially decreasing cortical tension generates aneuploidy in mouse oocytes

et al. 2020

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“…Such heterogeneity, especially in the absence of specific molecular biomarkers, makes risk stratification, diagnosis, and selection of treatment regimens less accurate. 60 Rapid morphological classification of biopsied cancer cell populations in slide sections using DHM could provide a first-order evaluation of cancer heterogeneity, especially if morphology were linked to clinical behavior. Interestingly, intravital microscopy suggests that cancer cells undergo EM transition to enable metastasis but can rapidly revert to epithelial phenotypes once in the metastatic site, a process termed EM plasticity.…”

Section: Discussionmentioning

confidence: 99%

Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging

et al. 2020

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Significance: We introduce an application of machine learning trained on optical phase features of epithelial and mesenchymal cells to grade cancer cells' morphologies, relevant to evaluation of cancer phenotype in screening assays and clinical biopsies. Aim: Our objective was to determine quantitative epithelial and mesenchymal qualities of breast cancer cells through an unbiased, generalizable, and linear score covering the range of observed morphologies. Approach: Digital holographic microscopy was used to generate phase height maps of noncancerous epithelial (Gie-No3B11) and fibroblast (human gingival) cell lines, as well as MDA-MB-231 and MCF-7 breast cancer cell lines. Several machine learning algorithms were evaluated as binary classifiers of the noncancerous cells that graded the cancer cells by transfer learning. Results: Epithelial and mesenchymal cells were classified with 96% to 100% accuracy. Breast cancer cells had scores in between the noncancer scores, indicating both epithelial and mesenchymal morphological qualities. The MCF-7 cells skewed toward epithelial scores, while MDA-MB-231 cells skewed toward mesenchymal scores. Linear support vector machines (SVMs) produced the most distinct score distributions for each cell line. Conclusions: The proposed epithelial-mesenchymal score, derived from linear SVM learning, is a sensitive and quantitative approach for detecting epithelial and mesenchymal characteristics of unknown cells based on well-characterized cell lines. We establish a framework for rapid and accurate morphological evaluation of single cells and subtle phenotypic shifts in imaged cell populations.

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“…[1a,3] Different cellular subpopulations may exist in individual tumors and some of them have shown to be responsible for initiating cancer metastasis and accelerating cancer development. [3b,4] Additionally, during cancer progression and medication, the phenotypic status of CTCs could dynamically evolve . Therefore, it is of great importance to monitor the phenotypic distribution of CTCs at the resolution of individual cells rather than acquiring an average information, which could be more adaptable to predict disease progression and therapeutic efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, relying on only single biomarker to diagnose and assess cancer is not sufficiently specific due to the complex nature of cancer and its heterogeneity. Identifying one biomarker may achieve to distinguish cancerous cells from healthy samples but it is inadequate for differentiating the subtle variations among subtypes in one given cancer . To address this challenge, it is expected to develop a convenient technique which simultaneously measures a panel of phenotypic biomarkers for a comprehensive phenotype profile and more accurate subtype identification.…”

Section: Introductionmentioning

confidence: 99%

Combining Multiplex SERS Nanovectors and Multivariate Analysis for In Situ Profiling of Circulating Tumor Cell Phenotype Using a Microfluidic Chip

Zhang

Wang

et al. 2018

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Isolating and in situ profiling the heterogeneous molecular phenotype of circulating tumor cells are of great significance for clinical cancer diagnosis and personalized therapy. Herein, an on-chip strategy is proposed that combines size-based microfluidic cell isolation with multiple spectrally orthogonal surface-enhanced Raman spectroscopy (SERS) analysis for in situ profiling of cell membrane proteins and identification of cancer subpopulations. With the developed microfluidic chip, tumor cells are sieved from blood on the basis of size discrepancy. To enable multiplex phenotypic analysis, three kinds of spectrally orthogonal SERS aptamer nanovectors are designed, providing individual cells with composite spectral signatures in accordance with surface protein expression. Next, to statistically demultiplex the complex SERS signature and profile the cellular proteomic phenotype, a revised classic least square algorithm is employed to obtain the 3D phenotypic information at single-cell resolution. Combined with categorization algorithm partial least square discriminate analysis, cells from different human breast cancer subtypes can be reliably classified with high sensitivity and selectivity. The results demonstrate that this platform can identify cancer subtypes with the spectral information correlated to the clinically relevant surface receptors, which holds great potential for clinical cancer diagnosis and precision medicine.

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Live-single-cell phenotypic cancer biomarkers-future role in precision oncology?

Cited by 26 publications

References 88 publications

Artificially decreasing cortical tension generates aneuploidy in mouse oocytes

Artificially decreasing cortical tension generates aneuploidy in mouse oocytes

Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging

Combining Multiplex SERS Nanovectors and Multivariate Analysis for In Situ Profiling of Circulating Tumor Cell Phenotype Using a Microfluidic Chip

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