Log::ProgramInfo: A Perl module to collect and log data for bioinformatics pipelines.

BackgroundTo reproduce and report a bioinformatics analysis, it is important to be able to determine the environment in which a program was run. It can also be valuable when trying to debug why different executions are giving unexpectedly different results.ResultsLog::ProgramInfo is a Perl module that writes a log file at the termination of execution of the enclosing program, to document useful execution characteristics. This log file can be used to re-create the environment in order to reproduce an earlier execution. It can also be used to compare the environments of two executions to determine whether there were any differences that might affect (or explain) their operation.AvailabilityThe source is available on CPAN (Macdonald and Boutros, Log-ProgramInfo. http://search.cpan.org/~boutroslb/Log-ProgramInfo/).ConclusionUsing Log::ProgramInfo in programs creating result data for publishable research, and including the Log::ProgramInfo output log as part of the publication of that research is a valuable method to assist others to duplicate the programming environment as a precursor to validating and/or extending that research

Divergent mutational processes distinguish hypoxic and normoxic tumours.

Many primary tumours have low levels of molecular oxygen (hypoxia), and hypoxic tumours respond poorly to therapy. Pan-cancer molecular hallmarks of tumour hypoxia remain poorly understood, with limited comprehension of its associations with specific mutational processes, non-coding driver genes and evolutionary features. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumour types, we quantify hypoxia in 1188 tumours spanning 27 cancer types. Elevated hypoxia associates with increased mutational load across cancer types, irrespective of underlying mutational class. The proportion of mutations attributed to several mutational signatures of unknown aetiology directly associates with the level of hypoxia, suggesting underlying mutational processes for these signatures. At the gene level, driver mutations in TP53, MYC and PTEN are enriched in hypoxic tumours, and mutations in PTEN interact with hypoxia to direct tumour evolutionary trajectories. Overall, hypoxia plays a critical role in shaping the genomic and evolutionary landscapes of cancer

Computational purification of individual tumor gene expression profiles leads to significant improvements in prognostic prediction.

Tumor heterogeneity is a limiting factor in cancer treatment and in the discovery of biomarkers to personalize it. We describe a computational purification tool, ISOpure, to directly address the effects of variable normal tissue contamination in clinical tumor specimens. ISOpure uses a set of tumor expression profiles and a panel of healthy tissue expression profiles to generate a purified cancer profile for each tumor sample and an estimate of the proportion of RNA originating from cancerous cells. Applying ISOpure before identifying gene signatures leads to significant improvements in the prediction of prognosis and other clinical variables in lung and prostate cancer

Cytokines and depression in cancer patients and caregivers.

Objective:A better understanding of the biobehavioral mechanisms underlying depression in cancer is required to translate biomarker findings into clinical interventions. We tested for associations between cytokines and the somatic and psychological symptoms of depression in cancer patients and their healthy caregivers. Patients and methods:The GRID Hamilton Rating Scale for Depression (Ham-D) was administered to 61 cancer patients of mixed type and stage, 26 primary caregivers and 38 healthy controls. Concurrently, blood was drawn for multiplexed plasma assays of 15 cytokines. Multiple linear regression, adjusted for biobehavioral variables, identified cytokine associations with the psychological (Ham-Dep) and somatic (Ham-Som) subfactors of the Ham-D. Results:The Ham-Dep scores of cancer patients were similar to their caregivers, but their Ham-Som scores were significantly higher (twofold, p=0.016). Ham-Som was positively associated with IL-1ra (coefficient: 1.27, p≤0.001) in cancer patients, and negatively associated with IL-2 (coefficient: -0.68, p=0.018) in caregivers. Ham-Dep was negatively associated with IL-4 (coefficient: -0.67, p=0.004) in cancer patients and negatively associated with IL-17 (coefficient: -1.81, p=0.002) in caregivers. Conclusion:The differential severity of somatic symptoms of depression in cancer patients and caregivers and the unique cytokine associations identified with each group suggests the potential for targeted interventions based on phenomenology and biology. The clinical implication is that depressive symptoms in cancer patients can arise from biological stressors, which is an important message to help destigmatize the development of depression in cancer patients