Cancer prevention research is a vital area that aims to reduce the burden of cancer by identifying, eliminating, or mitigating risk factors. Exploring the interplay between genetic factors, modifiable behaviors and environmental exposures can yield more targeted prevention strategies and inform public health policies. By its nature, there is important scope for consideration of public engagement efforts, governmental relations, and public-private partnerships in truly reducing exposures. Proposals that align with this theme may include the development and implementation of innovative screening methods and strategies to revolutionize early detection; the development of biomarkers to identify precancerous changes in tissues; an examination of the nexus between individual risk and evolving environmental conditions that may impact disease onset and progression (examples may include carcinogenic heavy metals, particulate air pollution due to drought and high temperatures, and industrial contaminants, among others); an investigation into the biological roles and interactions of pre-existing conditions and comorbidities; or stimulating the future initiation of comprehensive screening programs for individuals at risk but not yet affected, which are pivotal for evaluating the effectiveness of multi-cancer early detection tests. These groundbreaking research initiatives hold immense potential to significantly enhance cancer prevention and care within our service area. Innovative, implementable, and scalable solutions driven from this research area should have the potential for substantial improvements in the health of those we serve.

Cancer diagnosis is poised for a transformative leap, driven by ongoing advancements in our understanding of early disease progression, the ready availability and rapid progress in biochemical, histological, and imaging techniques and breakthroughs in physics-based and AI computational modeling. Proposals that leverage the unique combined strengths of UT and MD Anderson in basic, clinical, and applied research to pioneer new diagnostic approaches are highly encouraged. Examples of proposals in this area could include the development of multi-modal, multi-cancer early detection tests, leveraging machine learning to analyze nucleic acids, protein, metabolite or microbial profiles for early-stage cancer detection; the integration of spatial “omics” and histology to enable precise cancer subtyping and enhanced interpretation of histological slides for accurate diagnosis; development of multimodal diagnostic approaches at the intersection of imaging, minimally invasive sampling and machine learning; and the application of theranostics for the diagnosis, imaging, and treatment of cancer. Proposals that combine interdisciplinary approaches are also encouraged. Proposals responsive to this theme should aim to push the boundaries of current methodologies to significantly advance the field and successful execution of aims should be pivotal in shaping the future of cancer diagnosis.

Proposals for this emerging theme might involve research efforts having the potential to enhance our understanding of the biology of cancer by harnessing advanced computational and biophysical modeling techniques together with AI methods to revolutionize diagnostics and treatment planning. The key areas of interest include the development of sophisticated biophysical models of tumor dynamics that can serve as predictive cores for cancer patient digital twins to elucidate tumor initiation, growth, invasion, and metastasis, the integration of computational modeling with cutting-edge imaging techniques to create highly accurate cancer tissue specific and patient-specific models, and the creation of scalable algorithms for the dynamic updating of digital twins. The initiative also encourages the development of multiscale models that bridge from molecular and cellular scales to organoids, model organisms, human individuals, and populations at large, and the use of digital twins to improve diagnosis and design patient-specific treatments to achieve greater diagnostic accuracy and therapeutic efficacy, ultimately leading to better human-centric clinical decision-making. Furthermore, it emphasizes human-centric decision-making and interaction, including the development of interfaces that facilitate seamless integration into clinical workflows.

Novel approaches capable of overcoming the complexities of cancer or resistance to current therapies will lead to more personalized, safer, and potentially curative treatments. Proposals addressing this critical area could include those focused on the use of novel technologies, such as generative AI, genetic engineering, and nanotechnology, to develop the next generation of anti-cancer treatments. These may include new cell-based therapies, predictive TCR/BCR analytics to identify and target cancer antigens, novel proteins/peptides, enzymes, and antibodies, and advanced robotic tools for precision surgical oncology, in vivo gene engineering and gene editing, as well as targeting of coding and non-coding RNAs. Additional possible areas of interest could include new treatment modalities, such as mRNA-based therapeutics and nano-therapeutics, as well as approaches targeting oncogenic stem cell, epigenetic, and metabolic mechanisms, novel vaccine development, advancements in genome and epigenome engineering, and protein engineering. Proposals would be considered that identify the genetic susceptibilities and disease mechanisms underlying resistance through functional genomics and synthetic transcriptional control approaches, ultimately aiming at developing strategies to improve current treatments.

Yes, please use the templates provided to prepare your proposal. This will aid in the submission and review process.