Quantitative imaging in cancer evolution and ecology.

Area of Science:

• Oncology
• Radiology
• Evolutionary Biology

Background:

• Cancer therapy failure is often linked to tumor cells' adaptive evolution and heterogeneity.
• Tumors exhibit diverse imaging and genetic profiles, both between patients and within a single tumor.
• Intratumoral heterogeneity is influenced by environmental selection forces acting on cell phenotypes.

Purpose of the Study:

• To explore the potential of clinical imaging to assess and monitor intratumoral evolution.
• To investigate how spatially explicit image analysis can define intratumoral Darwinian dynamics.
• To highlight the role of advanced image analysis in developing evolution-based cancer therapies.

Main Methods:

• Utilizing new methods for quantitative, reproducible, and mineable clinical imaging data extraction and analysis.
• Employing spatially explicit image analysis to identify regionally distinct tumor habitats.
• Defining intratumoral Darwinian dynamics by identifying regional variations in environmental selection forces and cellular adaptation.

Main Results:

• Current quantitative imaging metrics often lack spatial resolution, treating tumors as uniform.
• Spatially explicit analysis reveals tumors are not well-mixed, with distinct regions harboring aggressive cell populations.
• Clinical imaging can identify environmental selection forces and cellular adaptation within tumors.

Conclusions:

• Clinical imaging, particularly with spatially explicit analysis, can monitor intratumoral evolution.
• Understanding intratumoral Darwinian dynamics through imaging is crucial for personalized cancer therapy.
• Advances in image analysis will position clinical imaging as central to evolution-based, patient-specific cancer treatment.