[go: up one dir, main page]

 
 
ijms-logo

Journal Browser

Journal Browser

Exploring Therapeutic Targets in the Evolving Landscape of Cancer Immunotherapy

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Immunology".

Deadline for manuscript submissions: 20 December 2024 | Viewed by 7813

Special Issue Editor


E-Mail Website
Guest Editor
Hematology-Oncology and Stem Cell Transplantation Unit, Department of Hematology and Innovative Diagnostic, Istituto Nazionale Tumori-IRCCS-Fondazione “G. Pascale” Napoli, 80131 Napoli, Italy
Interests: dendritic cells; immunotherapy; gamma-delta T cells; cancer biomarkers; tumor immunology; cytometry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cancer immunotherapy has truly revolutionized oncology treatment, significantly enhancing patient survival rates. Recent strides in this field explore cutting-edge approaches, including personalized vaccines, combination therapies and advancements in CAR-T cell therapy, all aimed at elevating treatment specificity and the overall effectiveness. Researchers are deeply engaged in unraveling the complexities of the tumor microenvironment, with a focus on modulating immunosuppressive factors and augmenting T-cell infiltration for optimal treatment outcomes. These advancements underscore a nuanced understanding of immune systnem dynamics within the context of cancer, paving the way for more sophisticated and impactful therapeutic strategies.

This Special Issue is dedicated to the exploration of cancer immunotherapy, with a specific focus on immune targets, CAR- and TCR-engineered T cell therapy for both blood and solid cancers, as well as malignancies associated with viral infections. It spans dynamic areas of research, including DC-based immunotherapy, Treg cell-mediated immunosuppression and innate immune signaling, offering a collection of innovative ideas aimed at not only enhancing therapeutic efficacy, but also broadening the application of immunotherapy for a diverse range of cancer patients.

Dr. Domenico Galati
Guest Editor

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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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

  • immune checkpoints
  • targeted therapies
  • tumor microenvironment
  • cancer immunology
  • precision medicine
  • cell-based cancer therapy

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

18 pages, 5691 KiB  
Article
Evidence of Neutrophils and Neutrophil Extracellular Traps in Human NMSC with Regard to Clinical Risk Factors, Ulceration and CD8+ T Cell Infiltrate
by Linda-Maria Hildegard Moeller, Carsten Weishaupt and Fiona Schedel
Int. J. Mol. Sci. 2024, 25(19), 10620; https://doi.org/10.3390/ijms251910620 - 2 Oct 2024
Viewed by 426
Abstract
Non-melanoma skin cancers (NMSC), including basal cell carcinoma (BCC), cutaneous squamous cell carcinoma (cSCC), and Merkel cell carcinoma (MCC), are increasingly common and present significant healthcare challenges. Neutrophil extracellular traps (NETs), chromatin fibers expulsed by neutrophil granulocytes, can promote immunotherapy resistance via an [...] Read more.
Non-melanoma skin cancers (NMSC), including basal cell carcinoma (BCC), cutaneous squamous cell carcinoma (cSCC), and Merkel cell carcinoma (MCC), are increasingly common and present significant healthcare challenges. Neutrophil extracellular traps (NETs), chromatin fibers expulsed by neutrophil granulocytes, can promote immunotherapy resistance via an impairment of CD8+ T cell-mediated cytotoxicity. Here, to identify a potential therapeutic target, we investigate the expulsion of NETs and their relation to CD8+ T cell infiltration in NMSC. Immunofluorescence staining for neutrophils (CD15) and NETs (H3cit), as well as immunohistochemistry for cytotoxic T cells (CD8+) on human cSCCs (n = 24), BCCs (n = 17) and MCCs (n = 12), revealed a correlation between neutrophil infiltration and ulceration diameter in BCC and MCC, but not in cSCC. In BCC and cSCC, neutrophil infiltration also correlated with the cross-sectional area (CSA). NETs were not associated with established risk factors but with the presence of an ulceration, and, in cSCC, with abscess-like structures. CD8+ T cell infiltration was not reduced in tumors that were NET-positive nor in those with a denser neutrophil infiltration. This study is the first to report and characterize NETs in NMSC. Thus, it gives an incentive for further research in this relevant yet understudied topic. Full article
Show Figures

Figure 1

Figure 1
<p>(<b>A</b>) Exemplary pictures of different neutrophil scores (N0–N3) assessed by immunofluorescence (IF) staining. CD15 (green) was used as a granulocyte marker, and DAPI (blue) served as counterstaining. Scale bars 200 µm. (<b>B</b>) Overview of the analyzed tumor specimens and the detection of neutrophils. (<b>C</b>) Representative H&amp;E as well as respective IF images of a cutaneous squamous cell carcinoma (cSCC), a basal cell carcinoma (BCC), and a Merkel cell carcinoma (MCC) (single neutrophils indicated by arrows). Several of the cSCCs showed a high infiltration, in part with a swarming behavior (dotted circle) which was exclusively present in cSCCs. Scale bars 100 µm.</p>
Full article ">Figure 2
<p>(<b>A</b>) (1) Representative H&amp;E staining of a BCC. Tumors were annotated manually on the scanned slides and the cross-sectional area (CSA) was calculated with the open-source software QuPath 0.4.3. (2) A non-ulcerated MCC. If an intact epidermis (arrowheads) could be distinguished throughout the whole sample, the specimen was labeled as non-ulcerated. (3) and (4) Overview and close-up of an ulcerated cSCC. Ulceration (yellow line) was defined as a discontinuity of the epidermis (start indicated by arrow). Scale bar 1000 µm. (<b>B</b>) In BCC and MCC the neutrophil score given in the immunofluorescence staining correlated significantly with the ulceration diameter. In cSCC, no correlation was found. (<b>C</b>) In cSCC and BCC, but not in MCC, the neutrophil infiltration correlated significantly with the CSA. Bars indicate mean + standard deviation (SD). r = Spearman’s <span class="html-italic">ρ</span>. * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 3
<p>(<b>A</b>) Exemplary pictures of different neutrophil extracellular traps (NET) scores (NET0–NET3) assessed by immunofluorescence staining. H3cit (red) indicates NET formation. DAPI (blue) served as counterstaining. NET0: A non-ulcerated BCC with no NETs. NET1: An ulcerated BCC with some infiltration by NETs. In BCCs, they typically could be distinguished in the scab (arrow) and in the ulceration zone. NET2: An ulcerated cSCC with medium NET infiltration. NET3: An ulcerated cSCC with extensive NET infiltration. Scale bar 200 µm. (<b>B</b>) Overview of the analyzed tumor specimens and the detection of NETs. (<b>C</b>) The presence of NETs was associated with a significantly larger ulceration diameter across all entities. Bars indicate mean + SD. (<b>D</b>) Representative immunofluorescence images of a cSCC with a neutrophil score of N3 and a NET score of NET3. CD15 (green) was used to stain granulocytes. Small images show CD15 (green) and H3cit (red) channels separately. Towards the ulceration, disintegrating granulocytes can be distinguished that, in part, expulse NETs (examples shown by arrowheads). Scale bars 100 µm. * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 4
<p>H&amp;E and the respective immunofluorescence (IF) images of exemplary stained cSCC and MCC metastases. CD15 (green) was used as a granulocyte marker, H3cit (red) visualized NETs, and DAPI (blue) served as counterstaining. Necrotic areas are surrounded by dotted lines. (<b>A</b>) A necrotic cutaneous cSCC metastasis. Scale bar 200 µm. Enlarged section: Neutrophils in the necrotic area expulse NETs (examples indicated by arrowheads). Scale bar 100 µm. (<b>B</b>) A necrotic lymph node, highly infiltrated by neutrophils (green), but without NETs. Scale bar 1 mm.</p>
Full article ">Figure 5
<p>Intra- and peritumoral CD8<sup>+</sup> T cells were counted via immunohistochemistry and the open-source Software QuPath 0.4.3. (<b>A</b>) The tumor (1, green area) was annotated manually and a 300 µm rim (2, red area) was defined as the peritumoral region. Positive cells (3, red dots) were counted with the Positive Cell Detection command. The CD8<sup>+</sup> T-cell density quotient was calculated by dividing the intratumoral CD8<sup>+</sup> T cell density [cells/mm<sup>2</sup>] by the peritumoral CD8<sup>+</sup> T cell density [cells/mm<sup>2</sup>]. Scale bar 200 µm. (<b>B</b>) In BCC and cSCC, significantly more CD8<sup>+</sup> T cells were detected in the peritumoral area than inside the tumor. (<b>C</b>,<b>D</b>) No difference in the CD8<sup>+</sup> T cell density quotient was seen in specimens that contained NETs and those that did not, nor in specimens with different neutrophil infiltration densities. **** indicates <span class="html-italic">p</span> &lt; 0.0001, ns = not significant.</p>
Full article ">
10 pages, 2715 KiB  
Communication
Squamous Cell Carcinoma in Never Smokers: An Insight into SMARCB1 Loss
by Akshay J. Patel, Hanan Hemead, Hannah Jesani, Andrea Bille, Philippe Taniere and Gary Middleton
Int. J. Mol. Sci. 2024, 25(15), 8165; https://doi.org/10.3390/ijms25158165 - 26 Jul 2024
Viewed by 873
Abstract
Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) constituting 85% of cases. Among NSCLCs, squamous cell carcinoma (SqCC) is strongly associated with smoking. However, lung cancer in never smokers (LCINS) represents approximately 25% of lung [...] Read more.
Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) constituting 85% of cases. Among NSCLCs, squamous cell carcinoma (SqCC) is strongly associated with smoking. However, lung cancer in never smokers (LCINS) represents approximately 25% of lung cancer cases globally and shows increasing incidence, particularly in East Asia. LCINS-SqCC is less well-characterized, especially regarding its genomic alterations and their impact on clinical outcomes. We conducted a retrospective analysis over a 20-year period (July 2003–July 2023) at two major tertiary centers in the UK. The cohort included 59 patients with LCINS-SqCC who underwent radical surgical resection. Data collected included demographic information, comorbidities, histopathological details, and outcome metrics such as disease-free and overall survival. Molecular sequencing of tumor specimens was performed to identify genomic aberrations. The cohort had a median age of 71 years (IQR 62–77) and a median BMI of 25.4 (IQR 22.8–27.8), with a slight male predominance (53%). The majority of patients (93%) had a preoperative MRC of 1–2. Recurrent disease was observed in 23 patients (39%), and 32 patients (54%) had died at a median follow-up of 3 years. Median disease-free survival was 545 days (IQR 132–1496), and overall survival was 888 days (IQR 443–2071). Preoperative creatinine levels were higher in patients who experienced recurrence (p = 0.037). Molecular analysis identified biallelic SMARCB1 loss in two younger patients, associated with rapid disease progression despite R0 resection. These patients’ tumors were PDL1-negative, TTF-1-negative, and positive for cytokeratin, CD56, and p40. SMARCB1-deficient SqCC in never smokers represents a highly aggressive variant with poor disease-free survival, highlighting the importance of integrating advanced molecular diagnostics in clinical practice. This study underscores the necessity for personalized treatment strategies, including targeted therapies such as EZH2 inhibitors and immune checkpoint blockade, to address the unique molecular pathways in SMARCB1-deficient cancers. Further clinical trials are essential to optimize therapeutic approaches for this challenging subgroup of lung cancer. Full article
Show Figures

Figure 1

Figure 1
<p>Kaplan–Meier curve demonstrating disease-free survival between SMARCB1 loss and preservation.</p>
Full article ">Figure 2
<p>Contrast-enhanced chest CT, axial section demonstrating a large left lower lobe lung cancer (<b>A</b>), coronal view (<b>B</b>), post-resectional recurrence on PET-CT, axial section (<b>C</b>), and recurrence in the station 8 paraoesophageal region (<b>D</b>).</p>
Full article ">Figure 3
<p>PET-CT axial slice of a right upper lobe stage I lung cancer (<b>A</b>). Post-operative recurrence in the upper paratracheal regions on PET-CT (<b>B</b>). Recurrence at the right hilum (<b>C</b>). Recurrence in the posterior ribs on the right-hand side (<b>D</b>).</p>
Full article ">
13 pages, 5739 KiB  
Article
Evaluation of Prostate-Specific Membrane Antigen (PSMA) Immunohistochemical Expression in Early-Stage Breast Cancer Subtypes
by Natalia Andryszak, Paweł Kurzawa, Monika Krzyżaniak, Michał Nowicki, Marek Ruchała, Dariusz Iżycki and Rafał Czepczyński
Int. J. Mol. Sci. 2024, 25(12), 6519; https://doi.org/10.3390/ijms25126519 - 13 Jun 2024
Viewed by 846
Abstract
Breast cancer, known for its diverse subtypes, ranks as one of the leading causes of cancer-related deaths. Prostate-specific membrane antigen (PSMA), primarily associated with prostate cancer, has also been identified in breast cancer, though its role remains unclear. This study aimed to evaluate [...] Read more.
Breast cancer, known for its diverse subtypes, ranks as one of the leading causes of cancer-related deaths. Prostate-specific membrane antigen (PSMA), primarily associated with prostate cancer, has also been identified in breast cancer, though its role remains unclear. This study aimed to evaluate PSMA expression across different subtypes of early-stage breast cancer and investigate its correlation with clinicopathological factors. This retrospective study included 98 breast cancer cases. PSMA expression was examined in both tumor cells and tumor-associated blood vessels. The analysis revealed PSMA expression in tumor-associated blood vessels in 88 cases and in tumor cells in 75 cases. Ki67 expression correlated positively with PSMA expression in blood vessels (p < 0.0001, RSpearman 0.42) and tumor cells (p = 0.010, RSpearman 0.26). The estrogen and progesterone receptor expression correlated negatively with PSMA levels in blood vessels (p = 0.0053, R Spearman −0.26 and p = 0.00026, R Spearman −0.347, respectively). Human epidermal growth factor receptor 2 (HER2) status did not significantly impact PSMA expression. We did not detect any statistically significant differences between breast cancer subtypes. These findings provide evidence for a heterogenous PSMA expression in breast cancer tissue and suggest its correlation with tumor aggressiveness. Despite the limited sample size, the study provides valuable insights into the potential of PSMA as a prognostic, diagnostic, and therapeutic target in the management of breast cancer. Full article
Show Figures

Figure 1

Figure 1
<p>Correlation between Ki67 level and PSMA expression in blood vessels of the tumor.</p>
Full article ">Figure 2
<p>Correlation between estrogen receptor percentage level and PSMA expression in blood vessels of the tumor.</p>
Full article ">Figure 3
<p>Correlation between progesterone receptor percentage level and PSMA expression in blood vessels of the tumor.</p>
Full article ">Figure 4
<p>The case of one patient with TNBC, Ki67 85%, grade 3. Immunohistochemistry revealed strongly positive expression of PSMA in tumor cells (cytoplasm + membrane) (<b>A</b>) and positive expression of PSMA in tumor-associated vessels (<b>B</b>). The patient was diagnosed at stage IIA. (<b>A</b>) Mag. ×400; and (<b>B</b>) mag. ×200.</p>
Full article ">Figure 5
<p>The case of one patient with HER2-luminal breast cancer, 90% estrogen receptor expression, 2% progesterone receptor expression, Ki67 45%, grade 2. Immunohistochemistry revealed focal positive expression of PSMA in tumor cells (<b>A</b>) and positive expression of PSMA in tumor-associated vessels (<b>B</b>). The patient was diagnosed at stage IIA. (<b>A</b>) Mag. ×200; and (<b>B</b>) mag. ×200.</p>
Full article ">Figure 6
<p>The case of one patient with luminal A-like breast cancer, 90% estrogen receptor expression, 70% progesterone receptor expression, Ki67 2%, grade 2. Immunohistochemistry revealed negative expression of PSMA in tumor cells (<b>A</b>) and weakly positive expression of PSMA in tumor-associated vessels (<b>B</b>). The patient was diagnosed at stage IB. (<b>A</b>) Mag. ×400, and (<b>B</b>) mag. ×400.</p>
Full article ">Figure 7
<p>Flowchart of selecting patients for the study.</p>
Full article ">
10 pages, 845 KiB  
Communication
Unlocking Predictive Power: Quantitative Assessment of CAR-T Expansion with Digital Droplet Polymerase Chain Reaction (ddPCR)
by Eugenio Galli, Marcello Viscovo, Federica Fosso, Ilaria Pansini, Giacomo Di Cesare, Camilla Iacovelli, Elena Maiolo, Federica Sorà, Stefan Hohaus, Simona Sica, Silvia Bellesi and Patrizia Chiusolo
Int. J. Mol. Sci. 2024, 25(5), 2673; https://doi.org/10.3390/ijms25052673 - 26 Feb 2024
Cited by 1 | Viewed by 1460
Abstract
Flow cytometry (FCM) and quantitative PCR (qPCR) are conventional methods for assessing CAR-T expansion, while digital droplet PCR (ddPCR) is emerging as a promising alternative. We monitored CAR-T transcript expansion in 40 B-NHL patients post-infusion of CAR-T products (axi-cel; tisa-cel; and brexu-cel) with [...] Read more.
Flow cytometry (FCM) and quantitative PCR (qPCR) are conventional methods for assessing CAR-T expansion, while digital droplet PCR (ddPCR) is emerging as a promising alternative. We monitored CAR-T transcript expansion in 40 B-NHL patients post-infusion of CAR-T products (axi-cel; tisa-cel; and brexu-cel) with both His-Tag FCM and ddPCR techniques. Sensitivity and predictive capacity for efficacy and safety outcomes of ddPCR were analyzed and compared with FCM. A significant correlation between CAR-T counts determined by FCM and CAR transcripts assessed by ddPCR (p < 0.001) was observed. FCM revealed median CD3+CAR+ cell counts at 7, 14, and 30 days post-infusion with no significant differences. In contrast, ddPCR-measured median copies of CAR-T transcripts demonstrated significant lower copy numbers in tisa-cel recipients compared to the other products at day 7 and day 14. Patients with a peak of CAR transcripts at day 7 exceeding 5000 copies/microg gDNA, termed “good CAR-T expanders”, were more likely to achieve a favorable response at 3 months (HR 10.79, 95% CI 1.16–100.42, p = 0.036). Good CAR-T expanders showed superior progression-free survival at 3, 6, and 12 months compared to poor CAR-T expanders (p = 0.088). Those reaching a peak higher than 5000 copies/microg gDNA were more likely to experience severe CRS and ICANS. DdPCR proves to be a practical method for monitoring CAR-T expansion, providing quantitative information that better predicts both treatment outcomes and toxicity. Full article
Show Figures

Figure 1

Figure 1
<p>Comparison of CAR-T expansion monitored by flow cytometry (FCM) and digital droplet PCR (ddPCR). (<b>A</b>) Spearman correlation analysis: the correlation between the data obtained by FCM and ddPCR is represented using a logarithmic scale on both axes. (<b>B</b>) Temporal comparison of expansion: the expansion dynamics of axi-cel, brexu-cel, and tisa-cel at d7, d14, and d30 post CAR-T infusion are illustrated. Dark blue bars depict expansion detected with FCM, while light blue bars represent expansion detected by ddPCR.</p>
Full article ">Figure 2
<p>Progression-free survival comparing patients with at least 5000 CAR copies/microg gDNA at day 7 (designated as expanders, blue line) with non-expanders, (red line). At three months after CAR-T infusion, expanders show a higher rate of partial or complete responses compared to non-expanders (100% vs. 62%).</p>
Full article ">
22 pages, 5015 KiB  
Article
Antitumor Effects of Intravenous Natural Killer Cell Infusion in an Orthotopic Glioblastoma Xenograft Murine Model and Gene Expression Profile Analysis
by Takayuki Morimoto, Tsutomu Nakazawa, Ryosuke Matsuda, Ryosuke Maeoka, Fumihiko Nishimura, Mitsutoshi Nakamura, Shuichi Yamada, Young-Soo Park, Takahiro Tsujimura and Ichiro Nakagawa
Int. J. Mol. Sci. 2024, 25(4), 2435; https://doi.org/10.3390/ijms25042435 - 19 Feb 2024
Viewed by 1821
Abstract
Despite standard multimodality treatment, containing maximum safety resection, temozolomide, radiotherapy, and a tumor-treating field, patients with glioblastoma (GBM) present with a dismal prognosis. Natural killer cell (NKC)-based immunotherapy would play a critical role in GBM treatment. We have previously reported highly activated and [...] Read more.
Despite standard multimodality treatment, containing maximum safety resection, temozolomide, radiotherapy, and a tumor-treating field, patients with glioblastoma (GBM) present with a dismal prognosis. Natural killer cell (NKC)-based immunotherapy would play a critical role in GBM treatment. We have previously reported highly activated and ex vivo expanded NK cells derived from human peripheral blood, which exhibited anti-tumor effect against GBM cells. Here, we performed preclinical evaluation of the NK cells using an in vivo orthotopic xenograft model, the U87MG cell-derived brain tumor in NOD/Shi-scid, IL-2RɤKO (NOG) mouse. In the orthotopic xenograft model, the retro-orbital venous injection of NK cells prolonged overall survival of the NOG mouse, indirectly indicating the growth-inhibition effect of NK cells. In addition, we comprehensively summarized the differentially expressed genes, especially focusing on the expression of the NKC-activating receptors’ ligands, inhibitory receptors’ ligands, chemokines, and chemokine receptors, between murine brain tumor treated with NKCs and with no agents, by using microarray. Furthermore, we also performed differentially expressed gene analysis between an internal and external brain tumor in the orthotopic xenograft model. Our findings could provide pivotal information for the NK-cell-based immunotherapy for patients with GBM. Full article
Show Figures

Figure 1

Figure 1
<p>Enhanced growth inhibition of glioblastoma (GBM) cells by natural killer cells (NKCs). The graph on the left shows the growth curves of U87MG (<b>a</b>) and T98G cells (<b>b</b>) co-cultured with NKCs at effector-to-target cell ratios of 1:1 (red) and 1:2 (green). The blue curve represents cell lines only. The graphs on the right depict real-time cell analysis-based growth inhibition assays. NKC#1 and NKC#2 were derived from another donors. Blue bars represent cell lines only, red bars represent an effector-to-target cell ratio of 1:1, and green bars represent an effector-to-target cell ratio of 1:2. The X and Y axes indicate the co-culture time (min) and relative normalized cell index, respectively. Values represent mean ± standard deviation of 5–6 experiments. Statistical differences were determined by two-way analysis of variance, followed by Tukey’s test. **** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05, ns: not significant.</p>
Full article ">Figure 2
<p>Antitumor effects of natural killer cells (NKCs) in an orthotopic xenograft murine model derived from a glioblastoma (GBM) cell line. (<b>a</b>) Schematic of the GBM xenograft model where mice were injected with NKCs via the retro-orbital sinus (<span class="html-italic">n</span> = 6/group). (<b>b</b>) Kaplan–Meier survival curves for mice treated (once or twice) or non-treated with NKCs. NKC#1 and NKC#2 were derived from another donors. Statistical differences were determined by two-way analysis of variance, followed by Tukey’s test. * <span class="html-italic">p</span> &lt; 0.05, ns: not significant. (<b>c</b>) Pathological validation of intracranial tumors from each group (negative background, NKC#1, and NKC#2) using hematoxylin and eosin staining. Scale bar, 50 µm.</p>
Full article ">Figure 2 Cont.
<p>Antitumor effects of natural killer cells (NKCs) in an orthotopic xenograft murine model derived from a glioblastoma (GBM) cell line. (<b>a</b>) Schematic of the GBM xenograft model where mice were injected with NKCs via the retro-orbital sinus (<span class="html-italic">n</span> = 6/group). (<b>b</b>) Kaplan–Meier survival curves for mice treated (once or twice) or non-treated with NKCs. NKC#1 and NKC#2 were derived from another donors. Statistical differences were determined by two-way analysis of variance, followed by Tukey’s test. * <span class="html-italic">p</span> &lt; 0.05, ns: not significant. (<b>c</b>) Pathological validation of intracranial tumors from each group (negative background, NKC#1, and NKC#2) using hematoxylin and eosin staining. Scale bar, 50 µm.</p>
Full article ">Figure 3
<p>Differential gene expression analysis between natural killer cell (NKC)-treated and non-treated intracranial tumors from the orthotopic glioblastoma (GBM) xenograft model. (<b>a</b>) Volcano plot illustrating log<sub>2</sub>-scaled fold change (x-axis) and −log<sub>10</sub> <span class="html-italic">p</span>-value (y-axis) for each gene. (<b>b</b>–<b>g</b>) Heatmaps of the transcriptome-wide Clariom™ S array of gene expression related to glioma stem cell (GSC) markers (<b>b</b>), extracellular matrix (ECM) markers (<b>c</b>), chemokines (<b>d</b>), chemokine receptors (<b>e</b>), NKC-activating receptor ligands (<b>f</b>), and NKC inhibitory receptor ligands (<b>g</b>). Bar graphs illustrating the normalized enrichment score (NES) (<b>h</b>). Enrichment plot depicting downregulated gene sets belonging to different gene ontology (GO) categories (<b>i</b>).</p>
Full article ">Figure 3 Cont.
<p>Differential gene expression analysis between natural killer cell (NKC)-treated and non-treated intracranial tumors from the orthotopic glioblastoma (GBM) xenograft model. (<b>a</b>) Volcano plot illustrating log<sub>2</sub>-scaled fold change (x-axis) and −log<sub>10</sub> <span class="html-italic">p</span>-value (y-axis) for each gene. (<b>b</b>–<b>g</b>) Heatmaps of the transcriptome-wide Clariom™ S array of gene expression related to glioma stem cell (GSC) markers (<b>b</b>), extracellular matrix (ECM) markers (<b>c</b>), chemokines (<b>d</b>), chemokine receptors (<b>e</b>), NKC-activating receptor ligands (<b>f</b>), and NKC inhibitory receptor ligands (<b>g</b>). Bar graphs illustrating the normalized enrichment score (NES) (<b>h</b>). Enrichment plot depicting downregulated gene sets belonging to different gene ontology (GO) categories (<b>i</b>).</p>
Full article ">Figure 3 Cont.
<p>Differential gene expression analysis between natural killer cell (NKC)-treated and non-treated intracranial tumors from the orthotopic glioblastoma (GBM) xenograft model. (<b>a</b>) Volcano plot illustrating log<sub>2</sub>-scaled fold change (x-axis) and −log<sub>10</sub> <span class="html-italic">p</span>-value (y-axis) for each gene. (<b>b</b>–<b>g</b>) Heatmaps of the transcriptome-wide Clariom™ S array of gene expression related to glioma stem cell (GSC) markers (<b>b</b>), extracellular matrix (ECM) markers (<b>c</b>), chemokines (<b>d</b>), chemokine receptors (<b>e</b>), NKC-activating receptor ligands (<b>f</b>), and NKC inhibitory receptor ligands (<b>g</b>). Bar graphs illustrating the normalized enrichment score (NES) (<b>h</b>). Enrichment plot depicting downregulated gene sets belonging to different gene ontology (GO) categories (<b>i</b>).</p>
Full article ">Figure 3 Cont.
<p>Differential gene expression analysis between natural killer cell (NKC)-treated and non-treated intracranial tumors from the orthotopic glioblastoma (GBM) xenograft model. (<b>a</b>) Volcano plot illustrating log<sub>2</sub>-scaled fold change (x-axis) and −log<sub>10</sub> <span class="html-italic">p</span>-value (y-axis) for each gene. (<b>b</b>–<b>g</b>) Heatmaps of the transcriptome-wide Clariom™ S array of gene expression related to glioma stem cell (GSC) markers (<b>b</b>), extracellular matrix (ECM) markers (<b>c</b>), chemokines (<b>d</b>), chemokine receptors (<b>e</b>), NKC-activating receptor ligands (<b>f</b>), and NKC inhibitory receptor ligands (<b>g</b>). Bar graphs illustrating the normalized enrichment score (NES) (<b>h</b>). Enrichment plot depicting downregulated gene sets belonging to different gene ontology (GO) categories (<b>i</b>).</p>
Full article ">Figure 3 Cont.
<p>Differential gene expression analysis between natural killer cell (NKC)-treated and non-treated intracranial tumors from the orthotopic glioblastoma (GBM) xenograft model. (<b>a</b>) Volcano plot illustrating log<sub>2</sub>-scaled fold change (x-axis) and −log<sub>10</sub> <span class="html-italic">p</span>-value (y-axis) for each gene. (<b>b</b>–<b>g</b>) Heatmaps of the transcriptome-wide Clariom™ S array of gene expression related to glioma stem cell (GSC) markers (<b>b</b>), extracellular matrix (ECM) markers (<b>c</b>), chemokines (<b>d</b>), chemokine receptors (<b>e</b>), NKC-activating receptor ligands (<b>f</b>), and NKC inhibitory receptor ligands (<b>g</b>). Bar graphs illustrating the normalized enrichment score (NES) (<b>h</b>). Enrichment plot depicting downregulated gene sets belonging to different gene ontology (GO) categories (<b>i</b>).</p>
Full article ">Figure 4
<p>Differential gene expression analysis between the external and internal layers of intracranial tumors from the orthotopic glioblastoma (GBM) xenograft model. (<b>a</b>) Volcano plot illustrating log<sub>2</sub>-scaled fold change (x-axis) and −log<sub>10</sub> <span class="html-italic">p</span>-value (y-axis) for each gene. (<b>b</b>–<b>g</b>) Heatmaps of the transcriptome-wide Clariom™ S array of gene expression related to glioma stem cell (GSC) markers (<b>b</b>), extracellular matrix (ECM) markers (<b>c</b>), chemokines (<b>d</b>), chemokine receptors (<b>e</b>), natural killer cell (NKC)-activating receptor ligands (<b>f</b>), and NKC inhibitory receptor ligands (<b>g</b>).</p>
Full article ">Figure 4 Cont.
<p>Differential gene expression analysis between the external and internal layers of intracranial tumors from the orthotopic glioblastoma (GBM) xenograft model. (<b>a</b>) Volcano plot illustrating log<sub>2</sub>-scaled fold change (x-axis) and −log<sub>10</sub> <span class="html-italic">p</span>-value (y-axis) for each gene. (<b>b</b>–<b>g</b>) Heatmaps of the transcriptome-wide Clariom™ S array of gene expression related to glioma stem cell (GSC) markers (<b>b</b>), extracellular matrix (ECM) markers (<b>c</b>), chemokines (<b>d</b>), chemokine receptors (<b>e</b>), natural killer cell (NKC)-activating receptor ligands (<b>f</b>), and NKC inhibitory receptor ligands (<b>g</b>).</p>
Full article ">Figure 4 Cont.
<p>Differential gene expression analysis between the external and internal layers of intracranial tumors from the orthotopic glioblastoma (GBM) xenograft model. (<b>a</b>) Volcano plot illustrating log<sub>2</sub>-scaled fold change (x-axis) and −log<sub>10</sub> <span class="html-italic">p</span>-value (y-axis) for each gene. (<b>b</b>–<b>g</b>) Heatmaps of the transcriptome-wide Clariom™ S array of gene expression related to glioma stem cell (GSC) markers (<b>b</b>), extracellular matrix (ECM) markers (<b>c</b>), chemokines (<b>d</b>), chemokine receptors (<b>e</b>), natural killer cell (NKC)-activating receptor ligands (<b>f</b>), and NKC inhibitory receptor ligands (<b>g</b>).</p>
Full article ">Figure 4 Cont.
<p>Differential gene expression analysis between the external and internal layers of intracranial tumors from the orthotopic glioblastoma (GBM) xenograft model. (<b>a</b>) Volcano plot illustrating log<sub>2</sub>-scaled fold change (x-axis) and −log<sub>10</sub> <span class="html-italic">p</span>-value (y-axis) for each gene. (<b>b</b>–<b>g</b>) Heatmaps of the transcriptome-wide Clariom™ S array of gene expression related to glioma stem cell (GSC) markers (<b>b</b>), extracellular matrix (ECM) markers (<b>c</b>), chemokines (<b>d</b>), chemokine receptors (<b>e</b>), natural killer cell (NKC)-activating receptor ligands (<b>f</b>), and NKC inhibitory receptor ligands (<b>g</b>).</p>
Full article ">Figure 4 Cont.
<p>Differential gene expression analysis between the external and internal layers of intracranial tumors from the orthotopic glioblastoma (GBM) xenograft model. (<b>a</b>) Volcano plot illustrating log<sub>2</sub>-scaled fold change (x-axis) and −log<sub>10</sub> <span class="html-italic">p</span>-value (y-axis) for each gene. (<b>b</b>–<b>g</b>) Heatmaps of the transcriptome-wide Clariom™ S array of gene expression related to glioma stem cell (GSC) markers (<b>b</b>), extracellular matrix (ECM) markers (<b>c</b>), chemokines (<b>d</b>), chemokine receptors (<b>e</b>), natural killer cell (NKC)-activating receptor ligands (<b>f</b>), and NKC inhibitory receptor ligands (<b>g</b>).</p>
Full article ">Figure 4 Cont.
<p>Differential gene expression analysis between the external and internal layers of intracranial tumors from the orthotopic glioblastoma (GBM) xenograft model. (<b>a</b>) Volcano plot illustrating log<sub>2</sub>-scaled fold change (x-axis) and −log<sub>10</sub> <span class="html-italic">p</span>-value (y-axis) for each gene. (<b>b</b>–<b>g</b>) Heatmaps of the transcriptome-wide Clariom™ S array of gene expression related to glioma stem cell (GSC) markers (<b>b</b>), extracellular matrix (ECM) markers (<b>c</b>), chemokines (<b>d</b>), chemokine receptors (<b>e</b>), natural killer cell (NKC)-activating receptor ligands (<b>f</b>), and NKC inhibitory receptor ligands (<b>g</b>).</p>
Full article ">Figure 4 Cont.
<p>Differential gene expression analysis between the external and internal layers of intracranial tumors from the orthotopic glioblastoma (GBM) xenograft model. (<b>a</b>) Volcano plot illustrating log<sub>2</sub>-scaled fold change (x-axis) and −log<sub>10</sub> <span class="html-italic">p</span>-value (y-axis) for each gene. (<b>b</b>–<b>g</b>) Heatmaps of the transcriptome-wide Clariom™ S array of gene expression related to glioma stem cell (GSC) markers (<b>b</b>), extracellular matrix (ECM) markers (<b>c</b>), chemokines (<b>d</b>), chemokine receptors (<b>e</b>), natural killer cell (NKC)-activating receptor ligands (<b>f</b>), and NKC inhibitory receptor ligands (<b>g</b>).</p>
Full article ">

Review

Jump to: Research

18 pages, 2127 KiB  
Review
Oncolytic Viruses as Reliable Adjuvants in CAR-T Cell Therapy for Solid Tumors
by Ruxandra Ilinca Stilpeanu, Bianca Stefania Secara, Mircea Cretu-Stancu and Octavian Bucur
Int. J. Mol. Sci. 2024, 25(20), 11127; https://doi.org/10.3390/ijms252011127 - 16 Oct 2024
Viewed by 322
Abstract
Although impactful scientific advancements have recently been made in cancer therapy, there remains an opportunity for future improvements. Immunotherapy is perhaps one of the most cutting-edge categories of therapies demonstrating potential in the clinical setting. Genetically engineered T cells express chimeric antigen receptors [...] Read more.
Although impactful scientific advancements have recently been made in cancer therapy, there remains an opportunity for future improvements. Immunotherapy is perhaps one of the most cutting-edge categories of therapies demonstrating potential in the clinical setting. Genetically engineered T cells express chimeric antigen receptors (CARs), which can detect signals expressed by the molecules present on the surface of cancer cells, also called tumor-associated antigens (TAAs). Their effectiveness has been extensively demonstrated in hematological cancers; therefore, these results can establish the groundwork for their applications on a wide range of requirements. However, the application of CAR-T cell technology for solid tumors has several challenges, such as the existence of an immune-suppressing tumor microenvironment and/or inadequate tumor infiltration. Consequently, combining therapies such as CAR-T cell technology with other approaches has been proposed. The effectiveness of combining CAR-T cell with oncolytic virus therapy, with either genetically altered or naturally occurring viruses, to target tumor cells is currently under investigation, with several clinical trials being conducted. This narrative review summarizes the current advancements, opportunities, benefits, and limitations in using each therapy alone and their combination. The use of oncolytic viruses offers an opportunity to address the existing challenges of CAR-T cell therapy, which appear in the process of trying to overcome solid tumors, through the combination of their strengths. Additionally, utilizing oncolytic viruses allows researchers to modify the virus, thus enabling the targeted delivery of specific therapeutic agents within the tumor environment. This, in turn, can potentially enhance the cytotoxic effect and therapeutic potential of CAR-T cell technology on solid malignancies, with impactful results in the clinical setting. Full article
Show Figures

Figure 1

Figure 1
<p>The structure of a CAR-T Cell. (<b>a</b>) The T cell, the chimeric antigen receptor (CAR), and the genetically engineered CAR-T cell are illustrated. (<b>b</b>) The structure of the CAR-T cell and its interaction with the surface antigen of the tumor cell are presented. (<b>c</b>) Differences between the intracellular signaling domains of the 5 generations of CARs. The first generation presents a CD3ζ-derived signaling module. The second generation of CARs is the first to contain a co-stimulatory domain. Co-stimulatory molecules include CD28, 4-1BB (CD137), CD27, and OX40 (CD134). TM—transmembrane domain; TRUCK—T cell redirected for universal cytokine-mediated killing; CoS1,2—co-stimulatory domain; IL-2Rβ—IL-2 receptor β. This figure was created with GoodNotes and adapted from reference [<a href="#B13-ijms-25-11127" class="html-bibr">13</a>] for (<b>a</b>) and (<b>b</b>) and reference [<a href="#B14-ijms-25-11127" class="html-bibr">14</a>] for (<b>c</b>).</p>
Full article ">Figure 2
<p>Three possible combinations of CAR-T cell therapy with other therapies are presented. From right to left: (<b>a</b>) chemotherapy, (<b>b</b>) oncolytic viral therapy, and (<b>c</b>) radiation therapy. This figure was created with GoodNotes and adapted from reference [<a href="#B2-ijms-25-11127" class="html-bibr">2</a>].</p>
Full article ">Figure 3
<p>The effect of CAR-T cells on macrophages and the development of cytokine release syndrome. The interaction of macrophages with activated CAR-T cells further leads to the release of chemokines and cytokines such as IL-1, IL-6, and iNOS, causing supplementary inflammatory reactions, resulting in CRS. This figure was created with GoodNotes and adapted from reference [<a href="#B117-ijms-25-11127" class="html-bibr">117</a>].</p>
Full article ">
21 pages, 3652 KiB  
Review
Targeting Interleukin-13 Receptor α2 and EphA2 in Aggressive Breast Cancer Subtypes with Special References to Chimeric Antigen Receptor T-Cell Therapy
by Dharambir Kashyap and Huda Salman
Int. J. Mol. Sci. 2024, 25(7), 3780; https://doi.org/10.3390/ijms25073780 - 28 Mar 2024
Viewed by 1456
Abstract
Breast cancer (BCA) remains the leading cause of cancer-related mortality among women worldwide. This review delves into the therapeutic challenges of BCA, emphasizing the roles of interleukin-13 receptor α2 (IL-13Rα2) and erythropoietin-producing hepatocellular receptor A2 (EphA2) in tumor progression and resistance. Highlighting their [...] Read more.
Breast cancer (BCA) remains the leading cause of cancer-related mortality among women worldwide. This review delves into the therapeutic challenges of BCA, emphasizing the roles of interleukin-13 receptor α2 (IL-13Rα2) and erythropoietin-producing hepatocellular receptor A2 (EphA2) in tumor progression and resistance. Highlighting their overexpression in BCA, particularly in aggressive subtypes, such as Her-2-enriched and triple-negative breast cancer (TNBC), we discuss the potential of these receptors as targets for chimeric antigen receptor T-cell (CAR-T) therapies. We examine the structural and functional roles of IL-13Rα2 and EphA2, their pathological significance in BCA, and the promising therapeutic avenues their targeting presents. With an in-depth analysis of current immunotherapeutic strategies, including the limitations of existing treatments and the potential of dual antigen-targeting CAR T-cell therapies, this review aims to summarize potential future novel, more effective therapeutic interventions for BCA. Through a thorough examination of preclinical and clinical studies, it underlines the urgent need for targeted therapies in combating the high mortality rates associated with Her-2-enriched and TNBC subtypes and discusses the potential role of IL-13Rα2 and EphA2 as promising candidates for the development of CAR T-cell therapies. Full article
Show Figures

Figure 1

Figure 1
<p>Structure of IL-13Rα2 and EphAR2 genes (Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>) and their protein 3D structures.</p>
Full article ">Figure 2
<p>Survival curve analysis shows that a higher expression of EphA2 (<b>a</b>) and IL-13Rα2 (<b>b</b>) correlates with a lesser survival of Her-2-enriched and TNBC patients (Reprinted/adapted with permission from Refs. [<a href="#B3-ijms-25-03780" class="html-bibr">3</a>,<a href="#B34-ijms-25-03780" class="html-bibr">34</a>]. Copyright ©2021 The Authors; Published by the American Association for Cancer Research and Copyright © 2017 The Author(s)).</p>
Full article ">Figure 3
<p>Illustration of IL-13Rα2- and EphA2-mediated tumorigenic signaling in breast cancer. Both activated pathways regulate cell adhesion, invasion, metastasis, and cell plasticity via regulating MMPs, PI3K/AKT/mTOR, and ERK/MAP signaling (Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>).</p>
Full article ">Figure 4
<p>Illustration of different immunotherapeutic approaches to targeted IL-13Rα2 and EphA2.</p>
Full article ">Figure 5
<p>Breast cancer immunosuppressive components, such as immune suppressive cells (MDSCs, tumor-associated macrophages, Tegs and regulatory B-cells, and tumor-associated neutrophils), cytokine and/or chemokine milieu, and immune checkpoint proteins.</p>
Full article ">Figure 6
<p>A CAR T-cell designed for targeting IL-13Rα2 and EphA2 antigens on breast cancer cells (Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>).</p>
Full article ">Figure 7
<p>Forest plot for survival outcome in six months for acute lymphoid leukemia [<a href="#B131-ijms-25-03780" class="html-bibr">131</a>,<a href="#B132-ijms-25-03780" class="html-bibr">132</a>,<a href="#B136-ijms-25-03780" class="html-bibr">136</a>,<a href="#B137-ijms-25-03780" class="html-bibr">137</a>,<a href="#B138-ijms-25-03780" class="html-bibr">138</a>]: (<b>a</b>) six-month overall survival, (<b>b</b>) six-month event-free survival (Reprinted/adapted with permission from Ref. [<a href="#B139-ijms-25-03780" class="html-bibr">139</a>]. Copyright © 2023 The Authors. Cancer Medicine published by John Wiley &amp; Sons Ltd.).</p>
Full article ">
Back to TopTop