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Cells
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Translational Research on Solid Tumors
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Translational Research on Solid Tumors

A special issue of Cells (ISSN 2073-4409). This special issue belongs to the section "Cellular Pathology".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 15112

Special Issue Editors

Dr. Paola Ulivi

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Guest Editor
Biosciences Laboratory, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, via P. Maroncelli 40, 47014 Meldola, Italy
Interests: translational research; biomarkers; liquid biopsy; oncology
Special Issues, Collections and Topics in MDPI journals
Dr. Milena Urbini

E-Mail Website
Guest Editor
Biosciences Laboratory, IRCCS Istituto Romagnolo per lo Studio dei Tumori “Dino Amadori” (IRST) , Via P. Maroncelli 40, 47014 Meldola, Italy
Interests: molecular biology; oncology; translational research; next-generation sequencing
Special Issues, Collections and Topics in MDPI journals
Dr. Giorgia Marisi

E-Mail Website
Guest Editor
Biosciences Laboratory, IRCCS Istituto Romagnolo per lo Studio dei Tumori “Dino Amadori” (IRST), via P. Maroncelli 40, 47014 Meldola, Italy
Interests: molecular biology; oncology; translational research; liquid biopsy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

In recent years, the treatment of solid tumors has focused more on the personalization of treatment, gradually orienting itself towards what is defined as precision medicine. Tumor molecular characteristics, tumor microenvironment composition, and other patient’s characteristics heavily affect prognosis and response to therapy. Moreover, for many types of cancers, the tumor molecular characterization represents the starting point for treatment decision making. Moreover, the heterogeneity of the tumor and its evolution during treatment have led to the use of alternative and non-invasive methods for molecular characterization, such as liquid biopsy.

For the current Special Issue, we invite contributions which showcase the latest research in terms of tumor and microenvironment molecular characterization, in relation to prognosis and response to therapy, also focalizing on the use of non-invasive methods for molecular characterization.

This Special Issue will include (but is not limited to) papers on the following topics: 

  • prognostic biomarkers;
  • tumor heterogeneity;
  • resistance mechanisms to therapy;
  • tumor microenvironment;
  • liquid biopsy;
  • epigenetics;
  • targeted therapy;
  • immunotherapy.

Dr. Paola Ulivi
Dr. Milena Urbini
Dr. Giorgia Marisi
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 short 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. Cells 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 2700 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.

Keywords

  • translational research
  • oncology
  • liquid biopsy

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Related Special Issue

Published Papers (6 papers)

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12 pages, 960 KiB  
Article
Prognostic Role of Circulating Tumor Cell Trajectories in Metastatic Colorectal Cancer
by Valentina Magri, Luca Marino, Chiara Nicolazzo, Angela Gradilone, Gianluigi De Renzi, Michela De Meo, Orietta Gandini, Arianna Sabatini, Daniele Santini, Enrico Cortesi and Paola Gazzaniga
Cells 2023, 12(8), 1172; https://doi.org/10.3390/cells12081172 - 16 Apr 2023
Cited by 11 | Viewed by 2487
Abstract
Background: A large amount of evidence from clinical studies has demonstrated that circulating tumor cells are strong predictors of outcomes in many cancers. However, the clinical significance of CTC enumeration in metastatic colorectal cancer is still questioned. The aim of this study was [...] Read more.
Background: A large amount of evidence from clinical studies has demonstrated that circulating tumor cells are strong predictors of outcomes in many cancers. However, the clinical significance of CTC enumeration in metastatic colorectal cancer is still questioned. The aim of this study was to evaluate the clinical value of CTC dynamics in mCRC patients receiving first-line treatments. Materials and methods: Serial CTC data from 218 patients were used to identify CTC trajectory patterns during the course of treatment. CTCs were evaluated at baseline, at a first-time point check and at the radiological progression of the disease. CTC dynamics were correlated with clinical endpoints. Results: Using a cut-off of ≥1 CTC/7.5 mL, four prognostic trajectories were outlined. The best prognosis was obtained for patients with no evidence of CTCs at any timepoints, with a significant difference compared to all other groups. Lower PFS and OS were recognized in group 4 (CTCs always positive) at 7 and 16 months, respectively. Conclusions: We confirmed the clinical value of CTC positivity, even with only one cell detected. CTC trajectories are better prognostic indicators than CTC enumeration at baseline. The reported prognostic groups might help to improve risk stratification, providing potential biomarkers to monitor first-line treatments. Full article
(This article belongs to the Special Issue Translational Research on Solid Tumors)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Kaplan–Maier survival curves (probability) for PFS (top) and OS (bottom). Comparison between the 4 trajectory groups. Group 1 (119 patients) CTC-ND (baseline)/CTC-ND (progression disease). Group 2 (48 patients): CTC-ND (baseline)/CTC+ (progression disease). Group 3 (16 patients): CTC+ (baseline)/CTC-ND (progression disease). Group 4 (35 patients): CTC+ (baseline)/CTC+ (progression disease).</p> Full article ">
18 pages, 5601 KiB  
Article
Molecular Analysis of the Superior Efficacy of a Dual Epidermal Growth Factor Receptor (EGFR)-DNA-Targeting Combi-Molecule in Comparison with Its Putative Prodrugs 6-Mono-Alkylamino- and 6,6-Dialkylaminoquinazoline in a Human Osteosarcoma Xenograft Model
by Caterina Facchin, Ana B. Fraga-Timiraos, Julie Schmitt, Nadia Babaa, Naveet Pannu, Antonio Aliaga, Anne-Laure Larroque and Bertrand J. Jean-Claude
Cells 2023, 12(6), 914; https://doi.org/10.3390/cells12060914 - 16 Mar 2023
Cited by 1 | Viewed by 2064
Abstract
Background: ZR2002 is a dual EGFR-DNA-targeting combi-molecule that carries a chloroethyl group at the six-position of the quinazoline ring designed to alkylate DNA. Despite its good pharmacokinetics, ZR2002 is metabolized in vivo into dechlorinated metabolites, losing the DNA-alkylating function required to damage [...] Read more.
Background: ZR2002 is a dual EGFR-DNA-targeting combi-molecule that carries a chloroethyl group at the six-position of the quinazoline ring designed to alkylate DNA. Despite its good pharmacokinetics, ZR2002 is metabolized in vivo into dechlorinated metabolites, losing the DNA-alkylating function required to damage DNA. To increase the DNA damage activity in tumor cells in vivo, we compared ZR2002 with two of its 6-N,N-disubstituted analogs: “JS61”, with a nitrogen mustard function at the six-position of the quinazoline ring, and “JS84”, with an N-methyl group. Methods: Tumor xenografts were performed with the human Saos-2 osteosarcoma cell line expressing EGFR. Mice were treated with ZR2002, JS84 or JS61, and the tumor burden was measured with a caliper and CT/PET imaging. Drug metabolism was analyzed with LC-MS. EGFR and ɣ-H2AX phosphorylation were quantified via Western blot analysis and immunohistochemistry. Results: In vivo analysis showed that significant tumor growth inhibition was only achieved when ZR2002 was administered in its naked form. The metabolic dealkylation of JS61 and JS84 did not release sufficient concentrations of ZR2002 for the intratumoral inhibition of P-EGFR or enhanced levels of P-H2AX. Conclusions: The results in toto suggest that intratumoral concentrations of intact ZR2002 are correlated with the highest inhibition of P-EGFR and induction of DNA damage in vivo. ZR2002 may well represent a good drug candidate for the treatment of EGFR-expressing osteosarcoma. Full article
(This article belongs to the Special Issue Translational Research on Solid Tumors)
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Figure 1

Figure 1
<p>Proposed in vivo metabolism pathways toward conversion of the combi-molecules JS84 (<b>A</b>) and JS61 (<b>B</b>) to ZR2002.</p> Full article ">Figure 2
<p>Antitumor activities of ZR2002, JS61 and JS84. Quantification of tumor growth measured with caliper in (<b>A</b>) ZR2002-treated group, (<b>B</b>) JS61-treated group and (<b>C</b>) JS84-treated group compared with vehicle group in each graph. (<b>D</b>) Representative coronary sections of CT images for ZR2002, JS84 or vehicle-treated mice (white arrows indicate the tumor) with CT tumor volume quantification on day 22. Data are expressed as means ± SEM. * <span class="html-italic">p</span> &lt; 0.05 between the two groups.</p> Full article ">Figure 3
<p>Tumor volume measurement with FDG-PET on day 22. FDG-PET scan was performed one day prior to the sacrifice of vehicle-, ZR2002- and JS84-treated groups. (<b>A</b>) Representative coronary sections of FDG-PET images for ZR2002-, JS84- or vehicle-treated mice (red arrows indicate the tumor) on day 22. (<b>B</b>) Metabolic volume. (<b>C</b>) Total lesion glycolysis (TLG). Data are expressed as means ± SEM. * <span class="html-italic">p</span> &lt; 0.05 between the two groups.</p> Full article ">Figure 4
<p>Immunohistochemistry and Western blot analysis of the effects of ZR2002, JS84 and JS61 on tumor tissues. ZR2002 decreases the phosphorylation of EGFR (P-EGFR). (<b>A</b>) Representative sections of Saos-2 tumors stained for P-EGFR. (<b>B</b>) Western blots for P-EGFR (Tyr 1068) and EGFR, where β-actin was used as internal control. Each band corresponds to different tumor lysates. (<b>C</b>) Quantification of Western blot: ratio of band intensity between P-EGFR and EGFR total protein. Data are expressed as means ± SEM. * <span class="html-italic">p</span> &lt; 0.05 between the two groups.</p> Full article ">Figure 5
<p>Immunohistochemistry and Western blot analysis of the effects of ZR2002, JS84 and JS61 on tumor tissues. ZR2002 increases the phosphorylation of ɣ-H2AX (P-ɣH2AX). (<b>A</b>) Representative tumor sections of Saos-2 tumors stained for P-ɣH2AX. (<b>B</b>) Western blots for P-ɣH2AX and β-actin, which was used as internal control. (<b>C</b>) Quantification of Western blots: ratio of band intensity between P-ɣH2AX and β-actin. Data are expressed as means ± SEM. * <span class="html-italic">p</span> &lt; 0.05 between the two groups.</p> Full article ">Figure 6
<p>Main metabolites released in the tumors after 3 h from combi-molecule administration. (<b>A</b>) LC-MS chromatogram of the metabolization of ZR2002 (first row), JS61 (middle row) and JS84 (bottom row). Metabolites: (1) RB10, (2) RB10-N-Acetyl, (3) ZR2002, (4) JS61-OH-Cl, (5) JS61-OH-2Cl, (6) RB10-N-Acetyl-Methyl, (7) RB10-Methyl and (8) JS84-OH-Cl. The peak of JS61 intact drug is in green, while JS84’s is in blue, and the peak of ZR2002 is in red. (<b>B</b>) Quantification of ZR2002 released in the treated tumors (N = 4). Data are expressed as means ± SEM. * <span class="html-italic">p</span> &lt; 0.05 between the two groups.</p> Full article ">Figure 7
<p>Scheme of combi-molecule metabolizations. All combi-molecules share the metabolic pathway of ZR2002, marked with the red rectangle. JS61 partially hydrolyzes, generating metabolites 4 and 5, of which concentrations are not sufficient to generate high concentrations of ZR2002 metabolite. JS84 generates high concentrations of RB10-Methyl and is not hydrolyzed enough in JS84-OH-Cl to generate high levels of ZR2002. Each metabolite has a name and number that refers to the chromatogram in <a href="#cells-12-00914-f006" class="html-fig">Figure 6</a>A.</p> Full article ">Figure A1
<p>Western blot of EGFR expression in osteosarcoma cell lines. β-actin was used as internal control, and the overexpression of EGFR in the lung cancer cell line, A549, was introduced as positive control.</p> Full article ">Figure A2
<p>Body weights of mice treated (N = 7) with the combi-molecules. (<b>A</b>) ZR2002 versus vehicle. (<b>B</b>) JS61 versus vehicle. (<b>C</b>) JS84 versus vehicle. Data are expressed as means ± SEM.</p> Full article ">Figure A3
<p>Main metabolites released in the plasma after 3 h from combi-molecule administration. (<b>A</b>) LC-MS chromatogram of the metabolization of ZR2002 (first row), JS61 (middle row) and JS84 (bottom row). Metabolites: (1) RB10, (2) RB10-N-Acetyl, (3) ZR2002, (4) JS61-OH-Cl, (5) JS61-OH-2Cl, (6) RB10-N-Acetyl-Methyl, (7) RB10-Methyl and (8) JS84-OH-Cl. The peak of JS61 intact drug is in green, while JS84’s is in blue, and ZR2002’s is in red. (<b>B</b>) Quantification of ZR2002 released in the treated tumors. Data are expressed as means ± SEM. * <span class="html-italic">p</span> &lt; 0.05 between the two groups.</p> Full article ">
13 pages, 2009 KiB  
Article
Overexpression of KMT9α Is Associated with Aggressive Basal-like Muscle-Invasive Bladder Cancer
by Florestan J. Koll, Eric Metzger, Jana Hamann, Anna Ramos-Triguero, Katrin Bankov, Jens Köllermann, Claudia Döring, Felix K. H. Chun, Roland Schüle, Peter J. Wild and Henning Reis
Cells 2023, 12(4), 589; https://doi.org/10.3390/cells12040589 - 11 Feb 2023
Cited by 6 | Viewed by 2280
Abstract
Muscle-invasive bladder cancer (MIBC) is associated with limited response rates to systemic therapy leading to a significant risk of recurrence and death. A recently discovered histone methyltransferase KMT9, acts as an epigenetic regulator of carcinogenesis in different tumor entities. In this study, we [...] Read more.
Muscle-invasive bladder cancer (MIBC) is associated with limited response rates to systemic therapy leading to a significant risk of recurrence and death. A recently discovered histone methyltransferase KMT9, acts as an epigenetic regulator of carcinogenesis in different tumor entities. In this study, we investigated the presence and association of histological and molecular subtypes and their impact on the survival of KMT9α in MIBC. We performed an immunohistochemical (IHC) analysis of KMT9α in 135 MIBC patients undergoing radical cystectomy. KMT9α was significantly overexpressed in the nucleus in MIBC compared to normal urothelium and low-grade urothelial cancer. Using the HTG transcriptome panel, we assessed mRNA expression profiles to determine molecular subtypes and identify differentially expressed genes. Patients with higher nuclear and nucleolar KMT9α expression showed basal/squamous urothelial cancer characteristics confirmed by IHC and differentially upregulated KRT14 expression. We identified a subset of patients with nucleolar expression of KMT9α, which was associated with an increased risk of death in uni- and multivariate analyses (HR 2.28, 95%CI 1.28–4.03, p = 0.005). In conclusion, basal-like MIBC and the squamous histological subtype are associated with high nuclear KMT9α expression. The association with poor survival makes it a potential target for the treatment of bladder cancer. Full article
(This article belongs to the Special Issue Translational Research on Solid Tumors)
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Figure 1

Figure 1
<p>(<b>A</b>) HE-staining of normal urothelium (1) next to an invasive tumor with squamous histological subtype (2), magnification 200×. (<b>B</b>): IHC for KMT9α. Normal urothelium (1) with a slight cytoplasmatic background but without nuclear expression. The neighboring tumor (2) shows nuclear expression of KMT9α in 10% of tumor cells, magnification 200×. (<b>C</b>): Percentage of cases with low (&lt;5%), intermediate (5–15%) and high (≥15%) nuclear KMT9α expression in “normal” urothelium (<span class="html-italic">n</span> = 8); urothelial pTa low-grade tumors (<span class="html-italic">n</span> = 14); MIBC (<span class="html-italic">n</span> = 135) and urothelial cancer metastases (<span class="html-italic">n</span> = 19). (<b>D</b>) Tumor with strong nuclear and nucleolar expression of KMT9α, magnification 630×.</p> Full article ">Figure 2
<p>Kaplan–Meier curve for overall survival for patients with low and high (≥15% positive tumor cells) nuclear KMT9α expression stratified for patients with cystectomy only (<b>A</b>) and patients receiving adjuvant chemotherapy (<b>B</b>,<b>C</b>) Kaplan–Meier curve for all patients with and without nucleolar KMT9α expression. Nucleolar KMT9α positivity was called when more than 10% of tumor cells showed clear nucleolar KMT9α expression. <span class="html-italic">p</span>-values were calculated using log-rank test. (<b>D</b>) Forrest plot for the multivariate survival analysis adjusting for tumor and lymph node status, application of adjuvant chemotherapy, and nucleolar KMT9α expression. T_Stage = tumor stage; N_Stage = lymph node status; KMT9aNucleoli = Nucleoli positive for KMT9α expression. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001.</p> Full article ">Figure 3
<p>(<b>A</b>) Mean nuclear KMT9α expression was significantly associated with histological bladder cancer subtypes. Bars and numbers indicate the mean nuclear KMT9α expression and the standard deviations. Numbers above the bars indicate <span class="html-italic">p</span>-values (two-sided <span class="html-italic">t</span>-test) between groups. NOS (not otherwise specified), <span class="html-italic">n</span> = 91; squamous, <span class="html-italic">n</span> = 16; micropapillary, <span class="html-italic">n</span> = 10; neuroendocrine, <span class="html-italic">n</span> = 4; “Other” subtypes include glandular (<span class="html-italic">n</span> = 1), plasmacytoid (<span class="html-italic">n</span> = 4), lymphoepithelial (<span class="html-italic">n</span> = 3), nested (<span class="html-italic">n</span> = 1), sarcomatoid (<span class="html-italic">n</span> = 3), and giant cell (<span class="html-italic">n</span> = 1). (<b>B</b>) The nuclear expression of KMT9α (%) was significantly higher in patients with mutated <span class="html-italic">TP53</span> compared to wild type (<span class="html-italic">n</span> = 57). Mutation status was called when ≥50% of cells showed clear nuclear p53 overexpression (<span class="html-italic">n</span> = 50) or p53 null type (<span class="html-italic">n</span> = 27); <span class="html-italic">p</span> &lt; 0.001. Values indicate mean nuclear KMT9α expression and standard deviations. IHC: immunohistochemistry. (<b>C</b>) Correlation between nucleolar KMT expressing tumors and molecular subtype according to the TCGA classification (<span class="html-italic">p</span> = 0.006), <span class="html-italic">n</span> = 85. (<b>D</b>) Volcano plot showing statistical significance (−log<sub>10</sub> <span class="html-italic">p</span>-value) versus log<sub>2</sub> expression fold change of genes comparing high (≥15%) vs. low (&lt;5%) nuclear KMT9α expression. The 10 genes with highest fold change are annotated.</p> Full article ">
13 pages, 2468 KiB  
Article
Identification and Validation of RELN Mutation as a Response Indicator for Immune Checkpoint Inhibitor Therapy in Melanoma and Non-Small Cell Lung Cancer
by Zhenpeng Li, Xin Wang, Yi Yang, Fuyan Shi, Wenjing Zhang, Qinghua Wang and Suzhen Wang
Cells 2022, 11(23), 3841; https://doi.org/10.3390/cells11233841 - 30 Nov 2022
Cited by 5 | Viewed by 2201
Abstract
Remarkable clinical benefits in several advanced cancers are observed under the treatment of immune checkpoint inhibitor (ICI) agents. However, only a smaller proportion of patients respond to the treatments. Reelin (RELN) is frequently mutated in the cancer genome. In this study, the RELN [...] Read more.
Remarkable clinical benefits in several advanced cancers are observed under the treatment of immune checkpoint inhibitor (ICI) agents. However, only a smaller proportion of patients respond to the treatments. Reelin (RELN) is frequently mutated in the cancer genome. In this study, the RELN mutation association with ICI treatment efficacy in melanoma and non-small cell lung cancer (NSCLC) was elucidated. Data from 631 melanoma and 109 NSCLC patients with both ICI treatment data and pre-treatment mutational profiles were collected. In addition, from the Cancer Genome Atlas (TCGA) project, we also obtained both tumors to explore the immunologic features behind RELN mutations. Melanoma patients with RELN mutations exhibited a favorable ICI survival benefit when compared with wild-type patients (HR: 0.66, 95% CI: 0.51–0.87, p = 0.003). A higher response rate was also noticed in RELN-mutated patients (38.9% vs. 28.3%, p = 0.017). The association of RELN mutations with a preferable immunotherapy outcome and response was further confirmed in NSCLC. Further exploration demonstrated that favorable immunocyte infiltration and immune response signaling pathways were found in patients with RELN mutations. In this study, RELN mutations were identified to connect with a better immune microenvironment and an improved ICI efficacy in melanoma and NSCLC, which provides a potential biomarker for immunological feature evaluation and immunotherapeutic outcome prediction at the molecular level. Full article
(This article belongs to the Special Issue Translational Research on Solid Tumors)
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Figure 1

Figure 1
<p>The detailed workflow operating in this work to explore the clinical ICI treatment implications of <span class="html-italic">RELN</span> mutations based on the genomic data and immunotherapy information.</p> Full article ">Figure 2
<p>ICI treatment prognosis and response rate analyses of <span class="html-italic">RELN</span> mutations in melanoma. (<b>A</b>) Survival curves of <span class="html-italic">RELN</span>-mutated and wild-type patients. (<b>B</b>) Multivariable Cox regression analysis of <span class="html-italic">RELN</span> mutations was performed with clinical confounders taken into consideration. (<b>C</b>) Bar plot representation of ICI response rates of <span class="html-italic">RELN</span>-mutated and wild-type patients. (<b>D</b>) Multivariable logistic regression analysis of <span class="html-italic">RELN</span> mutations was achieved.</p> Full article ">Figure 3
<p>ICI treatment prognosis and response rate analyses of <span class="html-italic">RELN</span> mutations in NSCLC. (<b>A</b>) Survival curves of <span class="html-italic">RELN</span>-mutated and wild-type NSCLC patients. (<b>B</b>) Multivariable Cox regression analysis of <span class="html-italic">RELN</span> mutations was performed with clinical confounders taken into consideration. (<b>C</b>) Bar plot representation of ICI response rates of <span class="html-italic">RELN</span>-mutated and wild-type patients. (<b>D</b>) Multivariable logistic regression analysis of <span class="html-italic">RELN</span> mutations was achieved with multiple confounding variables adjusted.</p> Full article ">Figure 4
<p>Association of <span class="html-italic">RELN</span> mutations with TMB in melanoma and NSCLC. (<b>A</b>) Univariate analysis between <span class="html-italic">RELN</span> mutations and TMB in melanoma. (<b>B</b>) Multivariable logistic analysis of <span class="html-italic">RELN</span> mutations was achieved with multiple confounding factors adjusted. (<b>C</b>) Univariate analysis between <span class="html-italic">RELN</span> mutations and TMB in NSCLC. (<b>D</b>) Multivariable logistic analysis of <span class="html-italic">RELN</span> mutations was achieved with multiple confounding factors controlled to acquire a real association.</p> Full article ">Figure 5
<p>Immune infiltration and signaling pathways behind <span class="html-italic">RELN</span> mutations in melanoma. (<b>A</b>) CIBERSORT method revealed the distinct immunocyte infiltration in <span class="html-italic">RELN</span> two subgroups. (<b>B</b>) Distinct enrichment scores of IFNγ signature in <span class="html-italic">RELN</span> two subgroups. Immunogenicity-related signaling pathways of (<b>C</b>) interferon γ response, (<b>D</b>) allograft rejection, and (<b>E</b>) interferon α response were enriched in patients with <span class="html-italic">RELN</span> mutations. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p> Full article ">
20 pages, 16281 KiB  
Article
Quantitative Phase Imaging Detecting the Hypoxia-Induced Patterns in Healthy and Neoplastic Human Colonic Epithelial Cells
by Igor Buzalewicz, Monika Mrozowska, Alicja Kmiecik, Michał Kulus, Katarzyna Haczkiewicz-Leśniak, Piotr Dzięgiel, Marzenna Podhorska-Okołów and Łukasz Zadka
Cells 2022, 11(22), 3599; https://doi.org/10.3390/cells11223599 - 14 Nov 2022
Cited by 4 | Viewed by 2720
Abstract
Hypoxia is a frequent phenomenon during carcinogenesis and may lead to functional and structural changes in proliferating cancer cells. Colorectal cancer (CRC) is one of the most common neoplasms in which hypoxia is associated with progression. The aim of this study was to [...] Read more.
Hypoxia is a frequent phenomenon during carcinogenesis and may lead to functional and structural changes in proliferating cancer cells. Colorectal cancer (CRC) is one of the most common neoplasms in which hypoxia is associated with progression. The aim of this study was to assess the optical parameters and microanatomy of CRC and the normal intestinal epithelium cells using the digital holotomography (DHT) method. The examination was conducted on cancer (HT-29, LoVo) and normal colonic cells (CCD-18Co) cultured in normoxic and hypoxic environments. The assessment included optical parameters such as the refractive index (RI) and dry mass as well as the morphological features. Hypoxia decreased the RI in all cells as well as in their cytoplasm, nucleus, and nucleoli. The opposite tendency was noted for spheroid-vesicular structures, where the RI was higher for the hypoxic state. The total volume of hypoxic CCD-18Co and LoVo cells was decreased, while an increase in this parameter was observed for HT-29 cells. Hypoxia increased the radius and cell volume, including the dry mass of the vesicular content. The changes in the optics and morphology of hypoxic cells may suggest the possibility of using DHT in the detection of circulating tumor cells (CTCs). Full article
(This article belongs to the Special Issue Translational Research on Solid Tumors)
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Figure 1

Figure 1
<p>The immunofluorescence labeling against nuclei (blue DAPI), HIF1α (red), and β-actin (green) in normoxic HT-29 cells (<b>A</b>–<b>D</b>) showed no HIF1α-positive cells (<b>B</b>,<b>D</b>). All cells were positive on DAPI (<b>A</b>–<b>D</b>) and β-actin (<b>C</b>,<b>D</b>); scale bars = 20 μm.</p> Full article ">Figure 2
<p>The immunofluorescence labeling against HIF1α (red) showed positive nuclear expression in hypoxic HT-29 cells (<b>B</b>,<b>D</b>). All hypoxic cells were positive on β-actin (green, (<b>C</b>,<b>D</b>)) and DAPI (blue nuclei, (<b>A</b>–<b>D</b>)); scale bars = 20 μm.</p> Full article ">Figure 3
<p>The schema of the DHT system (BS—beam splitters, M—mirror, RM—rotating mirror, P—prism, MO—microscopic objective) and the proposed RI data analysis algorithm.</p> Full article ">Figure 4
<p>A comparison of the representative 2D-RI tomograms and 3D visualizations (scale bars: 20 µm) of CCD-18Co (<b>A</b>), HT-29 (<b>B</b>), and LoVo (<b>C</b>) cells under normoxic and hypoxic conditions. The variation of single-cell total volume (<b>D</b>) and average RI value of cells (<b>E</b>) was also considered.</p> Full article ">Figure 5
<p>(<b>A</b>) The digitally stained 3D visualizations of CCD-18Co (1), HT-29 (2), and LoVo (3) cells under normoxic and hypoxic conditions (scale bars: 20 µm). (<b>B</b>) The boxplots representing the variation of the average RI values of cytoplasm (1), nucleus and nucleoli (2), and vesicular content (3) and their digitally stained representative visualizations. (<b>C</b>) The representative 2D-RI tomograms indicating the location of vesicles/lipid droplet-like structures inside the single-cells.</p> Full article ">Figure 6
<p>(<b>A</b>) The variation of the average volume of the vesicles/lipid droplet-like structures of examined cells in both normoxic and hypoxic conditions. (<b>B</b>) A comparison of the dry mass of detected structures in analyzed samples under normoxic and hypoxic conditions. (<b>C</b>) The determined distribution of the radius of these structures in analyzed cultures: CCD-18Co (1), HT-29 (2), LoVo (3) cells under normoxic and hypoxic environments with fitted probability density functions (pdf). (<b>D</b>) The comparison of the variation of the total volume of the high RI fraction (RI &gt; 1.375) structures in cancer cells (1) and their dry mass (2).</p> Full article ">Figure 7
<p>A comparison of cell morphology in normoxia (<b>A</b>–<b>C</b>) and hypoxia (<b>D</b>–<b>F</b>) in examined cell lines. No major differences were visible, besides elongated protrusions in hypoxic CCD-18Co cells (<b>D</b>). Regardless of oxygenation, cells tend to exhibit visible exocytic activity; they maintain similar shape and morphology of nuclei.</p> Full article ">Figure 8
<p>Micrographs of representative cells’ cytoplasm in normoxia (<b>A</b>–<b>C</b>) and hypoxia (<b>D</b>–<b>F</b>). (<b>A</b>,<b>D</b>) CCD-18Co cells in lower oxygenation tend to have more autophagic vacuoles (arrows) and multivesicular bodies (MVBs; dot marker), as well as more numerous Golgi apparatuses (short arrows). (<b>B</b>,<b>E</b>) Mitochondria in the LoVo cell line tend to be larger and fused (arrowhead), suggesting the dominance of fusion over the fission process. MVBs and autophagic vacuoles are less numerous than those in the CCD-18Co cell line. (<b>C</b>,<b>F</b>) Hypoxic mitochondria in HT-29 tend to be less electron-dense than the normoxic ones. No significant differences in autophagy or exocytosis were observed (n = 3 cells/each cell line).</p> Full article ">Figure 9
<p>Possibilities of using the digital holotomography (DHT) method in the detection of circulating tumor cells (CTCs).</p> Full article ">

Review

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18 pages, 1878 KiB  
Review
The Promise of Epigenetics Research in the Treatment of Appendiceal Neoplasms
by Luisa Ladel, Wan Ying Tan, Thanushiya Jeyakanthan, Bethsebie Sailo, Anup Sharma and Nita Ahuja
Cells 2023, 12(15), 1962; https://doi.org/10.3390/cells12151962 - 29 Jul 2023
Cited by 4 | Viewed by 2548
Abstract
Appendiceal cancers (AC) are a rare and heterogeneous group of malignancies. Historically, appendiceal neoplasms have been grouped with colorectal cancers (CRC), and treatment strategies have been modeled after CRC management guidelines due to their structural similarities and anatomical proximity. However, the two have [...] Read more.
Appendiceal cancers (AC) are a rare and heterogeneous group of malignancies. Historically, appendiceal neoplasms have been grouped with colorectal cancers (CRC), and treatment strategies have been modeled after CRC management guidelines due to their structural similarities and anatomical proximity. However, the two have marked differences in biological behavior and treatment response, and evidence suggests significant discrepancies in their respective genetic profiles. In addition, while the WHO classification for appendiceal cancers is currently based on traditional histopathological criteria, studies have demonstrated that histomorphology does not correlate with survival or treatment response in AC. Due to their rarity, appendiceal cancers have not been studied as extensively as other gastrointestinal cancers. However, their incidence has been increasing steadily over the past decade, making it crucial to identify new and more effective strategies for detection and treatment. Recent efforts to map and understand the molecular landscape of appendiceal cancers have unearthed a wealth of information that has made it evident that appendiceal cancers possess a unique molecular profile, distinct from other gastrointestinal cancers. This review focuses on the epigenetic landscape of epithelial appendiceal cancers and aims to provide a comprehensive overview of the current state of knowledge of epigenetic changes across different appendiceal cancer subtypes, highlighting the challenges as well as the promise of employing epigenetics in the quest for the detection of biomarkers, therapeutic targets, surveillance markers, and predictors of treatment response and survival in epithelial appendiceal neoplasms. Full article
(This article belongs to the Special Issue Translational Research on Solid Tumors)
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Figure 1

Figure 1
<p>Illustration of 2019 WHO classification of epithelial appendiceal cancers [<a href="#B23-cells-12-01962" class="html-bibr">23</a>]. LAMN: low-grade appendiceal mucinous neoplasm, HAMN: high-grade appendiceal mucinous neoplasm, PP/PYY: pancreatic polypeptide/peptide YY, MiNEN: mixed neuroendocrine–non-neuroendocrine neoplasm. Figure created with <a href="http://biorender.com" target="_blank">biorender.com</a>, accessed on 19 July 2023.</p> Full article ">Figure 2
<p>Machine-learning- and genomics-based clustering of appendiceal mucinous adenocarcinoma and adenocarcinoma samples as described by [<a href="#B35-cells-12-01962" class="html-bibr">35</a>], with subtype-defining mutations listed, including mutational frequencies, and mutations in epigenetic-related genes marked in blue. AC0-4: appendiceal cancer subtype 0–4 (nomenclature adopted from Ref. [<a href="#B35-cells-12-01962" class="html-bibr">35</a>] for this figure). Permission to reproduce granted by Springer Nature (license number 5593061501581). Figure created with <a href="http://biorender.com" target="_blank">biorender.com</a>, accessed on 19 July 2023.</p> Full article ">Figure 3
<p>Epigenetic regulatory gene mutations across appendiceal cancer subtypes extracted from the MSK-IMPACT platform created via cBioportal [<a href="#B49-cells-12-01962" class="html-bibr">49</a>,<a href="#B50-cells-12-01962" class="html-bibr">50</a>]. Appendiceal cancer subtypes: mucinous adenocarcinoma (N = 164), goblet cell adenocarcinoma (N = 72), and colonic-type adenocarcinoma (N = 37).</p> Full article ">Figure 4
<p>Major epigenetic pathways contributing to oncogenesis in appendiceal cancers and potential therapy-related targets. Ac: acetylation, DNMT: DNA methyltransferase, KAc: lysine acetylation, Me: methylation, P: phosphorylation, RTK: receptor tyrosine kinase, TSS: transcription start site, Ub: ubiquitination. Figure created with <a href="http://biorender.com" target="_blank">biorender.com</a>, accessed on 19 July 2023.</p> Full article ">
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