Personalized Medicine in Lung Cancer: Diagnosis, Treatment and Prognosis

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Mechanisms of Diseases".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 10069

Special Issue Editor


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Guest Editor
Department of Chemistry, National and Kapodistrian University of Athens, 15771 Athens, Greece
Interests: NSCLC; liquid biopsy; cfDNA; MRD; CTCs; heterogeneity; EMT; TKI
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Special Issue Information

Dear Colleagues,

Lung cancer is the most common cause of cancer-related mortality worldwide. Although new therapeutic strategies are defined according to the molecular profile of the tissue, a high rate of these targeted therapies fail to cure the disease because of several biological and technological challenges, mainly the heterogeneity of the tumor. The purpose of this Special Issue, Personalized Medicine in Lung Cancer: Diagnosis, Treatment and Prognosis, is to highlight: 1) the benefit of personalized medicine in the diagnosis, treatment, and prognosis of lung cancer patients; 2) the new assays that could achieve high sensitivity for early detection of minimal residual disease (MRD) in lung cancer patients; 3) the crucial role of liquid biopsies in personalized medicine in lung cancer; and 4) the spatial heterogeneity  as well as temporal heterogeneity between the primary tumor and local or distant recurrences in the same patient. Finally, we also wish to present the rapid development of technology and drugs in this area of research.

Dr. Athina N. Markou
Guest Editor

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Keywords

  • personalized medicine
  • heterogeneity of lung cancer
  • minimal residual disease
  • liquid biopsy
  • targeted therapies

Published Papers (3 papers)

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Research

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13 pages, 1211 KiB  
Article
Prognostic Value of Dual-Time-Point [18F]FDG PET/CT for Predicting Distant Metastasis after Treatment in Patients with Non-Small Cell Lung Cancer
by Sang Mi Lee, Jeong Won Lee, Ji-Hyun Lee, In Young Jo and Su Jin Jang
J. Pers. Med. 2022, 12(4), 592; https://doi.org/10.3390/jpm12040592 - 7 Apr 2022
Cited by 4 | Viewed by 1753
Abstract
This study aimed to evaluate the prognostic significance of 2-Deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) uptake in the bone marrow (BM) and primary tumors on dual-time-point (DTP) PET/CT for predicting progression-free survival (PFS) and distant metastasis-free survival (DMFS) in patients with non-small cell [...] Read more.
This study aimed to evaluate the prognostic significance of 2-Deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) uptake in the bone marrow (BM) and primary tumors on dual-time-point (DTP) PET/CT for predicting progression-free survival (PFS) and distant metastasis-free survival (DMFS) in patients with non-small cell lung cancer (NSCLC). We retrospectively analyzed DTP [18F]FDG PET/CT images from 211 patients with NSCLC. The maximum standardized uptake value (SUV) of primary lung cancer and mean [18F]FDG uptake of the BM (BM SUV) were measured from early and delayed PET/CT images, and the percent changes in these parameters (∆maximum SUV and ∆BM SUV) were calculated. On multivariate survival analysis, the maximum SUV and BM SUV on both early and delayed PET/CT scans were significantly associated with PFS, while the ∆maximum SUV and ∆BM SUV failed to show statistical significance. For DMFS, the ∆maximum SUV and ∆BM SUV were independent predictors along with the TNM stage. Distant progression was observed only in 1.3% of patients with low ∆maximum SUV and ∆BM SUV, whereas 28.2% of patients with high ∆maximum SUV and ∆BM SUV experienced distant progression. The ∆maximum SUV and ∆BM SUV on DTP [18F]FDG PET/CT were significant independent predictors for DMFS in patients with NSCLC. Full article
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15 pages, 4356 KiB  
Article
Cancer Stem Cell-Like Circulating Tumor Cells Are Prognostic in Non-Small Cell Lung Cancer
by Eva Obermayr, Nina Koppensteiner, Nicole Heinzl, Eva Schuster, Barbara Holzer, Hannah Fabikan, Christoph Weinlinger, Oliver Illini, Maximilian Hochmair and Robert Zeillinger
J. Pers. Med. 2021, 11(11), 1225; https://doi.org/10.3390/jpm11111225 - 18 Nov 2021
Cited by 16 | Viewed by 2744
Abstract
Despite recent advances in the treatment of non-small cell lung cancer (NSCLC), less than 10% of patients survive the first five years when the disease has already spread at primary diagnosis. Methods: Blood samples were taken from 118 NSCLC patients at primary diagnosis [...] Read more.
Despite recent advances in the treatment of non-small cell lung cancer (NSCLC), less than 10% of patients survive the first five years when the disease has already spread at primary diagnosis. Methods: Blood samples were taken from 118 NSCLC patients at primary diagnosis or at progression of the disease before the start of a new treatment line and enriched for circulating tumor cells (CTCs) by microfluidic Parsortix™ (Angle plc, Guildford GU2 7AF, UK) technology. The gene expression of epithelial cancer stem cell (CSC), epithelial to mesenchymal (EMT), and lung-related markers was assessed by qPCR, and the association of each marker with overall survival (OS) was evaluated using log-rank tests. Results: EpCAM was the most prevalent transcript, with 53.7% positive samples at primary diagnosis and 25.6% at recurrence. EpCAM and CK19, as well as NANOG, PROM1, TERT, CDH5, FAM83A, and PTHLH transcripts, were associated with worse OS. However, only the CSC-specific NANOG and PROM1 were related to the outcome both at primary diagnosis (NANOG: HR 3.21, 95%CI 1.02–10.14, p = 0.016; PROM1: HR 4.23, 95% CI 0.65–27.56, p = 0.007) and disease progression (NANOG: HR 4.17, 95%CI 0.72–24.14, p = 0.025; PROM1: HR 4.77, 95% CI 0.29–78.94, p = 0.032). Conclusions: The present study further underlines the relevance of the molecular characterization of CTCs. Our multi-marker analysis highlighted the prognostic value of cancer stem cell-related transcripts at primary diagnosis and disease progression. Full article
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Review

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36 pages, 1280 KiB  
Review
Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges
by Francisco Silva, Tania Pereira, Inês Neves, Joana Morgado, Cláudia Freitas, Mafalda Malafaia, Joana Sousa, João Fonseca, Eduardo Negrão, Beatriz Flor de Lima, Miguel Correia da Silva, António J. Madureira, Isabel Ramos, José Luis Costa, Venceslau Hespanhol, António Cunha and Hélder P. Oliveira
J. Pers. Med. 2022, 12(3), 480; https://doi.org/10.3390/jpm12030480 - 16 Mar 2022
Cited by 22 | Viewed by 4857
Abstract
Advancements in the development of computer-aided decision (CAD) systems for clinical routines provide unquestionable benefits in connecting human medical expertise with machine intelligence, to achieve better quality healthcare. Considering the large number of incidences and mortality numbers associated with lung cancer, there is [...] Read more.
Advancements in the development of computer-aided decision (CAD) systems for clinical routines provide unquestionable benefits in connecting human medical expertise with machine intelligence, to achieve better quality healthcare. Considering the large number of incidences and mortality numbers associated with lung cancer, there is a need for the most accurate clinical procedures; thus, the possibility of using artificial intelligence (AI) tools for decision support is becoming a closer reality. At any stage of the lung cancer clinical pathway, specific obstacles are identified and “motivate” the application of innovative AI solutions. This work provides a comprehensive review of the most recent research dedicated toward the development of CAD tools using computed tomography images for lung cancer-related tasks. We discuss the major challenges and provide critical perspectives on future directions. Although we focus on lung cancer in this review, we also provide a more clear definition of the path used to integrate AI in healthcare, emphasizing fundamental research points that are crucial for overcoming current barriers. Full article
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