Further molecular profiling of tumors harboring therapeutic targets within non-small cell lung cancer

Background: Treatment of NSCLC has been revolutionized in recent years with the introduction of several targeted therapies for selected genetically altered subtypes of NSCLC. A better understanding of molecular characteristics of NSCLC, which features common drug targets, may identify new therapeutic options. Methods: Over 6,700 non-small cell lung cancer cases referred to Caris Life Sciences between 2009 and 2014. Diagnoses and history were collected from referring physicians. Specific testing was performed per physician request and included a combination of sequencing (Sanger, NGS or pyrosequencing), protein expression (IHC), gene amplification/rearrangement (CISH or FISH), and/or RNA fragment analysis. Results: Tumors profiles from patients with hormone receptor positive disease (HER2, ER, PR, or AR positive by IHC) (n=629), HER2 mutations (n=8) ALK rearrangements (n=55), ROS1 rearrangement (n=17), cMET amplification or mutation (n=126), and cKIT mutation (n=11) were included in this analysis and compared to the whole cohort. Tumors with ALK rearrangement overexpressed AR in 18% of cases, and 7% presented with concomitant KRAS mutation. Lower rates of PTEN loss, as assessed by IHC, were observed in ALK positive (20%), ROS1 positive (9%) and cKIT mutated tumors (25%) compared to the overall NSCLC population (58%). cMET was overexpressed in 66% of ROS1 translocated and 57% of HER2 mutated tumors. cKIT mutations were found co-existing with APC (20%) and EGFR (20%) mutations. Pathway analysis revealed that hormone receptor positive disease carried more mutations in the ERK pathway (32%) compared to 9% in the mTOR pathway. 25% of patients with HER2 mutations harbored a co-existing mutation in the mTOR pathway. Conclusions: Pathway profiling reveals that NSCLC tumors present more often than reported with several concomitant alterations affecting the ERK or AKT pathway. Additionally, they are also characterized by the expression of potential biological modifiers of the cell cycle like hormonal receptors, representing a rationale for dual inhibition strategies in selected patients. Further refining of the understanding of NSCLC biomarker profile will optimize research for new treatment strategies.

Metabolic syndrome and nonalcoholic fatty liver disease: Is insulin resistance the link?

Metabolic syndrome (MetS) is a disease composed of different risk factors such as obesity, type 2 diabetes or dyslipidemia. The prevalence of this syndrome is increasing worldwide in parallel with the rise in obesity. Nonalcoholic fatty liver disease (NAFLD) is now the most frequent chronic liver disease in western countries, affecting more than 30% of the general population. NAFLD encompasses a spectrum of liver manifestations ranging from simple steatosis to nonalcoholic steatohepatitis (NASH), fibrosis and cirrhosis, which may ultimately progress to hepatocellular carcinoma. There is accumulating evidence supporting an association between NAFLD and MetS. Indeed, NAFLD is recognized as the liver manifestation of MetS. Insulin resistance is increasingly recognized as a key factor linking MetS and NAFLD. Insulin resistance is associated with excessive fat accumulation in ectopic tissues, such as the liver, and increased circulating free fatty acids, which can further promote inflammation and endoplasmic reticulum stress. This in turn aggravates and maintains the insulin resistant state, constituting a vicious cycle. Importantly, evidence shows that most of the patients developing NAFLD present at least one of the MetS traits. This review will define MetS and NAFLD, provide an overview of the common pathophysiological mechanisms linking MetS and NAFLD, and give a perspective regarding treatment of these ever growing metabolic diseases.

Analyzing the temporal regulation of translation efficiency in mouse liver.

Mammalian physiology and behavior follow daily rhythms that are orchestrated by endogenous timekeepers known as circadian clocks. Rhythms in transcription are considered the main mechanism to engender rhythmic gene expression, but important roles for posttranscriptional mechanisms have recently emerged as well (reviewed in Lim and Allada (2013) [1]). We have recently reported on the use of ribosome profiling (RPF-seq), a method based on the high-throughput sequencing of ribosome protected mRNA fragments, to explore the temporal regulation of translation efficiency (Janich et al., 2015 [2]). Through the comparison of around-the-clock RPF-seq and matching RNA-seq data we were able to identify 150 genes, involved in ribosome biogenesis, iron metabolism and other pathways, whose rhythmicity is generated entirely at the level of protein synthesis. The temporal transcriptome and translatome data sets from this study have been deposited in NCBI's Gene Expression Omnibus under the accession number GSE67305. Here we provide additional information on the experimental setup and on important optimization steps pertaining to the ribosome profiling technique in mouse liver and to data analysis.

Performance of 18F-FET versus 18F-FDG-PET for the diagnosis and grading of brain tumors: systematic review and meta-analysis.

BACKGROUND: For the past decade (18)F-fluoro-ethyl-l-tyrosine (FET) and (18)F-fluoro-deoxy-glucose (FDG) positron emission tomography (PET) have been used for the assessment of patients with brain tumor. However, direct comparison studies reported only limited numbers of patients. Our purpose was to compare the diagnostic performance of FET and FDG-PET. METHODS: We examined studies published between January 1995 and January 2015 in the PubMed database. To be included the study should: (i) use FET and FDG-PET for the assessment of patients with isolated brain lesion and (ii) use histology as the gold standard. Analysis was performed on a per patient basis. Study quality was assessed with STARD and QUADAS criteria. RESULTS: Five studies (119 patients) were included. For the diagnosis of brain tumor, FET-PET demonstrated a pooled sensitivity of 0.94 (95% CI: 0.79-0.98) and pooled specificity of 0.88 (95% CI: 0.37-0.99), with an area under the curve of 0.96 (95% CI: 0.94-0.97), a positive likelihood ratio (LR+) of 8.1 (95% CI: 0.8-80.6), and a negative likelihood ratio (LR-) of 0.07 (95% CI: 0.02-0.30), while FDG-PET demonstrated a sensitivity of 0.38 (95% CI: 0.27-0.50) and specificity of 0.86 (95% CI: 0.31-0.99), with an area under the curve of 0.40 (95% CI: 0.36-0.44), an LR+ of 2.7 (95% CI: 0.3-27.8), and an LR- of 0.72 (95% CI: 0.47-1.11). Target-to-background ratios of either FDG or FET, however, allow distinction between low- and high-grade gliomas (P > .11). CONCLUSIONS: For brain tumor diagnosis, FET-PET performed much better than FDG and should be preferred when assessing a new isolated brain tumor. For glioma grading, however, both tracers showed similar performances.