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Cancers
Special Issues
Advancements in Preclinical Models for Solid Cancers
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Advancements in Preclinical Models for Solid Cancers

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Clinical Research of Cancer".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 4000

Special Issue Editors

Dr. Ewa Krawczyk

E-Mail Website
Guest Editor
Department of Pathology, Center for Cell Reprogramming, Georgetown University Medical Center, Washington, DC 20057, USA
Interests: cancer research; cell conditional reprogramming; in vitro models; preclinical disease models; cell culture; rare cancers; HPV; kidney disease
Dr. Paula Dobosz

E-Mail Website
Guest Editor
Department of Pathomorphology and Clinical Immunology, Poznan University of Medical Sciences, 60-355 Poznan, Poland
Interests: cancer; cancer genetics; cancer genomics; cancer miRNA; cancer biomarkers; pancreas; cancer immunotherapy; immunotherapy

Special Issue Information

Dear Colleagues,

We are pleased to introduce and invite you to a Special Issue that is focused on the latest developments in preclinical modeling of various types of solid cancers. Cancer is a major health burden globally and one of the leading causes of death worldwide. There is an urgent need for novel and effective preclinical methods, both in vitro and in vivo, that can model the disease at any stage, including primary and metastatic cancers. New advanced preclinical models can aid in cancer diagnosis, drug screening and development, and personalized treatment strategies.

Cancer research currently depends on reliable preclinical models that are commonly used in all areas of basic and translational research, including studies on mechanisms of tumorigenesis, the cancer microenvironment, metastasis, molecular biology, structural biology, epigenetics, medicinal chemistry, and precision medicine, to name but a few. However, no preclinical model is ideal and all of them have various limitations to a lesser or greater extent. Thus, there is an urgent need for the development of new models that recapitulate particular stages of various solid cancers and closely resemble pathology and the course of these malignancies in humans.

This Special Issue aims to collect manuscripts describing advances in the development of preclinical models that can provide valuable information for researchers and clinicians strategizing new therapeutic/diagnostic approaches. Original research articles and reviews are both welcome. Research areas may include (but are not limited to) the following:

  1. Recent advances in preclinical cancer models for solid cancers;
  2. Development and implementation of patient-derived models for solid cancers;
  3. Preclinical solid cancer models as a tool in precision and personalized medicine.

I look forward to receiving your contributions.

Dr. Ewa Krawczyk
Dr. Paula Dobosz
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 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. Cancers 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 2900 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

  • solid cancers
  • preclinical cancer models
  • in vitro cancer models
  • in vivo cancer models
  • translational cancer research
  • strengths and limitations of preclinical cancer models

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Published Papers (4 papers)

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Review

20 pages, 1358 KB  
Review
Conditionally Reprogrammed Cells as Preclinical Model for Rare Cancers
by Ewa Krawczyk
Cancers 2025, 17(17), 2834; https://doi.org/10.3390/cancers17172834 - 29 Aug 2025
Abstract
Despite their disadvantages, preclinical models in vitro are still crucial for every area of biomedical science. They remain a necessary basis for biological, biochemical, and mechanistic studies of pathophysiology of human disease, evaluation of diagnostic tests, assessment of vaccines, as well as screening [...] Read more.
Despite their disadvantages, preclinical models in vitro are still crucial for every area of biomedical science. They remain a necessary basis for biological, biochemical, and mechanistic studies of pathophysiology of human disease, evaluation of diagnostic tests, assessment of vaccines, as well as screening of potential and repurposed drugs before they are adapted to clinical use. In contrast to animal models in vivo, preclinical in vitro models are cost and time effective. They are easier to use, and, in most cases, they are not associated with ethical concerns. Therefore, they are extensively used in cancer research. Conditional cell reprogramming (CCR) has been one of the novel technologies utilized as a preclinical model in vitro for various common cancers and other diseases. It may be even more important for the research related to rare cancers—elusive, difficult to study, and with insufficient number of relevant models available. Applications of this technology for the basic and translational studies of rare cancers are described in this article. Evaluation of the mechanisms of tumorigenicity and metastasis in neuroblastoma, neuroendocrine cervical carcinoma, ependymoma and astrocytoma, as well as screening of potential drugs and other therapeutic approaches for the laryngeal and hypopharyngeal carcinoma and adenoid cystic carcinoma, demonstrate that the CCR technology is a potential reliable model for various aspects of rare cancer research in the future. Full article
(This article belongs to the Special Issue Advancements in Preclinical Models for Solid Cancers)
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22 pages, 1989 KB  
Review
Machine Learning Models for Predicting Gynecological Cancers: Advances, Challenges, and Future Directions
by Pankaj Garg, Madhu Krishna, Prakash Kulkarni, David Horne, Ravi Salgia and Sharad S. Singhal
Cancers 2025, 17(17), 2799; https://doi.org/10.3390/cancers17172799 - 27 Aug 2025
Abstract
Gynecological cancer, especially breast, cervical, and ovarian cancer, are significant health issues affecting women worldwide. When screened they are mostly detected at later stages because of non-specific signs and symptoms as well as the unavailability of reliable screening methods. The improvement of early [...] Read more.
Gynecological cancer, especially breast, cervical, and ovarian cancer, are significant health issues affecting women worldwide. When screened they are mostly detected at later stages because of non-specific signs and symptoms as well as the unavailability of reliable screening methods. The improvement of early oncologic prediction methods is therefore needed to work out the survival rates, guide individualized treatment, and relieve healthcare pressures. Outcome forecasting and clinical detection are rapidly changing with the use of machine learning (ML), one of the promising technologies used to analyze complex biomedical data. Artificial intelligence (AI)-based ML models are capable of determining low-level trends and making accurate predictions of disease risk and outcomes, because they can combine different datasets (clinical records, genomics, proteomics, medical imaging) and learn to identify subtle patterns. Standard algorithms, including support vector machines, random forests, and deep learning (DL) models, such as convolutional neural networks, have demonstrated high potential in identifying the type of cancer, monitoring disease progression, and designing treatment patterns. This manuscript reviews the recent developments in the use of ML models to advance oncologic prediction tasks in gynecologic oncology. It reports on critical domains, like screening, risk classification, and survival modeling, as well as comments on difficulties, like data inconsistency, inability of interpretation of models, and issues of clinical interpretation. New developments, such as explainable AI, federated learning (FL), and multi-omics fusion, are discussed to develop these models and to make them applicable in practice because of their reliability. Conclusively, this article emphasizes the transformative role of ML in precision oncology to deliver improved, patient-centered outcomes to women who are victims of gynecological cancers. Full article
(This article belongs to the Special Issue Advancements in Preclinical Models for Solid Cancers)
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20 pages, 1321 KB  
Review
Regenerative Immunotherapy for Cancer: Transcription Factor Reprogramming of Tumor-Specific T Cells
by Tyler R. McCaw, Nicholas P. Restifo, Kathrin Plath and Joseph G. Crompton
Cancers 2025, 17(13), 2225; https://doi.org/10.3390/cancers17132225 - 2 Jul 2025
Viewed by 1195
Abstract
Cell-based immunotherapy is a promising treatment strategy for cancer. Particularly in the case of solid tumors, however, this strategy only benefits a minority of patients. A critical limitation to immunotherapy is T cell exhaustion, a terminal differentiation state characterized by loss of self-renewal [...] Read more.
Cell-based immunotherapy is a promising treatment strategy for cancer. Particularly in the case of solid tumors, however, this strategy only benefits a minority of patients. A critical limitation to immunotherapy is T cell exhaustion, a terminal differentiation state characterized by loss of self-renewal and cytotoxic capacity. For over a decade, regenerative immunology approaches to overcome exhaustion and restore stem-like features of T cells have been pursued. The reprogramming of tumor-specific T cells back to a less-differentiated, stem-like state using induced pluripotent stem cell (iPSC) technology has been viewed as a powerful and highly appealing strategy to overcome the limitations imposed by exhaustion. However, clinical translation of these approaches has been stymied by the requirement for subsequent iPSC-to-T cell re-maturation strategies, vanishingly low efficiencies, and resource-intensive cell culture protocols. In this review, we discuss the emergence of transcription factor reprogramming to iPSCs, contemporary techniques for T cell reprogramming, as well as techniques for re-differentiation into mature T cells. We discuss the potential clinical utility of T cell reprogramming and re-maturation strategies alongside progress and major roadblocks toward clinical translation. If these challenges can be addressed, transcription factor reprogramming of T cells into iPSCs and subsequent re-maturation into tumor-specific stem-like T cells may represent an incredibly efficacious approach to cancer immunotherapy. Full article
(This article belongs to the Special Issue Advancements in Preclinical Models for Solid Cancers)
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28 pages, 2571 KB  
Review
Advancing Antibody–Drug Conjugates: Precision Oncology Approaches for Breast and Pancreatic Cancers
by Dhanvin R. Yajaman, Youngman Oh, Jose G. Trevino and J. Chuck Harrell
Cancers 2025, 17(11), 1792; https://doi.org/10.3390/cancers17111792 - 27 May 2025
Cited by 1 | Viewed by 2161
Abstract
Background/Objectives: ADCs bring an innovative strategy to cancer treatment by conjugating powerful cytotoxic agents to the specificity of monoclonal antibodies. This review discusses recent advancements and challenges in the field of ADCs, along with future potential applications. Methods: Studies focused on the development [...] Read more.
Background/Objectives: ADCs bring an innovative strategy to cancer treatment by conjugating powerful cytotoxic agents to the specificity of monoclonal antibodies. This review discusses recent advancements and challenges in the field of ADCs, along with future potential applications. Methods: Studies focused on the development of ADCs were reviewed. These include the effects of payload improvements, linker technologies, antibody engineering, and ADC internalization, which were particular topics of examination regarding their role in pancreatic ductal adenocarcinoma (PDAC) and triple-negative breast cancer (TNBC). The efficacy of some ADCs for pancreatic and breast cancers was compared. Results: In TNBC, ADCs such as sacituzumab govitecan and trastuzumab deruxtecan have improved progression-free survival in advanced cases. In contrast, PDAC ADC development is challenged by low antigen density and poor internalization; despite evidence of target engagement in early trials targeting mesothelin and MUC1, ADCs for PDAC have yet to achieve significant clinical efficacy or regulatory approval. Conclusions: While ADCs have significantly advanced treatment options in TNBC, PDAC remains a difficult target due to its stroma-rich microenvironment and lack of high-density, tumor-specific antigens. This article emphasizes the need for tailor-made ADC designs to enhance results in various types of cancers and provides valuable insight into future advancements in precision oncology. Full article
(This article belongs to the Special Issue Advancements in Preclinical Models for Solid Cancers)
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