Cancers

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23 pages, 8915 KiB

Open AccessArticle

Annexin A1 Is Involved in the Antitumor Effects of 5-Azacytidine in Human Oral Squamous Carcinoma Cells

by Nunzia Novizio, Raffaella Belvedere, Mariangela Palazzo, Silvia Varricchio, Francesco Merolla, Stefania Staibano, Gennaro Ilardi and Antonello Petrella

Cancers 2025, 17(7), 1058; https://doi.org/10.3390/cancers17071058 - 21 Mar 2025

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Abstract

Background: the treatment of squamous cell carcinomas of the oral cavity (OSCCs) is limited by the lack of reliable diagnostic/prognostic, and predictive markers, as well as by intrinsic tumor cell heterogeneity. 5-azacytidine (5-AZA) offers opportunities for cancer cell reprogramming to develop new target-specific [...] Read more.

Background: the treatment of squamous cell carcinomas of the oral cavity (OSCCs) is limited by the lack of reliable diagnostic/prognostic, and predictive markers, as well as by intrinsic tumor cell heterogeneity. 5-azacytidine (5-AZA) offers opportunities for cancer cell reprogramming to develop new target-specific treatments. The protein annexin A1 (ANXA1) is downregulated in head and neck squamous cell carcinoma (HNSCC), correlated with pathological differentiation grade. Objectives: this work aimed to further investigate the role of ANXA1 in OSCC progression based on 5-AZA activity. Methods: we used CAL27 and CAL33 cell lines, which differ in drug sensitivity and differentiation status. Results: CAL27 showed a higher expression of the stemness markers compared to CAL33 cells, but this positivity was lost after treatment with 5-AZA. This drug also decreased CAL27 cell motility, promoting a less aggressive phenotype. Moreover, 5-AZA increased ANXA1 expression only in CAL27. After siRNA-mediated downmodulation, we witnessed a significant rise in cell motility and the inversion of E-/N-cadherin expression, which was reverted again by 5-AZA. To investigate the role of exogenous ANXA1 derived from the tumor microenvironment, we treated CAL27 with Ac2-26, an ANXA1 mimetic peptide. Interestingly, we found that this peptide alone showed impacts similar to 5-AZA in reversing the aggressive phenotype. All these effects were not evidenced in CAL33 cells. Finally, to prove the loop of the exogenous protein, we detected increased expression of its receptors, formyl peptide receptors (FPRs), and their activation, leading to oncosuppressor effects. Conclusions: we propose that ANXA1 mediates the effects of 5-AZA only in poorly differentiated stemlike CAL27 cell lines. This suggests the relevance of ANXA1 as a diagnostic/prognostic biomarker in OSCCs, paving the way for personalized therapies to overcome treatment difficulties. Full article

(This article belongs to the Special Issue Adaptive Resistance to Targeted Cancer Therapies and Rational Development of Combination Therapies)

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17 pages, 6585 KiB

Open AccessArticle

BUB1 Inhibition Overcomes Radio- and Chemoradiation Resistance in Lung Cancer

by Shivani Thoidingjam, Sushmitha Sriramulu, Oudai Hassan, Stephen L. Brown, Farzan Siddiqui, Benjamin Movsas, Shirish Gadgeel and Shyam Nyati

Cancers 2024, 16(19), 3291; https://doi.org/10.3390/cancers16193291 - 27 Sep 2024

Cited by 1 | Viewed by 1433

Abstract

Background: Despite advances in targeted therapies and immunotherapies, traditional treatments like microtubule stabilizers (paclitaxel, docetaxel), DNA-intercalating platinum drugs (cisplatin), and radiation therapy remain essential for managing locally advanced and metastatic lung cancer. Identifying novel molecular targets could enhance the efficacy of these [...] Read more.

Background: Despite advances in targeted therapies and immunotherapies, traditional treatments like microtubule stabilizers (paclitaxel, docetaxel), DNA-intercalating platinum drugs (cisplatin), and radiation therapy remain essential for managing locally advanced and metastatic lung cancer. Identifying novel molecular targets could enhance the efficacy of these treatments. Hypothesis: We hypothesize that BUB1 (Ser/Thr kinase) is overexpressed in lung cancers and its inhibition will sensitize lung cancers to chemoradiation. Methods: BUB1 inhibitor (BAY1816032) was combined with cisplatin, paclitaxel, a PARP inhibitor olaparib, and radiation in cell proliferation and radiation-sensitization assays. Biochemical and molecular assays evaluated the impact on DNA damage signaling and cell death. Results: Immunostaining of lung tumor microarrays (TMAs) confirmed higher BUB1 expression in non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) compared to normal tissues. In NSCLC, BUB1 overexpression correlated directly with the expression of TP53 mutations and poorer overall survival in NSCLC and SCLC patients. BAY1816032 synergistically sensitized lung cancer cell lines to paclitaxel and olaparib and enhanced cell killing by radiation in both NSCLC and SCLC. Molecular analysis indicated a shift towards pro-apoptotic and anti-proliferative states, evidenced by altered BAX, BCL2, PCNA, and Caspases-9 and -3 expressions. Conclusions: Elevated BUB1 expression is associated with poorer survival in lung cancer. Inhibiting BUB1 sensitizes NSCLC and SCLC to chemotherapies (cisplatin, paclitaxel), targeted therapy (olaparib), and radiation. Furthermore, we present the novel finding that BUB1 inhibition sensitized both NSCLC and SCLC to radiotherapy and chemoradiation. Our results demonstrate BUB1 inhibition as a promising strategy to sensitize lung cancers to radiation and chemoradiation therapies. Full article

(This article belongs to the Special Issue Adaptive Resistance to Targeted Cancer Therapies and Rational Development of Combination Therapies)

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24 pages, 5267 KiB

Open AccessArticle

Computational Modeling of Drug Response Identifies Mutant-Specific Constraints for Dosing panRAF and MEK Inhibitors in Melanoma

by Andrew Goetz, Frances Shanahan, Logan Brooks, Eva Lin, Rana Mroue, Darlene Dela Cruz, Thomas Hunsaker, Bartosz Czech, Purushottam Dixit, Udi Segal, Scott Martin, Scott A. Foster and Luca Gerosa

Cancers 2024, 16(16), 2914; https://doi.org/10.3390/cancers16162914 - 22 Aug 2024

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Abstract

Purpose: This study explores the potential of pre-clinical in vitro cell line response data and computational modeling in identifying the optimal dosage requirements of pan-RAF (Belvarafenib) and MEK (Cobimetinib) inhibitors in melanoma treatment. Our research is motivated by the critical role of drug [...] Read more.

Purpose: This study explores the potential of pre-clinical in vitro cell line response data and computational modeling in identifying the optimal dosage requirements of pan-RAF (Belvarafenib) and MEK (Cobimetinib) inhibitors in melanoma treatment. Our research is motivated by the critical role of drug combinations in enhancing anti-cancer responses and the need to close the knowledge gap around selecting effective dosing strategies to maximize their potential. Results: In a drug combination screen of 43 melanoma cell lines, we identified specific dosage landscapes of panRAF and MEK inhibitors for NRAS vs. BRAF mutant melanomas. Both experienced benefits, but with a notably more synergistic and narrow dosage range for NRAS mutant melanoma (mean Bliss score of 0.27 in NRAS vs. 0.1 in BRAF mutants). Computational modeling and follow-up molecular experiments attributed the difference to a mechanism of adaptive resistance by negative feedback. We validated the in vivo translatability of in vitro dose–response maps by predicting tumor growth in xenografts with high accuracy in capturing cytostatic and cytotoxic responses. We analyzed the pharmacokinetic and tumor growth data from Phase 1 clinical trials of Belvarafenib with Cobimetinib to show that the synergy requirement imposes stricter precision dose constraints in NRAS mutant melanoma patients. Conclusion: Leveraging pre-clinical data and computational modeling, our approach proposes dosage strategies that can optimize synergy in drug combinations, while also bringing forth the real-world challenges of staying within a precise dose range. Overall, this work presents a framework to aid dose selection in drug combinations. Full article

(This article belongs to the Special Issue Adaptive Resistance to Targeted Cancer Therapies and Rational Development of Combination Therapies)

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25 pages, 3019 KiB

Open AccessArticle

Purinergic Ca²⁺ Signaling as a Novel Mechanism of Drug Tolerance in BRAF-Mutant Melanoma

by Philip E. Stauffer, Jordon Brinkley, David A. Jacobson, Vito Quaranta and Darren R. Tyson

Cancers 2024, 16(13), 2426; https://doi.org/10.3390/cancers16132426 - 30 Jun 2024

Cited by 2 | Viewed by 1667

Abstract

Drug tolerance is a major cause of relapse after cancer treatment. Despite intensive efforts, its molecular basis remains poorly understood, hampering actionable intervention. We report a previously unrecognized signaling mechanism supporting drug tolerance in BRAF-mutant melanoma treated with BRAF inhibitors that could be [...] Read more.

Drug tolerance is a major cause of relapse after cancer treatment. Despite intensive efforts, its molecular basis remains poorly understood, hampering actionable intervention. We report a previously unrecognized signaling mechanism supporting drug tolerance in BRAF-mutant melanoma treated with BRAF inhibitors that could be of general relevance to other cancers. Its key features are cell-intrinsic intracellular Ca²⁺ signaling initiated by P2X7 receptors (purinergic ligand-gated cation channels) and an enhanced ability for these Ca²⁺ signals to reactivate ERK1/2 in the drug-tolerant state. Extracellular ATP, virtually ubiquitous in living systems, is the ligand that can initiate Ca²⁺ spikes via P2X7 channels. ATP is abundant in the tumor microenvironment and is released by dying cells, ironically implicating treatment-initiated cancer cell death as a source of trophic stimuli that leads to ERK reactivation and drug tolerance. Such a mechanism immediately offers an explanation of the inevitable relapse after BRAFi treatment in BRAF-mutant melanoma and points to actionable strategies to overcome it. Full article

(This article belongs to the Special Issue Adaptive Resistance to Targeted Cancer Therapies and Rational Development of Combination Therapies)

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17 pages, 13837 KiB

Open AccessArticle

Dissecting the Spatially Restricted Effects of Microenvironment-Mediated Resistance on Targeted Therapy Responses

by Tatiana Miti, Bina Desai, Daria Miroshnychenko, David Basanta and Andriy Marusyk

Cancers 2024, 16(13), 2405; https://doi.org/10.3390/cancers16132405 - 29 Jun 2024

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Abstract

The response of tumors to anti-cancer therapies is defined not only by cell-intrinsic therapy sensitivities but also by local interactions with the tumor microenvironment. Fibroblasts that make tumor stroma have been shown to produce paracrine factors that can strongly reduce the sensitivity of [...] Read more.

The response of tumors to anti-cancer therapies is defined not only by cell-intrinsic therapy sensitivities but also by local interactions with the tumor microenvironment. Fibroblasts that make tumor stroma have been shown to produce paracrine factors that can strongly reduce the sensitivity of tumor cells to many types of targeted therapies. Moreover, a high stroma/tumor ratio is generally associated with poor survival and reduced therapy responses. However, in contrast to advanced knowledge of the molecular mechanisms responsible for stroma-mediated resistance, its effect on the ability of tumors to escape therapeutic eradication remains poorly understood. To a large extent, this gap of knowledge reflects the challenge of accounting for the spatial aspects of microenvironmental resistance, especially over longer time frames. To address this problem, we integrated spatial inferences of proliferation-death dynamics from an experimental animal model of targeted therapy responses with spatial mathematical modeling. With this approach, we dissected the impact of tumor/stroma distribution, magnitude and distance of stromal effects. While all of the tested parameters affected the ability of tumor cells to resist elimination, spatial patterns of stroma distribution within tumor tissue had a particularly strong impact. Full article

(This article belongs to the Special Issue Adaptive Resistance to Targeted Cancer Therapies and Rational Development of Combination Therapies)

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25 pages, 2925 KiB

Open AccessArticle

Cell State Transition Models Stratify Breast Cancer Cell Phenotypes and Reveal New Therapeutic Targets

by Oleksii S. Rukhlenko, Hiroaki Imoto, Ayush Tambde, Amy McGillycuddy, Philipp Junk, Anna Tuliakova, Walter Kolch and Boris N. Kholodenko

Cancers 2024, 16(13), 2354; https://doi.org/10.3390/cancers16132354 - 27 Jun 2024

Cited by 1 | Viewed by 1940

Abstract

Understanding signaling patterns of transformation and controlling cell phenotypes is a challenge of current biology. Here we applied a cell State Transition Assessment and Regulation (cSTAR) approach to a perturbation dataset of single cell phosphoproteomic patterns of multiple breast cancer (BC) and normal [...] Read more.

Understanding signaling patterns of transformation and controlling cell phenotypes is a challenge of current biology. Here we applied a cell State Transition Assessment and Regulation (cSTAR) approach to a perturbation dataset of single cell phosphoproteomic patterns of multiple breast cancer (BC) and normal breast tissue-derived cell lines. Following a separation of luminal, basal, and normal cell states, we identified signaling nodes within core control networks, delineated causal connections, and determined the primary drivers underlying oncogenic transformation and transitions across distinct BC subtypes. Whereas cell lines within the same BC subtype have different mutational and expression profiles, the architecture of the core network was similar for all luminal BC cells, and mTOR was a main oncogenic driver. In contrast, core networks of basal BC were heterogeneous and segregated into roughly four major subclasses with distinct oncogenic and BC subtype drivers. Likewise, normal breast tissue cells were separated into two different subclasses. Based on the data and quantified network topologies, we derived mechanistic cSTAR models that serve as digital cell twins and allow the deliberate control of cell movements within a Waddington landscape across different cell states. These cSTAR models suggested strategies of normalizing phosphorylation networks of BC cell lines using small molecule inhibitors. Full article

(This article belongs to the Special Issue Adaptive Resistance to Targeted Cancer Therapies and Rational Development of Combination Therapies)

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21 pages, 6738 KiB

Open AccessArticle

Identifying Key Regulatory Genes in Drug Resistance Acquisition: Modeling Pseudotime Trajectories of Breast Cancer Single-Cell Transcriptome

by Keita Iida and Mariko Okada

Cancers 2024, 16(10), 1884; https://doi.org/10.3390/cancers16101884 - 15 May 2024

Cited by 2 | Viewed by 2177

Abstract

Single-cell RNA-sequencing (scRNA-seq) technology has provided significant insights into cancer drug resistance at the single-cell level. However, understanding dynamic cell transitions at the molecular systems level remains limited, requiring a systems biology approach. We present an approach that combines mathematical modeling with a [...] Read more.

Single-cell RNA-sequencing (scRNA-seq) technology has provided significant insights into cancer drug resistance at the single-cell level. However, understanding dynamic cell transitions at the molecular systems level remains limited, requiring a systems biology approach. We present an approach that combines mathematical modeling with a pseudotime analysis using time-series scRNA-seq data obtained from the breast cancer cell line MCF-7 treated with tamoxifen. Our single-cell analysis identified five distinct subpopulations, including tamoxifen-sensitive and -resistant groups. Using a single-gene mathematical model, we discovered approximately 560–680 genes out of 6000 exhibiting multistable expression states in each subpopulation, including key estrogen-receptor-positive breast cancer cell survival genes, such as RPS6KB1. A bifurcation analysis elucidated their regulatory mechanisms, and we mapped these genes into a molecular network associated with cell survival and metastasis-related pathways. Our modeling approach comprehensively identifies key regulatory genes for drug resistance acquisition, enhancing our understanding of potential drug targets in breast cancer. Full article

(This article belongs to the Special Issue Adaptive Resistance to Targeted Cancer Therapies and Rational Development of Combination Therapies)

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16 pages, 6044 KiB

Open AccessArticle

Reducing State Conflicts between Network Motifs Synergistically Enhances Cancer Drug Effects and Overcomes Adaptive Resistance

by Yunseong Kim, Sea Rom Choi and Kwang-Hyun Cho

Cancers 2024, 16(7), 1337; https://doi.org/10.3390/cancers16071337 - 29 Mar 2024

Viewed by 1300

Abstract

Inducing apoptosis in cancer cells is a primary goal in anti-cancer therapy, but curing cancer with a single drug is unattainable due to drug resistance. The complex molecular network in cancer cells causes heterogeneous responses to single-target drugs, thereby inducing an adaptive drug [...] Read more.

Inducing apoptosis in cancer cells is a primary goal in anti-cancer therapy, but curing cancer with a single drug is unattainable due to drug resistance. The complex molecular network in cancer cells causes heterogeneous responses to single-target drugs, thereby inducing an adaptive drug response. Here, we showed that targeted drug perturbations can trigger state conflicts between multi-stable motifs within a molecular regulatory network, resulting in heterogeneous drug responses. However, we revealed that properly regulating an interconnecting molecule between these motifs can synergistically minimize the heterogeneous responses and overcome drug resistance. We extracted the essential cellular response dynamics of the Boolean network driven by the target node perturbation and developed an algorithm to identify a synergistic combinatorial target that can reduce heterogeneous drug responses. We validated the proposed approach using exemplary network models and a gastric cancer model from a previous study by showing that the targets identified with our algorithm can better drive the networks to desired states than those with other control theories. Of note, our approach suggests a new synergistic pair of control targets that can increase cancer drug efficacy to overcome adaptive drug resistance. Full article

(This article belongs to the Special Issue Adaptive Resistance to Targeted Cancer Therapies and Rational Development of Combination Therapies)

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