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Molecular Mechanisms and Therapies of Lung Cancer

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

Deadline for manuscript submissions: 15 June 2024 | Viewed by 5668

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Special Issue Editors

Prof. Dr. Andrzej Swierniak

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Guest Editor

Department of Systems Biology and Engineering, Silesian University of Technology, 44-100 Gliwice, Poland
Interests: lung cancer; molecular mechanisms and biomarkers; omics in lung cancer; data analysis and integration of molecular; clinical and radiomic data; molecular targets in lung cancer; molecular therapies; next-generation sequencing; mathematical modeling

Prof. Dr. Marek Kimmel

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Guest Editor

Department of Statistics and Bioengineering, Rice University, Houston, TX 77005, USA
Interests: lung cancer; molecular mechanisms and biomarkers; omics in lung cancer; data analysis and integration of molecular; clinical and radiomic data; molecular targets in lung cancer; molecular therapies; next-generation sequencing; mathematical modeling

Special Issue Information

Dear Colleagues,

The aim of the Special Issue is to highlight interdisciplinary research that employs methods of molecular biology and oncology on one side, and machine learning, data analysis, mathematical modeling, and systems biology on the other, with a firm foundation of clinical and molecular data. It will present a new framework for data integration and analysis to support interdisciplinary research in the area of molecular mechanisms of lung cancer, and will lead to progress in developing more efficient molecular biology-based therapies. While tobacco smoking is recognized as the primary cause of lung cancer, it can also be attributed to other environment factors. The high heterogeneity of lung cancer is mainly related to the molecular mechanisms that constitute its background. We expect that the results presented in this Special Issue will show how molecular data can be effectively used for the diagnosis and prediction of progress in lung cancer research. The search for accurate prognostic biomarkers in lung cancer, especially in the context of metastasis, is hindered by its high heterogeneity and complexity. Clinical and molecular characteristics have shown promise for the prediction of metastasis. For example, MALAT-1, a long non-coding RNA, was demonstrated to be significantly associated with metastasis in lung cancer. CA125 and NSE were found to be indicative of liver metastasis. For EGFR-mutant lung cancer patients, vimentin expression was identified as a potential predictor of brain metastasis. As molecular diagnostics becomes routinely available, targeted therapies, aimed at EGFR, FGFR, ALK, or KRAS mutations, can be used among mutation carriers. Papers that report results and progress in the development of these and similar types of therapies are of special interests for this Special Issue. Since IJMS is a journal of molecular science, clinical submissions with biomolecular experiments are welcome.

Prof. Dr. Andrzej Swierniak
Prof. Dr. Marek Kimmel
Guest Editors

Manuscript Submission Information

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Keywords

lung cancer
molecular mechanisms
multiomic data
mathematical modeling
molecular therapies

Published Papers (4 papers)

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Research

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17 pages, 2968 KiB

Open AccessArticle

Multiomics-Based Feature Extraction and Selection for the Prediction of Lung Cancer Survival

by Roman Jaksik, Kamila Szumała, Khanh Ngoc Dinh and Jarosław Śmieja

Int. J. Mol. Sci. 2024, 25(7), 3661; https://doi.org/10.3390/ijms25073661 - 25 Mar 2024

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Abstract

Lung cancer is a global health challenge, hindered by delayed diagnosis and the disease’s complex molecular landscape. Accurate patient survival prediction is critical, motivating the exploration of various -omics datasets using machine learning methods. Leveraging multi-omics data, this study seeks to enhance the accuracy of survival prediction by proposing new feature extraction techniques combined with unbiased feature selection. Two lung adenocarcinoma multi-omics datasets, originating from the TCGA and CPTAC-3 projects, were employed for this purpose, emphasizing gene expression, methylation, and mutations as the most relevant data sources that provide features for the survival prediction models. Additionally, gene set aggregation was shown to be the most effective feature extraction method for mutation and copy number variation data. Using the TCGA dataset, we identified 32 molecular features that allowed the construction of a 2-year survival prediction model with an AUC of 0.839. The selected features were additionally tested on an independent CPTAC-3 dataset, achieving an AUC of 0.815 in nested cross-validation, which confirmed the robustness of the identified features. Full article

(This article belongs to the Special Issue Molecular Mechanisms and Therapies of Lung Cancer)

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13 pages, 475 KiB

Open AccessArticle

Mathematical Model of Intrinsic Drug Resistance in Lung Cancer

by Emilia Kozłowska and Andrzej Swierniak

Int. J. Mol. Sci. 2023, 24(21), 15801; https://doi.org/10.3390/ijms242115801 - 31 Oct 2023

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Abstract

Drug resistance is a bottleneck in cancer treatment. Commonly, a molecular treatment for cancer leads to the emergence of drug resistance in the long term. Thus, some drugs, despite their initial excellent response, are withdrawn from the market. Lung cancer is one of the most mutated cancers, leading to dozens of targeted therapeutics available against it. Here, we have developed a mechanistic mathematical model describing sensitization to nine groups of targeted therapeutics and the emergence of intrinsic drug resistance. As we focus only on intrinsic drug resistance, we perform the computer simulations of the model only until clinical diagnosis. We have utilized, for model calibration, the whole-exome sequencing data combined with clinical information from over 1000 non-small-cell lung cancer patients. Next, the model has been applied to find an answer to the following questions: When does intrinsic drug resistance emerge? And how long does it take for early-stage lung cancer to grow to an advanced stage? The results show that drug resistance is inevitable at diagnosis but not always detectable and that the time interval between early and advanced-stage tumors depends on the selection advantage of cancer cells. Full article

(This article belongs to the Special Issue Molecular Mechanisms and Therapies of Lung Cancer)

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16 pages, 882 KiB

Open AccessArticle

Carbonic Anhydrase IX Suppression Shifts Partial Response to Checkpoint Inhibitors into Complete Tumor Eradication: Model-Based Investigation

by Julia Grajek and Jan Poleszczuk

Int. J. Mol. Sci. 2023, 24(12), 10068; https://doi.org/10.3390/ijms241210068 - 13 Jun 2023

Cited by 1 | Viewed by 1081

Abstract

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of solid malignancies, including non-small-cell lung cancer. However, immunotherapy resistance constitutes a significant challenge. To investigate carbonic anhydrase IX (CAIX) as a driver of resistance, we built a differential equation model of tumor–immune interactions. The model considers treatment with the small molecule CAIX inhibitor SLC-0111 in combination with ICIs. Numerical simulations showed that, given an efficient immune response, CAIX KO tumors tended toward tumor elimination in contrast to their CAIX-expressing counterparts, which stabilized close to the positive equilibrium. Importantly, we demonstrated that short-term combination therapy with a CAIX inhibitor and immunotherapy could shift the asymptotic behavior of the original model from stable disease to tumor eradication. Finally, we calibrated the model with data from murine experiments on CAIX suppression and combination therapy with anti-PD-1 and anti-CTLA-4. Concluding, we have developed a model that reproduces experimental findings and enables the investigation of combination therapies. Our model suggests that transient CAIX inhibition may induce tumor regression, given a sufficient immune infiltrate in the tumor, which can be boosted with ICIs. Full article

(This article belongs to the Special Issue Molecular Mechanisms and Therapies of Lung Cancer)

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Review

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21 pages, 501 KiB

Open AccessReview

Mathematical Modeling Support for Lung Cancer Therapy—A Short Review

by Jaroslaw Smieja

Int. J. Mol. Sci. 2023, 24(19), 14516; https://doi.org/10.3390/ijms241914516 - 25 Sep 2023

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Abstract

The paper presents a review of models that can be used to describe dynamics of lung cancer growth and its response to treatment at both cell population and intracellular processes levels. To address the latter, models of signaling pathways associated with cellular responses to treatment are overviewed. First, treatment options for lung cancer are discussed, and main signaling pathways and regulatory networks are briefly reviewed. Then, approaches used to model specific therapies are discussed. Following that, models of intracellular processes that are crucial in responses to therapies are presented. The paper is concluded with a discussion of the applicability of the presented approaches in the context of lung cancer. Full article

(This article belongs to the Special Issue Molecular Mechanisms and Therapies of Lung Cancer)

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