Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges.

The power of predictive modeling for radiotherapy outcomes has historically been limited by an inability to adequately capture patient-specific variabilities; however, next-generation platforms together with imaging technologies and powerful bioinformatic tools have facilitated strategies and provided optimism. Integrating clinical, biological, imaging, and treatment-specific data for more accurate prediction of tumor control probabilities or risk of radiation-induced side effects are high-dimensional problems whose solutions could have widespread benefits to a diverse patient population-we discuss technical approaches toward this objective. Increasing interest in the above is specifically reflected by the emergence of two nascent fields, which are distinct but complementary: radiogenomics, which broadly seeks to integrate biological risk factors together with treatment and diagnostic information to generate individualized patient risk profiles, and radiomics, which further leverages large-scale imaging correlates and extracted features for the same purpose. We review classical analytical and data-driven approaches for outcomes prediction that serve as antecedents to both radiomic and radiogenomic strategies. Discussion then focuses on uses of conventional and deep machine learning in radiomics. We further consider promising strategies for the harmonization of high-dimensional, heterogeneous multiomics datasets (panomics) and techniques for nonparametric validation of best-fit models. Strategies to overcome common pitfalls that are unique to data-intensive radiomics are also discussed.

The anti-malarial drug atovaquone potentiates platinum-mediated cancer cell death by increasing oxidative stress.

Platinum chemotherapies are highly effective cytotoxic agents but often induce resistance when used as monotherapies. Combinatorial strategies limit this risk and provide effective treatment options for many cancers. Here, we repurpose atovaquone (ATQ), a well-tolerated & FDA-approved anti-malarial agent by demonstrating that it potentiates cancer cell death of a subset of platinums. We show that ATQ in combination with carboplatin or cisplatin induces striking and repeatable concentration- and time-dependent cell death sensitization in vitro across a variety of cancer cell lines. ATQ induces mitochondrial reactive oxygen species (mROS), depleting intracellular glutathione (GSH) pools in a concentration-dependent manner. The superoxide dismutase mimetic MnTBAP rescues ATQ-induced mROS production and pre-loading cells with the GSH prodrug N-acetyl cysteine (NAC) abrogates the sensitization. Together, these findings implicate ATQ-induced oxidative stress as key mediator of the sensitizing effect. At physiologically achievable concentrations, ATQ and carboplatin furthermore synergistically delay the growth of three-dimensional avascular spheroids. Clinically, ATQ is a safe and specific inhibitor of the electron transport chain (ETC) and is concurrently being repurposed as a candidate tumor hypoxia modifier. Together, these findings suggest that ATQ is deserving of further study as a candidate platinum sensitizing agent.

Maxillomandibular advancement is a successful treatment for obstructive sleep apnoea: a systematic review and meta-analysis.

The primary objective of this review was to establish the effectiveness of maxillomandibular advancement (MMA) as a successful treatment modality in improving airway patency in patients with obstructive sleep apnoea (OSA). A systematic and detailed search was performed using PubMed Central, covering the period January 2000 to December 2015, with well-defined selection criteria. The authors independently conducted the study selection, data extraction, and assessed the risk of bias of the included studies. Twenty studies met the inclusion criteria. The outcome measures studied were the apnoea-hypopnoea index (AHI), respiratory disturbance index (RDI), Epworth Sleepiness Scale (ESS), lowest oxygen saturation (LSAT), and body mass index (BMI). The random-effects model was adopted for meta-analysis as moderate heterogeneity was identified. The analysis revealed significant changes in the outcome measures after the intervention. The results showed that the preoperative severity of OSA based on AHI and RDI significantly influences the outcome of MMA intervention, with a strong positive correlation between the pre MMA AHI values and the percentage change post intervention. The surgical success of MMA in patients with OSA was found to be 100% with respect to AHI and RDI scores. It is concluded that MMA is a successful treatment for OSA.