Habitat Imaging of Tumors Enables High Confidence Sub-Regional Assessment of Response to Therapy

SIMPLE SUMMARY: Medical imaging techniques such as magnetic resonance imaging (MRI) are powerful tools that can map and measure tumor behavior in great detail. In particular, MRI can provide information about differences present within and between tumors that have a notionally similar type. At present, such imaging techniques are underused in assessment of cancer treatments, often because complicated spatial patterns present in each individual tumor mask individual responses to therapy. In this study we use mathematical modeling to assess tumors derived from 5 different mouse models of cancer. The modeling technique detected response to therapy in individual tumors and for different types of drug and radiation therapy, which was not possible using standard analysis of MRI data, where only group effects are detectable. Our results have potential to reduce the use of animals in medical research. They also enable a new high throughput MRI-based analysis of tumor models undergoing evaluation with new therapies. ABSTRACT: Imaging biomarkers are used in therapy development to identify and quantify therapeutic response. In oncology, use of MRI, PET and other imaging methods can be complicated by spatially complex and heterogeneous tumor micro-environments, non-Gaussian data and small sample sizes. Linear Poisson Modelling (LPM) enables analysis of complex data that is quantitative and can operate in small data domains. We performed experiments in 5 mouse models to evaluate the ability of LPM to identify responding tumor habitats across a range of radiation and targeted drug therapies. We tested if LPM could identify differential biological response rates. We calculated the theoretical sample size constraints for applying LPM to new data. We then performed a co-clinical trial using small data to test if LPM could detect multiple therapeutics with both improved power and reduced animal numbers compared to conventional t-test approaches. Our data showed that LPM greatly increased the amount of information extracted from diffusion-weighted imaging, compared to cohort t-tests. LPM distinguished biological response rates between Calu6 tumors treated with 3 different therapies and between Calu6 tumors and 4 other xenograft models treated with radiotherapy. A simulated co-clinical trial using real data detected high precision per-tumor treatment effects in as few as 3 mice per cohort, with p-values as low as 1 in 10,000. These findings provide a route to simultaneously improve the information derived from preclinical imaging while reducing and refining the use of animals in cancer research.