The Warburg effect describes the increased utilization of glycolysis rather than oxidative phosphorylation by tumour cells for their energy requirements under physiological oxygen conditions. This effect has been the basis for much speculation on the survival advantage of tumour cells, tumourigenesis and the microenvironment of tumours. More recently, studies have begun to reveal how the Warburg effect could influence drug efficacy and how our understanding of tumour energetics could be exploited to improve drug development. In particular, evidence is emerging demonstrating how better modelling of the tumour metabolic microenvironment could lead to a better prediction of drug efficacy and the identification of new combination strategies. This review will provide details of the current understanding of the complex interplay between glucose metabolism and pharmacology and discuss opportunities for utilizing the Warburg effect in future drug development.

The combination of DNA bisulfite treatment with high-throughput sequencing technologies has enabled investigation of genome-wide DNA methylation beyond CpG sites and CpG islands. These technologies have opened new avenues to understand the interplay between epigenetic events, chromatin plasticity and gene regulation. However, the processing, managing and mining of this huge volume of data require specialized computational tools and statistical methods that are yet to be standardized. Here, we describe a complete bisulfite sequencing analysis workflow, including recently developed programs, highlighting each of the crucial analysis steps required, i.e. sequencing quality control, reads alignment, methylation scoring, methylation heterogeneity assessment, genomic features annotation, data visualization and determination of differentially methylated cytosines. Moreover, we discuss the limitations of these technologies and considerations to perform suitable analyses.

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Programmed death ligand-1 (PD-L1) expression as determined by immunohistochemistry (IHC) is potentially predictive of clinical outcome. The aim of this study was to assess the concordance of reported PD-L1 IHC assays and investigate factors influencing variability. Consecutive sections from 20 non-small cell lung cancers (NSCLCs) comprising resection, core biopsy, cytology and pleural fluid samples underwent IHC with 5 different antibody/autostainer combinations: 22C3/Link48, 28-8/BOND-MAX, E1L3N/BOND-MAX, SP142/BenchMark and SP263/BenchMark. PD-L1 RNA levels were assessed using RNAscope. The frequency of positive cases using scoring thresholds from clinical trials was 72%, 33%, 61%, 56%, and 33% for the 5 IHC protocols respectively, and 33% for RNAscope. Pairwise agreement on the classification of cases as positive or negative for PD-L1 expression ranged from 61%-94%. On a continuous scale, the lowest correlation was between 28-8/BOND-MAX and SP142/BenchMark (R2=0.25) and highest was between 22C3/Link48 and E1L3N/BOND-MAX (R2=0.71). When cases were ordered according to tumor cell (TC)%, a similar ranking of cases across IHC protocols could be observed, albeit with different quanta and limits of detection. Single-slide OPAL 7-color fluorescence IHC analysis revealed a high degree of co-localization of staining from the 5 PD-L1 antibodies. Using SP142 antibody in a BOND-MAX protocol led to increased TC% quanta, while retaining a similar ranking of samples according to TC%. The results of this study highlight tumor PD-L1 status can vary significantly according to IHC protocol. Protocol-dependent staining intensities and nominated thresholds for positivity contribute to this variability, while the antibody used appears to be less of a factor.