Background:Activating mutations in KRAS have been suggested as potential predictive and prognostic biomarkers. However, the prognostic impact of specific point mutations remains less clear. This study assessed the prognostic impact of specific KRAS mutations on survival for patients with colorectal cancer.Methods:Retrospective review of patients KRAS typed for advanced and recurrent colorectal cancer between 2010 and 2015 in a UK Cancer Network.Results:We evaluated the impact of KRAS genotype in 392 patients. Mutated KRAS was detected in 42.9% of tumours. KRAS mutations were more common in moderate vs well-differentiated tumours. On multivariate analysis, primary tumour T stage (HR 2.77 (1.54–4.98), P=0.001), N stage (HR 1.51 (1.01–2.26), P=0.04), curative intent surgery (HR 0.51 (0.34–0.76), P=0.001), tumour grade (HR 0.44 (0.30–0.65), P=0.001) and KRAS mutation (1.54 (1.23–2.12), P=0.005) were all predictive of overall survival. Patients with KRAS codon 12 mutations had worse overall survival (HR 1.76 (95% CI 1.27–2.43), P=0.001). Among the five most common codon 12 mutations, only p.G12C (HR 2.21 (1.15–4.25), P=0.01) and p.G12V (HR 1.69 (1.08–2.62), P=0.02) were predictive of overall survival.Conclusions:For patients with colorectal cancer, p.G12C and p.G12V mutations in codon 12 were independently associated with worse overall survival after diagnosis.
Genomics and metabolomics are widely used to explore specialized metabolite diversity. The Paired Omics Data Platform is a community initiative to systematically document links between metabolome and (meta)genome data, aiding identification of natural product biosynthetic origins and metabolite structures.
The Burkholderia cepacia complex is a group of closely related bacterial species with large genomes that infect immunocompromised individuals and those living with cystic fibrosis. Some of these species are found more frequently and cause more severe disease than others, yet metabolomic differences between these have not been described. Furthermore, our understanding of how these species respond to antibiotics is limited. We investigated the metabolomics differences between three most prevalent Burkholderia spp. associated with cystic fibrosis: B. cenocepacia, B. multivorans, and B. dolosa in the presence and absence of the antibiotic trimethoprim. Using a combination of supervised and unsupervised metabolomics data visualization and analysis tools, we describe the overall differences between strains of the same species and between species. Specifically, we report, for the first time, the role of the pyomelanin pathway in the metabolism of trimethoprim. We also report differences in the detection of known secondary metabolites such as fragin, ornibactin, and N-acylhomoserine lactones and their analogs in closely related strains. Furthermore, we highlight the potential for the discovery of new secondary metabolites in clinical strains of Burkholderia spp. The metabolomics differences described in this study highlight the personalized nature of closely related Burkholderia strains.
While combined antiretroviral therapy (cART) can result in undetectable plasma viral loads, it does not eradicate HIV infection. Furthermore, HIV-infected individuals while on cART remain at an increased risk of developing serious comorbidities, such as cancer, neurological disease, and atherosclerosis, suggesting that during cART, tissue-based HIV may contribute to such pathologies. We obtained DNA and RNA env, nef, and pol sequences using single-genome sequencing from postmortem tissues of three HIV ؉ cART-treated (cART ؉ ) individuals with undetectable viral load and metastatic cancer at death and performed time-scaled Bayesian evolutionary analyses. We used a sensitive in situ hybridization technique to visualize HIV gag-pol mRNA transcripts in cerebellum and lymph node tissues from one patient. Tissue-associated virus evolved at similar rates in cART ؉ and cARTnaive (cART ؊ ) patients. Phylogenetic trees were characterized by two distinct features: (i) branching patterns consistent with constant viral evolution and dispersal among tissues and (ii) very recently derived clades containing both DNA and RNA sequences from multiple tissues. Rapid expansion of virus near death corresponded to wide-spread metastasis. HIV RNA ؉ cells clustered in cerebellum tissue but were dispersed in lymph node tissue, mirroring the evolutionary patterns observed for that patient. Activated, infiltrating macrophages were associated with HIV RNA. Our data provide evidence that tissues serve as a sanctuary for wild-type HIV during cART and suggest the importance of macrophages as an alternative reservoir and mechanism of virus spread. IMPORTANCECombined antiretroviral therapy (cART) reduces plasma HIV to undetectable levels; however, removal of cART results in plasma HIV rebound, thus highlighting its inability to entirely rid the body of infection. Additionally, HIV-infected individuals on cART remain at high risk of serious diseases, which suggests a contribution from residual HIV. In this study, we isolated and sequenced HIV from postmortem tissues from three HIV ؉ cART ؉ individuals who died with metastatic cancer and had no detectable plasma viral load. Using high-resolution evolutionary analyses, we found that tissue-based HIV continues to replicate, evolve, and migrate among tissues during cART. Furthermore, cancer onset and metastasis coincided with increased HIV expansion, suggesting a linked mechanism. HIV-expressing cells were associated with tissue macrophages, a target of HIV infection. Our results suggest the importance of tissues, and macrophages in particular, as a target for novel anti-HIV therapies.A lthough current combined antiretroviral therapy (cART) regimens can extend the lives of individuals infected with human immunodeficiency virus (HIV) by years or decades, two major problems remain. First, HIV is never eradicated in treated patients and plasma viral loads (VL) will rebound if treatment is stopped. Subsets of T cells (1-7) and lymph (8, 9) are considered likely reservoirs of virus during cART. Vi...
Even though raw mass spectrometry data is information rich, the vast majority of the data is underutilized. The ability to interrogate these rich datasets is handicapped by the limited capability and flexibility of existing software. We introduce the Mass Spec Query Language (MassQL) that addresses these issues by enabling an expressive set of mass spectrometry patterns to be queried directly from raw data. MassQL is an open-source mass spectrometry query language for flexible and mass spectrometer manufacturer-independent mining of MS data. We envision the flexibility, scalability, and ease of use of MassQL will empower the mass spectrometry community to take fuller advantage of their mass spectrometry data and accelerate discoveries.
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