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Advancements in Preclinical Models for Solid Cancers

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 24259

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Special Issue Editors

Dr. Ewa Krawczyk

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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

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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:

Recent advances in preclinical cancer models for solid cancers;
Development and implementation of patient-derived models for solid cancers;
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 250 words) can be sent to the Editorial Office for assessment.

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 (11 papers)

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Research

Jump to: Review

19 pages, 7655 KB

Open AccessArticle

DeepGene-BC: Deep Learning-Based Breast Cancer Subtype Prediction via Somatic Point Mutation Profiles

by Pengfei Hou, Liangjie Liu, Yijia Duan, Shanshan Yin, Wenqian Yan, Chongchen Pang, Yang Yan, Sabreena Aziz, Mika Torhola, Henna Kujanen, Klaus Förger, Hui Shi, Guang He and Yi Shi

Cancers 2026, 18(4), 570; https://doi.org/10.3390/cancers18040570 - 9 Feb 2026

Viewed by 818

Abstract

Background: Molecular subtyping of breast cancer usually relies on transcriptomic profiles, a method constrained by limitations in robustness and clinical applicability. While somatic point mutations represent a stable genomic alternative, their predictive utility is hindered by high dimensionality, extreme sparsity, and weak single-gene associations. Methods: Here, we present deepGene-BC, a deep learning framework that synergizes a pathway-informed feature selection strategy with a hybrid neural network tailored for sparse binary data. To distill sparse genome-wide mutations into a compact and interpretable feature set, deepGene-BC integrates mutation recurrence filtering, curated pathway priors, and mutual information-based gene prioritization. These refined features are subsequently modeled using a specialized hybrid architecture designed to capture complex linear effects, feature interactions, and higher-order nonlinear patterns. Results: When benchmarked against an independent test set (n = 273) from the TCGA breast cancer cohort, deepGene-BC achieved an overall accuracy of 77.3% and an average sensitivity of 75.2%, accompanied by a strong overall discriminative performance (macro-averaged AU-ROC = 0.94, 95% CI: 0.92–0.96). Conclusions: By effectively combining biologically informed feature engineering with deep learning, deepGene-BC holds significant promise for non-invasive molecular stratification and precision oncology. Full article

(This article belongs to the Special Issue Advancements in Preclinical Models for Solid Cancers)

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17 pages, 2392 KB

Open AccessArticle

Predicting Radiotherapy Outcomes with Deep Learning Models Through Baseline and Adaptive Simulation Computed Tomography in Patients with Pharyngeal Cancer

by Kuo-Chen Wu, Shang-Wen Chen, Yuan-Yen Chang, Yao-Ching Wang, Ying-Chun Lin, Chao-Jen Chang, Zong-Kai Hsu, Ruey-Feng Chang and Chia-Hung Kao

Cancers 2025, 17(21), 3492; https://doi.org/10.3390/cancers17213492 - 30 Oct 2025

Cited by 1 | Viewed by 1043

Abstract

Background/Objectives: The implementation of adaptive radiation therapy (ART) is increasingly becoming widely available in the clinical practice of radiotherapy (RT). For patients with pharyngeal cancer receiving RT, this study aimed to develop a deep learning (DL) model by merging baseline and ART simulation computed tomography (CT) images to predict treatment outcomes. Methods: Clinical and imaging data from 162 patients of newly diagnosed oropharyngeal or hypopharyngeal cancer were analyzed. All completed definitive treatment and their baseline and ART non-contrast simulation CTs were utilized for training. After augmentation of the CT images, a deep contrastive learning model was employed to predict the occurrence of local recurrence (LR), neck lymph node relapse (NR), and distant metastases (DM). Receiver operating characteristic curve analysis was conducted to evaluate the model’s performance. Results: Over a median follow-up period of 34 months, 53 (32.7%), 36 (22.2%), and 23 (14.0%) patients developed LR, NR, and DM, respectively. Following the integration of prediction results from baseline and ART simulation CTs, the area under the curve for predicting the occurrence of LR, NR, and DM reached 0.773, 0.747, and 0.793. At the same time, the accuracy for the three endpoints was 72.4%, 74.7%, and 75.7%, respectively. Conclusions: For patients with pharyngeal cancer ready to receive RT-based treatment, our proposed models can predict the development of LR, NR, or DM through baseline and ART simulation CTs. External validation needs to be conducted to confirm the model’s performance. Full article

(This article belongs to the Special Issue Advancements in Preclinical Models for Solid Cancers)

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Review

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26 pages, 1840 KB

Open AccessReview

Human-Centric Modeling in Metastatic Breast Cancer: Organoids, Organ-on-Chip Systems, and New Approach Methodologies in the Post-FDA Modernization Act 2.0 Era

by Hissah Alatawi, Haritha H. Nair, Asif Raza, Emiliana Velez, Arun K. Sharma and Satya Narayan

Cancers 2026, 18(7), 1166; https://doi.org/10.3390/cancers18071166 - 4 Apr 2026

Viewed by 878

Abstract

Metastatic breast cancer (MBC) remains an overwhelming clinical challenge due to its inherent clonal evolution and the frequent development of drug resistance. A significant hurdle in therapeutic discovery is the reliance on traditional 2D cell cultures and animal models, which often fail to accurately replicate human tumor pathophysiology or predict clinical responses. Consequently, the field of oncology is increasingly exploring a transition towards human-centric research that prioritizes biological data derived directly from patients. Considering the FDA Modernization Act 2.0 and the 2025 FDA Roadmap, frameworks are being established to explore the integration of new approach methodologies (NAMs)—including patient-derived organoids (PDOs) and organ-on-a-chip (OoC) systems—into the drug development pipeline. This review examines how these platforms aim to better simulate the human physiological environment by capturing the complex architecture and microenvironment of the tumor. We further discuss how the integration of these models with Artificial Intelligence (AI), spatial multi-omics, and real-time liquid biopsies is being investigated to enhance the speed and precision of therapeutic testing. While still in the translational phase, emerging evidence suggests that human-centric platforms may eventually support rapid functional drug screening, potentially informing patient treatment responses within clinically relevant timeframes. Strengthening the biological link between the patient and their longitudinal data represents a promising strategy to address the complexities of MBC and improve clinical outcomes. These human-centric platforms preserve patient-specific tumor heterogeneity, recapitulate microenvironmental interactions, and enable functional drug testing under physiologically relevant conditions, thereby improving translational accuracy compared to conventional models. Full article

(This article belongs to the Special Issue Advancements in Preclinical Models for Solid Cancers)

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16 pages, 956 KB

Open AccessReview

A Unique Protein Adjuvant for Precision Immunotherapy to Prevent Recurrence of Surgically Resected Colorectal Cancer

by Yasuhiro Suzuki, Rajesh Mani and B. Mark Evers

Cancers 2026, 18(6), 1003; https://doi.org/10.3390/cancers18061003 - 20 Mar 2026

Viewed by 595

Abstract

Effectively activating protective CD8⁺ T cell immunity specifically against cancer antigens is an important pathway to prevent the growth of various types of cancers. A major obstacle in this approach is variations in cancer antigens among patients. A valuable material to overcome the antigen variation among cancer patients is the use of each individual’s own cancer cells for immunization. In colorectal cancer (CRC), approximately one-third of the patients who receive curative surgical resection have a recurrence of cancer. Therefore, the use of surgically resected CRC for immunotherapy to specifically activate the protective CD8⁺ T cells against their own cancer cells is a valuable approach to prevent the recurrence of cancer. However, since cancer-specific antigens are often not strongly immunogenic, a potent immunostimulant is required as an adjuvant for efficiently facilitating the activation of cancer-specific protective CD8⁺ T cells. We recently identified that a protein molecule, the amino-terminus region of the dense granule protein 6 (GRA6Nt) of Toxoplasma gondii, selectively activates innate expressions of IFN-γ and IL-18 and functions as a powerful adjuvant when used in immunization with nonreplicable (treated with mitomycin C or irradiated) MC38 CRC cells to potently activate the cytotoxic activity and IFN-γ production of CD8⁺ T cells against cancer cells. In addition, immunization using the GRA6Nt protein adjuvant effectively inhibits the growth of identical CRC cells after its challenge implantation, which mimics a recurrence of the surgically resected CRC used for the immunizations. In contrast to the two nucleotide- or deoxynucleotide-based Toll-like receptor agonists currently being used as adjuvants in cancer immunotherapy in clinical settings, GRA6Nt is a protein molecule. Thus, the rGRA6Nt protein adjuvant provides a new pathway in cancer immunotherapy to effectively activate the protective CD8⁺ T cells specific for the individual’s cancer cells to prevent the recurrence of surgically resected CRC in patients. Full article

(This article belongs to the Special Issue Advancements in Preclinical Models for Solid Cancers)

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19 pages, 1992 KB

Open AccessReview

Advancing the Study of Glioblastoma Through 3D Tumor Models

by Karen Salmeron-Moreno, Josephine Buclez, Chris Donghyun Kim, Karthik Papisetty, Thomas McCaffery, Fadi Jacob, Rommi Kashlan, Hithardhi Duggireddy, Karthik Valiveti, Justin Maldonado, Gustavo Pradilla and Tomas Garzon-Muvdi

Cancers 2026, 18(4), 668; https://doi.org/10.3390/cancers18040668 - 18 Feb 2026

Viewed by 1420

Abstract

Glioblastoma (GBM), the most aggressive primary brain malignancy, remains a challenge to experimentally model. Accurately modeling the intra- and intertumoral heterogeneity of GBMs is essential for enhancing the predictive power of preclinical models and improving the effectiveness of current therapies. This review highlights recent advances in 3D tumor modeling, which accurately replicate the structural, cellular, and biochemical complexity of GBMs. We examine their translational potential and discuss current barriers to clinical translation. Full article

(This article belongs to the Special Issue Advancements in Preclinical Models for Solid Cancers)

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16 pages, 2728 KB

Open AccessReview

Advancements in Preclinical Models for NF2-Related Schwannomatosis Research

by Bo-Shi Zhang, Simeng Lu, Scott R. Plotkin and Lei Xu

Cancers 2026, 18(2), 224; https://doi.org/10.3390/cancers18020224 - 11 Jan 2026

Viewed by 1123

Abstract

NF2-related Schwannomatosis (NF2-SWN) remains a disorder with few effective treatment options. Patients develop vestibular schwannomas (VSs) on both auditory nerves, which gradually impair hearing and often result in significant communication difficulties, social withdrawal, and higher rates of depression. Progress in understanding NF2-SWN biology and translating discoveries into therapies has been slowed by the absence of robust animal models that faithfully reproduce both tumor behavior and the associated neurological deficits. In this review, we summarized the development of animal models that not only reproduce tumor growth in the peripheral nerve microenvironment but also reproduce tumor-induced neurological symptoms, such as hearing loss and ataxia. We further highlight the currently available organotypic models for NF2-SWN. Together, these systems provide an essential foundation for advancing mechanistic studies and accelerating the development of effective therapies for this devastating disorder. Full article

(This article belongs to the Special Issue Advancements in Preclinical Models for Solid Cancers)

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22 pages, 1759 KB

Open AccessReview

Tumour-on-Chip Models for the Study of Ovarian Cancer: Current Challenges and Future Prospects

by Sung Yeon Lim, Lamia Sabry Aboelnasr and Mona El-Bahrawy

Cancers 2025, 17(19), 3239; https://doi.org/10.3390/cancers17193239 - 6 Oct 2025

Cited by 2 | Viewed by 2022

Abstract

Ovarian cancer is a highly lethal malignancy, characterised by late-stage diagnosis, marked inter- and intra-tumoural heterogeneity, and frequent development of chemoresistance. Existing preclinical models, including conventional two-dimensional cultures, three-dimensional spheroids, and organoids, only partially recapitulate the structural and functional complexity of the ovarian tumour microenvironment (TME). Tumour-on-chip (CoC) technology has emerged as a promising alternative, enabling the co-culture of tumour and stromal cells within a microengineered platform that incorporates relevant extracellular matrix components, biochemical gradients, and biomechanical cues under precisely controlled microfluidic conditions. This review provides a comprehensive overview of CoC technology relevant to ovarian cancer research, outlining fabrication strategies, device architectures, and TME-integration approaches. We systematically analyse published ovarian cancer-specific CoC models, revealing a surprisingly limited number of studies and a lack of standardisation across design parameters, materials, and outcome measures. Based on these findings, we identify critical technical and biological considerations to inform the rational design of next-generation CoC platforms, with the aim of improving their reproducibility, translational value, and potential for personalised medicine applications. Full article

(This article belongs to the Special Issue Advancements in Preclinical Models for Solid Cancers)

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20 pages, 1358 KB

Open AccessReview

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

Viewed by 2212

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 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

Open AccessReview

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

Cited by 11 | Viewed by 2962

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 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

Open AccessReview

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

Cited by 1 | Viewed by 4164

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 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

Open AccessReview

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 5 | Viewed by 6009

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 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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