A new method for the high-precision assessment of tumor changes in response to treatment

Ketogenic diet and metastasis: A critical review of the literature and possible mechanisms

The ketogenic diet (KD) is a low-carb diet that has been indicated as a possible coadjuvant in cancer therapy, mainly due to its capacity to reduce glycolysis production, inflammation, and oxidative stress. However, KD's role in metastasis remains poorly explored. This study aims to provide a critical review of the literature about KD's efficacy in metastasis therapy and the possible molecular mechanisms behind it. Initially, general concepts on KD and metastasis are discussed. Then, it delves deeper into the main cancer mechanisms explored by KD experimental studies, discussing the central results obtained in metastasis research and their main limiting conditions. Following, there is a critical analysis of clinical trials, including those in the grey literature. In the end, there is a summary of the actual studies' limitations and barriers to future research. To date, it is possible to conclude that there is not enough evidence supporting the efficacy of KD in the treatment of metastasis.

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.

A reformulation of pLSA for uncertainty estimation and hypothesis testing in bio-imaging

MOTIVATION

Probabilistic latent semantic analysis (pLSA) is commonly applied to describe mass spectra (MS) images. However, the method does not provide certain outputs necessary for the quantitative scientific interpretation of data. In particular, it lacks assessment of statistical uncertainty and the ability to perform hypothesis testing. We show how linear Poisson modelling advances pLSA, giving covariances on model parameters and supporting χ2 testing for the presence/absence of MS signal components. As an example, this is useful for the identification of pathology in MALDI biological samples. We also show potential wider applicability, beyond MS, using magnetic resonance imaging (MRI) data from colorectal xenograft models.

RESULTS

Simulations and MALDI spectra of a stroke-damaged rat brain show MS signals from pathological tissue can be quantified. MRI diffusion data of control and radiotherapy-treated tumours further show high sensitivity hypothesis testing for treatment effects. Successful χ2 and degrees-of-freedom are computed, allowing null-hypothesis thresholding at high levels of confidence.

AVAILABILITY AND IMPLEMENTATION

Open-source image analysis software available from TINA Vision, www.tina-vision.net.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.