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Cancers
Special Issues
Flow Cytometric Analysis in Cancer
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Flow Cytometric Analysis in Cancer

A special issue of Cancers (ISSN 2072-6694).

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 18487

Special Issue Editors

Dr. David R. Kaplan

E-Mail Website
Guest Editor
Department of Pathology, Case Western Reserve University, 2854 Sedgewick Road, Shaker Heights, OH, USA
Interests: flow cytometry; signal amplification; cell-specific molecular expression levels; bipolar disorder; major depressive disorder; PTSD; multiple sclerosis; acute myocardial infarction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ulrich Sack

E-Mail Website
Guest Editor
Institute of Clinical Immunology, Medical Faculty, University of Leipzig, 04103 Leipzig, Germany
Interests: immune oncology; circulating tumour cells; immune function; antibody therapy; flow cytometry; clinical immunology; accreditation; lymphocytes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Flow cytometry is a powerful technology because it assesses cells or particles one at a time as they pass through the beams of lasers.  Since clinical samples comprise cells of heterogeneous origins and capabilities, single cell analysis is advantageous. It allows for signals to be assigned to specific cellular subpopulations, thereby greatly enhancing the specificity and precision of the analysis. 

The investigation of samples from patients with cancer has profited by flow cytometry in two specific ways. First, flow cytometry has been used as a tool for the analysis of the cancer cells. Most obviously, flow cytometry has been used to phenotype hematopoietic malignancies such as leukemias, lymphomas, and myelomas since they exist a priori as single cells, but more recently the analysis of exosomes and circulating tumor cells has demonstrated the value of the technology for solid tumors.  Remote sensing of solid tumors by flow cytometric analysis of blood is a promising capability.

The second facet of flow cytometry in cancer involves the analysis of immunity including both the immune response to neoplastic cells and the effects of neoplastic cells on immune cells. Both adaptive immunity with the specific antigen receptors of T cells and B cells and innate immunity with pattern recognition receptors including monocytes and NK cells are pertinent to the response to neoplasia. Beyond signaling receptors for cancer, immunoregulatory mechanisms are crucial in responding to neoplastic cells. The importance of the PD1, TIGIT, and CD47 pathways have been demonstrated to be crucial in responding to cancer cells, and the capability of neoplasia to manipulate these checkpoints has been a major advance over the past few years.

In this Special Issue authors will describe how flow cytometric analysis has impacted studies of cancer. 

Dr. David R. Kaplan
Prof. Dr. Ulrich Sack
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (4 papers)

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Research

19 pages, 3446 KiB  
Article
A Highly Sensitive Flow Cytometric Approach to Detect Rare Antigen-Specific T Cells: Development and Comparison to Standard Monitoring Tools
by Meytal Dror Levinsky, Baruch Brenner, Michal Yalon, Zohar Levi, Zvi Livneh, Zoya Cohen, Tamar Paz-Elizur, Rachel Grossman, Zvi Ram and Ilan Volovitz
Cancers 2023, 15(3), 574; https://doi.org/10.3390/cancers15030574 - 17 Jan 2023
Viewed by 3449
Abstract
Personalized vaccines against patient-unique tumor-associated antigens represent a promising new approach for cancer immunotherapy. Vaccine efficacy is assessed by quantification of changes in the frequency and/or the activity of antigen-specific T cells. Enzyme-linked immunosorbent spot (ELISpot) and flow cytometry (FCM) are methodologies frequently [...] Read more.
Personalized vaccines against patient-unique tumor-associated antigens represent a promising new approach for cancer immunotherapy. Vaccine efficacy is assessed by quantification of changes in the frequency and/or the activity of antigen-specific T cells. Enzyme-linked immunosorbent spot (ELISpot) and flow cytometry (FCM) are methodologies frequently used for assessing vaccine efficacy. We tested these methodologies and found that both ELISpot and standard FCM [monitoring CD3/CD4/CD8/IFNγ/Viability+CD14+CD19 (dump)] demonstrate background IFNγ secretion, which, in many cases, was higher than the antigen-specific signal measured by the respective methodology (frequently ranging around 0.05–0.2%). To detect such weak T-cell responses, we developed an FCM panel that included two early activation markers, 4-1BB (CD137) and CD40L (CD154), in addition to the above-cited markers. These two activation markers have a close to zero background expression and are rapidly upregulated following antigen-specific activation. They enabled the quantification of rare T cells responding to antigens within the assay well. Background IFNγ-positive CD4 T cell frequencies decreased to 0.019% ± 0.028% and CD8 T cells to 0.009% ± 0.013%, which are 19 and 13 times lower, respectively, than without the use of these markers. The presented methodology enables highly sensitive monitoring of T-cell responses to tumor-associated antigens in the very low, but clinically relevant, frequencies. Full article
(This article belongs to the Special Issue Flow Cytometric Analysis in Cancer)
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17 pages, 4379 KiB  
Article
Identification of Immune Cell Components in Breast Tissues by a Multiparametric Flow Cytometry Approach
by Luigi Coppola, Giovanni Smaldone, Massimiliano D’aiuto, Giuseppe D’aiuto, Gennaro Mossetti, Massimo Rinaldo, Simona Verticilo, Emanuele Nicolai, Marco Salvatore and Peppino Mirabelli
Cancers 2022, 14(16), 3869; https://doi.org/10.3390/cancers14163869 - 10 Aug 2022
Cited by 5 | Viewed by 3060
Abstract
Immune cell components are able to infiltrate tumor tissues, and different reports described the presence of infiltrating immune cells (TILs) in several types of solid tumors, including breast cancer. The primary immune cell component cells are reported as a lymphocyte population mainly comprising [...] Read more.
Immune cell components are able to infiltrate tumor tissues, and different reports described the presence of infiltrating immune cells (TILs) in several types of solid tumors, including breast cancer. The primary immune cell component cells are reported as a lymphocyte population mainly comprising the cytotoxic (CD8+) T cells, with varying proportions of helper (CD4+) T cells and CD19+ B cells, and rarely NK cells. In clinical practice, an expert pathologist commonly detects TILs areas in hematoxylin and eosin (H&E)-stained histological slides via light microscopy. Moreover, other more in-depth approaches could be used to better define the immunological component associated with tumor tissues. Using a multiparametric flow cytometry approach, we have studied the immune cells obtained from breast tumor tissues compared to benign breast pathologies. A detailed evaluation of immune cell components was performed on 15 and 14 biopsies obtained from breast cancer and fibroadenoma subjects, respectively. The percentage of tumor-infiltrating T lymphocytes was significantly higher in breast cancer patients compared to patients with fibroadenoma. Infiltrating helper T lymphocytes were increased in the case of malignant breast lesions, while cytotoxic T lymphocytes disclosed an opposite trend. In addition, our data suggest that the synergistic effect of the presence/activation of NK cells and NKT cells, in line with the data in the literature, determines the dampening of the immune response. Moreover, the lymphocyte-to-monocyte ratio was calculated and was completely altered in patients with breast cancer. Our approach could be a potent prognostic factor to be used in diagnostic/therapeutic purposes for the improvement of breast cancer patients’ management. Full article
(This article belongs to the Special Issue Flow Cytometric Analysis in Cancer)
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14 pages, 2060 KiB  
Article
Artificial Intelligence Enhances Diagnostic Flow Cytometry Workflow in the Detection of Minimal Residual Disease of Chronic Lymphocytic Leukemia
by Mohamed E. Salama, Gregory E. Otteson, Jon J. Camp, Jansen N. Seheult, Dragan Jevremovic, David R. Holmes III, Horatiu Olteanu and Min Shi
Cancers 2022, 14(10), 2537; https://doi.org/10.3390/cancers14102537 - 21 May 2022
Cited by 28 | Viewed by 3710
Abstract
Flow cytometric (FC) immunophenotyping is critical but time-consuming in diagnosing minimal residual disease (MRD). We evaluated whether human-in-the-loop artificial intelligence (AI) could improve the efficiency of clinical laboratories in detecting MRD in chronic lymphocytic leukemia (CLL). We developed deep neural networks (DNN) that [...] Read more.
Flow cytometric (FC) immunophenotyping is critical but time-consuming in diagnosing minimal residual disease (MRD). We evaluated whether human-in-the-loop artificial intelligence (AI) could improve the efficiency of clinical laboratories in detecting MRD in chronic lymphocytic leukemia (CLL). We developed deep neural networks (DNN) that were trained on a 10-color CLL MRD panel from treated CLL patients, including DNN trained on the full cohort of 202 patients (F-DNN) and DNN trained on 138 patients with low-event cases (MRD < 1000 events) (L-DNN). A hybrid DNN approach was utilized, with F-DNN and L-DNN applied sequentially to cases. “Ground truth” classification of CLL MRD was confirmed by expert analysis. The hybrid DNN approach demonstrated an overall accuracy of 97.1% (95% CI: 84.7–99.9%) in an independent cohort of 34 unknown samples. When CLL cells were reported as a percentage of total white blood cells, there was excellent correlation between the DNN and expert analysis [r > 0.999; Passing–Bablok slope = 0.997 (95% CI: 0.988–0.999) and intercept = 0.001 (95% CI: 0.000–0.001)]. Gating time was dramatically reduced to 12 s/case by DNN from 15 min/case by the manual process. The proposed DNN demonstrated high accuracy in CLL MRD detection and significantly improved workflow efficiency. Additional clinical validation is needed before it can be fully integrated into the existing clinical laboratory practice. Full article
(This article belongs to the Special Issue Flow Cytometric Analysis in Cancer)
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18 pages, 4619 KiB  
Article
Advanced Immune Cell Profiling by Multiparameter Flow Cytometry in Humanized Patient-Derived Tumor Mice
by Christina Bruss, Kerstin Kellner, Olaf Ortmann, Stephan Seitz, Gero Brockhoff, James A. Hutchinson and Anja Kathrin Wege
Cancers 2022, 14(9), 2214; https://doi.org/10.3390/cancers14092214 - 28 Apr 2022
Cited by 6 | Viewed by 6689
Abstract
“Humanized” mice have been widely used for the characterization of human cancer progression and as a powerful preclinical model. Standardization of multicolor phenotyping could help to identify immune cell patterns involved in checkpoint-related complications. Therefore, we applied established protocols for immune cell profiling [...] Read more.
“Humanized” mice have been widely used for the characterization of human cancer progression and as a powerful preclinical model. Standardization of multicolor phenotyping could help to identify immune cell patterns involved in checkpoint-related complications. Therefore, we applied established protocols for immune cell profiling to our humanized Patient-Derived Xenograft (hPDX) model. hPDX are characterized by the co-existence of a human immune system and a patient-derived tumor transplant. These mice possess a human-like immune system after CD34+ stem cell transplantation while the reconstitution level of the immune system was not related to the quantity of transplanted CD34+ cells. Contamination ≤ 1.2% by CD3+ cells in the hematopoietic stem cell (HSC) transplant did not trigger abnormal T cell maturation. Different B and T cell differentiation stages were identified, as well as regulatory T cells (Tregs) and exhausted T cells that expressed TIGIT, PD-1, or KLRG1. Overall, the application of standardized protocols for the characterization of immune cells using flow cytometry will contribute to a better understanding of immune-oncologic processes. Full article
(This article belongs to the Special Issue Flow Cytometric Analysis in Cancer)
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