Meeting report from the second EurocanPlatform summer school on translational cancer research, Portugal, 20-24 October 2014

The second EurocanPlatform summer school was held in Algarve, Portugal and attracted scientists, clinicians and pathologists with a common interest in cancer research to discuss the latest developments and challenges in the field. The meeting focused on translational cancer research and also included lectures, workshops and discussions, which covered all aspects of the translational research continuum, from early detection through treatment to survivorship. The rate of new cancer cases and cancer mortality increases every year. Although the last decade witnessed enormous progress in understanding cancer biology and the development of new therapies, the efficacy of these therapies is challenged by cancer resistance. It clearly suggests that new druggable targets are required and their translation from laboratory to bedside must be faster and more efficient to improve survival rates and standards of care

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

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

Antitumor efficacy of radiation plus immunotherapy depends upon dendritic cell activation of effector CD8+ T cells

Tumor cells dying after cytotoxic therapy are a potential source of antigen for T-cell priming. Antigen-presenting cells (APC) can cross-present MHC I–restricted peptides after the uptake of dying cells. Depending on the nature of the surrounding environmental signals, APCs then orchestrate a spectrum of responses ranging from immune activation to inhibition. Previously, we had demonstrated that combining radiation with either agonistic monoclonal antibody (mAb) to CD40 or a systemically administered TLR7 agonist could enhance CD8 T-cell–dependent protection against syngeneic murine lymphoma models. However, it remains unknown how individual APC populations affect this antitumor immune response. Using APC depletion models, we now show that dendritic cells (DC), but not macrophages or B cells, were responsible for the generation of long-term immunologic protection following combination therapy with radiotherapy and either agonistic CD40 mAb or systemic TLR7 agonist therapy. Novel immunotherapeutic approaches that augment antigen uptake and presentation by DCs may further enhance the generation of therapeutic antitumor immune responses, leading to improved outcomes after radiotherapy

Diffusion model comparison identifies distinct tumor sub-regions and tracks treatment response.

PURPOSE:MRI biomarkers of tumor response to treatment are typically obtained from parameters derived from a model applied to pre-treatment and post-treatment data. However, as tumors are spatially and temporally heterogeneous, different models may be necessary in different tumor regions, and model suitability may change over time. This work evaluates how the suitability of two diffusion-weighted (DW) MRI models varies spatially within tumors at the voxel level and in response to radiotherapy, potentially allowing inference of qualitatively different tumor microenvironments. METHODS:DW-MRI data were acquired in CT26 subcutaneous allografts before and after radiotherapy. Restricted and time-independent diffusion models were compared, with regions well-described by the former hypothesized to reflect cellular tissue, and those well-described by the latter expected to reflect necrosis or oedema. Technical and biological validation of the percentage of tissue described by the restricted diffusion microstructural model (termed %MM) was performed through simulations and histological comparison. RESULTS:Spatial and radiotherapy-related variation in model suitability was observed. %MM decreased from a mean of 64% at baseline to 44% 6 days post-radiotherapy in the treated group. %MM correlated negatively with the percentage of necrosis from histology, but overestimated it due to noise. Within MM regions, microstructural parameters were sensitive to radiotherapy-induced changes. CONCLUSIONS:There is spatial and radiotherapy-related variation in different models' suitability for describing diffusion in tumor tissue, suggesting the presence of different and changing tumor sub-regions. The biological and technical validation of the proposed %MM cancer imaging biomarker suggests it correlates with, but overestimates, the percentage of necrosis