The efficacy of an unrestricted cycling ketogenic diet in preclinical models of IDH wild-type and IDH mutant glioma

Comprehensive quantitative radiogenomic evaluation reveals novel radiomic subtypes with distinct immune pattern in glioma

BACKGROUND

Accurate classification of gliomas is critical to the selection of immunotherapy, and MRI contains a large number of radiomic features that may suggest some prognostic relevant signals. We aim to predict new subtypes of gliomas using radiomic features and characterize their survival, immune, genomic profiles and drug response.

METHODS

We initially obtained 341 images of 36 patients from the CPTAC dataset for the development of deep learning models. Further 1812 images of 111 patients from TCGA_GBM and 152 images of 53 patients from TCGA_LGG were collected for testing and validation. A deep learning method based on Mask R-CNN was developed to identify new subtypes of glioma patients and compared the survival status, immune infiltration patterns, genomic signatures, specific drugs, and predictive models of different subtypes.

RESULTS

200 glioma patients (mean age, 33 years ± 19 [standard deviation]) were enrolled. The accuracy of the deep learning model for identifying tumor regions achieved 88.3 % (98/111) in the test set and 83 % (44/53) in the validation set. The sample was divided into two subtypes based on radiomic features showed different prognostic outcomes (hazard ratio, 2.70). According to the results of the immune infiltration analysis, the subtype with a poorer prognosis was defined as the immunosilencing radiomic (ISR) subtype (n = 43), and the other subtype was the immunoactivated radiomic (IAR) subtype (n = 53). Subtype-specific genomic signatures distinguished celllines into ISR celllines (n = 9) and control celllines (n = 13), and identified eight ISR-specific drugs, four of which were validated by the OCTAD database. Three machine learning-based classifiers showed that radiomic and genomic co-features better predicted the radiomic subtypes of gliomas.

CONCLUSIONS

These findings provide insights into how radiogenomic could identify specific subtypes that predict prognosis, immune and drug sensitivity in a non-invasive manner.

Anti-tumor effects of ketogenic diets and their synergism with other treatments in mice: Bayesian evidence synthesis of 1755 individual mouse survival data

BACKGROUND

Ketogenic diets (KDs) are high-fat diets with putative anti-tumor effects. The aim of this study was to synthesize the evidence for the anti-tumor effects of KDs in mice, with a focus on their possible synergism with chemotherapy (CT), radiotherapy (RT), or targeted therapies (TT).

METHODS

Relevant studies were retrieved from a literature search. A total of 43 articles reporting on 65 mouse experiments fulfilled the inclusion criteria, and 1755 individual mouse survival times were collated from the study authors or the publications. The restricted mean survival time ratio (RMSTR) between the KD and control groups served as the effect size. Bayesian evidence synthesis models were used to estimate pooled effect sizes and to assess the impact of putative confounders and synergism between KD and other therapies.

RESULTS

Overall, there was a significant survival-prolonging effect of KD monotherapy (RMSTR = 1.161 ± 0.040), which was confirmed in meta-regression accounting for syngeneic versus xenogeneic models, early versus late KD start and subcutaneous versus other organ growth. Combining the KD with RT or TT, but not CT, was associated with a further 30% (RT) or 21% (TT) prolongation of survival. An analysis accounting for 15 individual tumor entities showed that KDs exerted significant survival-prolonging effects in pancreatic cancer (all treatment combinations), gliomas (KD + RT and KD + TT), head and neck cancer (KD + RT), and stomach cancer (KD+RT and KD + TT).

CONCLUSIONS

This analytical study confirmed the overall anti-tumor effects of KDs in a large number of mouse experiments and provides evidence for synergistic effects with RT and TT.

Ketogenic Diet in the Treatment of Gliomas and Glioblastomas

In recent years, scientific interest in the use of the ketogenic diet (KD) as a complementary approach to the standard cancer therapy has grown, in particular against those of the central nervous system (CNS). In metabolic terms, there are the following differences between healthy and neoplastic cells: neoplastic cells divert their metabolism to anaerobic glycolysis (Warburg effect), they alter the normal mitochondrial functioning, and they use mainly certain amino acids for their own metabolic needs, to gain an advantage over healthy cells and to lead to a pro-oncogenetic effect. Several works in literature speculate which are the molecular targets of KD used against cancer. The following different mechanisms of action will be explored in this review: metabolic, inflammatory, oncogenic and oncosuppressive, ROS, and epigenetic modulation. Preclinical and clinical studies on the use of KD in CNS tumors have also increased in recent years. An interesting hypothesis emerged from the studies about the possible use of a ketogenic diet as a combination therapy along with chemotherapy (CT) and radiotherapy (RT) for the treatment of cancer. Currently, however, clinical data are still very limited but encouraging, so we need further studies to definitively validate or disprove the role of KD in fighting against cancer.