Innovative Methods, Models and Technologies Shaping the Path to Cancer Cures by 2035

The field of oncological research is currently confronting significant challenges in bridging the gap between preclinical studies and clinical outcomes. Traditional hypothesis-driven research, often utilizing classical methodologies such as PCR, western blotting, and murine models, is being overshadowed by advanced data-driven discovery techniques. These involve cutting-edge methods like single-cell sequencing and multi-omics approaches, powered by AI and complex data analytics. Although these innovations promise enhanced accuracy, they also present challenges. These include potential data misinterpretation, overfitting models to available data, and demand for substantial expertise in mathematics and statistics, often excluding traditional oncologists, immunologists, and other biologists from the forefront of innovation.

This Research Topic aims to explore novel biological systems and cutting-edge tools that could improve translational accuracy and accelerate the development of cancer therapies. Contributions should address how next-generation methods and models help mimic human tumor biology, evaluate mechanisms of action, or predict clinical responses — more accurately, efficiently, and cost-effectively. This Topic also seeks to highlight advances in experimental platforms supporting the development of promising modalities such as cancer vaccines, adoptive T cell therapies, bispecific antibodies, TCR therapies, and many others. By capturing state-of-the-art approaches, this collection aims to define what the next decade of cancer drug development may look like. We are particularly interested in how these systems integrate with modern assay platforms (e.g., imaging, high-dimensional flow cytometry, multi-omics), functional immune reagents, and high-throughput screening.

We welcome original research articles, reviews, methods papers, perspectives, and opinion pieces that address any of the following topics:

- Novel cancer models (e.g., organoids, patient-derived xenografts, humanized mice)
- Tools and workflows enabling immune-oncology studies
- Integration of imaging, proteomics, genomics, or spatial technologies
- Application of phase 0 or micro-dosing trials in oncology
- High-content assay platforms for mechanistic or functional evaluation
- Translational platforms for cancer vaccines, ACT, bispecifics, or TCR therapies
- Benchmarking of models and biomarkers for predictive value and clinical relevance

Alongside the anticipated submissions, we encourage researchers to verify data using "wet lab" techniques and adequately discuss the limitations of their studies. Original research articles, review papers, methodological reports, and opinion pieces that contribute to these areas are highly encouraged.

Please note that manuscripts consisting solely of bioinformatics or computational analysis of public omics databases that are not supplemented by relevant functional validation (clinical cohort or biological validation in vitro or in vivo) are out of scope for this Research Topic.