Dataset Information

Integrative Analysis and Machine Learning based Characterization of Single Circulating Tumor Cells.

ABSTRACT: We collated publicly available single-cell expression profiles of circulating tumor cells (CTCs) and showed that CTCs across cancers lie on a near-perfect continuum of epithelial to mesenchymal (EMT) transition. Integrative analysis of CTC transcriptomes also highlighted the inverse gene expression pattern between PD-L1 and MHC, which is implicated in cancer immunotherapy. We used the CTCs expression profiles in tandem with publicly available peripheral blood mononuclear cell (PBMC) transcriptomes to train a classifier that accurately recognizes CTCs of diverse phenotype. Further, we used this classifier to validate circulating breast tumor cells captured using a newly developed microfluidic system for label-free enrichment of CTCs.

SUBMITTER: Iyer A

PROVIDER: S-EPMC7230872 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Integrative Analysis and Machine Learning based Characterization of Single Circulating Tumor Cells.

Iyer Arvind A Gupta Krishan K Sharma Shreya S Hari Kishore K Lee Yi Fang YF Ramalingam Neevan N Yap Yoon Sim YS West Jay J Bhagat Ali Asgar AA Subramani Balaram Vishnu BV Sabuwala Burhanuddin B Tan Tuan Zea TZ Thiery Jean Paul JP Jolly Mohit Kumar MK Ramalingam Naveen N Sengupta Debarka D

Journal of clinical medicine 20200422 4

We collated publicly available single-cell expression profiles of circulating tumor cells (CTCs) and showed that CTCs across cancers lie on a near-perfect continuum of epithelial to mesenchymal (EMT) transition. Integrative analysis of CTC transcriptomes also highlighted the inverse gene expression pattern between PD-L1 and MHC, which is implicated in cancer immunotherapy. We used the CTCs expression profiles in tandem with publicly available peripheral blood mononuclear cell (PBMC) transcriptom ...[more]

PMID: 32331451

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Project description:Understanding behavioral systems as emergent systems comprising the environment and organism subsystems, include spatial dynamics as a primary dimension in natural settings. Nevertheless, under the standard approaches, the experimental analysis of behavior is based on the single response paradigm and the temporal distribution of discrete responses. Thus, the continuous analysis of spatial behavioral dynamics is a scarcely studied field. The technological advancements in computer vision have opened new methodological perspectives for the continuous sensing of spatial behavior. With the application of such advancements, recent studies suggest that there are multiple features embedded in the spatial dynamics of behavior, such as entropy, and that they are affected by programmed stimuli (e.g., schedules of reinforcement) at least as much as features related to discrete responses. Despite the progress, the characterization of behavioral systems is still segmented, and integrated data analysis and representations between discrete responses and continuous spatial behavior are exiguous in the experimental analysis of behavior. Machine learning advancements, such as t-distributed stochastic neighbor embedding and variable ranking, provide invaluable tools to crystallize an integrated approach for analyzing and representing multidimensional behavioral data. Under this rationale, the present work (1) proposes a multidisciplinary approach for the integrative and multilevel analysis of behavioral systems, (2) provides sensitive behavioral measures based on spatial dynamics and helpful data representations to study behavioral systems, and (3) reveals behavioral aspects usually ignored under the standard approaches in the experimental analysis of behavior. To exemplify and evaluate our approach, the spatial dynamics embedded in phenomena relevant to behavioral science, namely, water-seeking behavior and motivational operations, are examined, showing aspects of behavioral systems hidden until now.

Project description:Circulating tumor cells (CTCs), an advantageous target of liquid biopsy, is an important biomarker for the prognosis and monitoring of cancer. Currently, detection techniques for CTCs are mainly based on the physical and/or epithelial characteristics of tumor cells. However, biofunctional activity markers that can indicate the high metastatic capacity of CTCs are lacking.Functional microarray, quantitative real-time polymerase chain reaction, and Western blot were used on five prostate cancer cell lines with different metastatic capacities to identify the metastasis-related metabolic genes. The identified genes were detected in the CTCs of 64 clinical samples using the RNA in situ hybridization. A multi-criteria weighted model was used to determine the optimal metabolic markers for the CTCs test. Based on five fluorescent signals targeting DAPI, CD45, metabolic, epithelial (EpCAM/CKs), and mesenchymal (Vimentin/Twist) markers, the filtration-enriched CTCs were classified as GM+CTCs/GM-CTCs (metabolic types) or E-CTCs/H-CTCs/M-CTCs (EMT types). Correlation analysis and ROC curve were conducted on 54 prostate cancer samples to evaluate the clinical significance of CTCs subtypes.Eight metastasis-related metabolic genes were identified, including HK2, PDP2, G6PD, PGK1, PHKA1, PYGL, PDK1, and PKM2. Among them, PGK1 and G6PD were determined as optimal glucose metabolic (GM) markers for CTCs. GM+CTCs (marked by PGK1/G6PD) were detectable in 64.8% (35/54) of prostate cancer patients, accounting for 46.5% (134/288) of total CTCs. An increased GM+CTCs level was associated with advanced tumor stage and metastasis (P?<? 0.05). In the discrimination of cancer metastasis from non-metastasis, GM+CTCs presented a higher AUC of the ROC curve (0.780) compared with the EMT CTCs subtypes (E-CTCs 0.729, H-CTCs 0.741, and M-CTCs 0.648). A triple tPSA-Gleason-GM+CTCs marker increased the AUC to 0.904, which was better than that of the tPSA-Gleason-H-CTCs marker (0.874).The metabolic marker (PGK1/G6PD) is determined as the indicator for the biofunctional activity analysis of CTCs, compared with the existing morphological (EMT) classification on CTCs. The metabolic characterization of CTCs demonstrates that hypermetabolic GM+CTCs are promising biomarkers for prostate cancer metastasis.

Dataset Information

Integrative Analysis and Machine Learning based Characterization of Single Circulating Tumor Cells.

Publications

Integrative Analysis and Machine Learning based Characterization of Single Circulating Tumor Cells.

Similar Datasets

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