BackgroundHypoxia is associated with a poor prognosis in prostate cancer. This work aimed to derive and validate a hypoxia-related mRNA signature for localized prostate cancer.MethodHypoxia genes were identified in vitro via RNA-sequencing and combined with in vivo gene co-expression analysis to generate a signature. The signature was independently validated in eleven prostate cancer cohorts and a bladder cancer phase III randomized trial of radiotherapy alone or with carbogen and nicotinamide (CON).ResultsA 28-gene signature was derived. Patients with high signature scores had poorer biochemical recurrence free survivals in six of eight independent cohorts of prostatectomy-treated patients (Log rank test P < .05), with borderline significances achieved in the other two (P < .1). The signature also predicted biochemical recurrence in patients receiving post-prostatectomy radiotherapy (n = 130, P = .007) or definitive radiotherapy alone (n = 248, P = .035). Lastly, the signature predicted metastasis events in a pooled cohort (n = 631, P = .002). Prognostic significance remained after adjusting for clinic-pathological factors and commercially available prognostic signatures. The signature predicted benefit from hypoxia-modifying therapy in bladder cancer patients (intervention-by-signature interaction test P = .0026), where carbogen and nicotinamide was associated with improved survival only in hypoxic tumours.ConclusionA 28-gene hypoxia signature has strong and independent prognostic value for prostate cancer patients.

The low incidence of pelvic infection questions the value of using prophylactic antibiotics. No increased risk of infection was demonstrated in cases with preexisting peritoneal damage.

SummaryLate-phase clinical trials investigating metformin as a cancer therapy are underway. However, there remains controversy as to the mode of action of metformin in tumors at clinical doses. We conducted a clinical study integrating measurement of markers of systemic metabolism, dynamic FDG-PET-CT, transcriptomics, and metabolomics at paired time points to profile the bioactivity of metformin in primary breast cancer. We show metformin reduces the levels of mitochondrial metabolites, activates multiple mitochondrial metabolic pathways, and increases 18-FDG flux in tumors. Two tumor groups are identified with distinct metabolic responses, an OXPHOS transcriptional response (OTR) group for which there is an increase in OXPHOS gene transcription and an FDG response group with increased 18-FDG uptake. Increase in proliferation, as measured by a validated proliferation signature, suggested that patients in the OTR group were resistant to metformin treatment. We conclude that mitochondrial response to metformin in primary breast cancer may define anti-tumor effect.

With the increase in next generation sequencing generating large amounts of genomic data, gene expression signatures are becoming critically important tools to interpret these data, and are poised to make a large impact on diagnosis, management and prognosis for a number of diseases. Increasingly, it is becoming crucial to establish whether the expression patterns and statistical properties of a set of genes, or signature, are conserved across datasets. Conversely, it is increasingly necessary to compare independent datasets with respect to the expression of established signatures reflecting their clinical or biological characteristics. In this work, we introduce the first protocol, sigQC, which enables a streamlined, systematic approach for the evaluation of gene signatures across different, independent, datasets. To facilitate accessibility, we implemented the protocol in an R package (https://cran.r-project.org/web/packages/sigQC/) designed for users with modest computational skills. SigQC has been adopted by us and collaborators in several basic biology and biomarker studies. The emphasis is in showing the basic but critical quality control steps involved in the generation and application of a clinically and biologically useful, transportable gene signature, including evaluating its expression, variability and structure. It begins with evaluating signature genes' expression and variability, then evaluates statistical properties of the distribution of their expression, and then computes various signature scoring metrics, and gives empirical estimates for the significance of each of these metrics. We demonstrate the application of this protocol, showing how the outputs created from sigQC may be used for the evaluation of gene signatures on large-scale gene expression datasets. cost of potentially increased noise from these non-linear relationships. S-scoring is based on a linear combination of z-scores, and combines the approaches of standardising the dataset with the directionality and flexibility of a linear model 30 . Thus, like a linear model, this scoring system, while it may be more flexible for defining dataset specific scores, often does not translate easily to new datasets or technologies. These methods are not tested explicitly in the current version of sigQC, however the metrics provided by sigQC provide a broad statistical assessment of the genes in a given signature across datasets and technologies, information which can be used to design more context-specific scoring techniques. MaterialsEquipment Hardware:  Personal computer, capable of running R version 3.3.0 or higher Software:  R version ≥ 3.3.0, available to install from https://www.r-project.org/  Bioconductor compatible with R version; installation instructions available from https://www.bioconductor.org/install/  sigQC package, available to download from https://cran.rproject.org/web/packages/sigQC/index.html  The following R packages are required as dependencies: MASS, lattice, KernSmooth, cluster, nnet, class, gridGraphics, biclust,...