The prognostic value of vascular endothelial growth factor in 574 node-negative breast cancer patients who did not receive adjuvant systemic therapy

The growth and metastasising capacity of solid tumours are dependent on angiogenesis. Vascular endothelial growth factor is a mediator of angiogenesis. In this study we investigated whether vascular endothelial growth factor is associated with the natural course of the disease in primary invasive breast cancer. In 574 tumours of patients with node-negative invasive breast cancer the cytosolic levels of vascular endothelial growth factor were measured using a quantitative enzyme-linked immunosorbent assay. These patients did not receive adjuvant systemic therapy and were followed for a median follow-up time of 61 months (range 2–155 months) after the primary diagnosis. Correlations with well-known prognostic factors, and univariate and multivariate survival analyses were performed. Vascular endothelial growth factor level was positively associated with age and tumour size (P=0.042 and P=0.029, respectively). In addition, vascular endothelial growth factor level was inversely, but weakly correlated with progesterone receptor levels (PgR) (rs=−0.090, P=0.035). A high vascular endothelial growth factor level (equal or above the median level of 0.53 ng mg−1 protein) predicted a reduced relapse-free survival and overall survival in the univariate survival rate analysis (for both P=0.005). In the multivariate analysis as well, vascular endothelial growth factor showed to be an independent predictor of poor relapse-free survival and overall survival (P=0.045 and P=0.029, respectively), in addition to age, tumour size and PgR. The results show that cytosolic levels of vascular endothelial growth factor in tumour tissue samples are independently indicative of prognosis for patients with node-negative breast cancer who were not treated with adjuvant systemic therapy. This implies that vascular endothelial growth factor is related with the natural course of breast cancer progression

Diagnostic DNA Methylation Biomarkers for Renal Cell Carcinoma:A Systematic Review

CONTEXT: The 5-yr survival of early-stage renal cell carcinoma (RCC) is approximately 93%, but once metastasised, the 5-yr survival plummets to 12%, indicating that early RCC detection is crucial to improvement in survival. DNA methylation biomarkers have been suggested to be of potential diagnostic value; however, their current state of clinical translation is unclear and a comprehensive overview is lacking. OBJECTIVE: To systematically review and summarise all literature regarding diagnostic DNA methylation biomarkers for RCC. EVIDENCE ACQUISITION: We performed a systematic literature review of PubMed, EMBASE, Medline, and Google Scholar up to January 2019, according to the Preferred Reporting Items for Systematic Review and Meta-Analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines. Included studies were scored according to the Standards for Reporting of Diagnostic Accuracy Studies (STARD) criteria. Forest plots were generated to summarise diagnostic performance of all biomarkers. Level of evidence (LoE) and potential risk of bias were determined for all included studies. EVIDENCE SYNTHESIS: After selection, 19 articles reporting on 44 diagnostic DNA methylation biomarkers and 11 multimarker panels were included; however, only 15 biomarkers were independently validated. STARD scores varied from 4 to 13 out of 23 points, with a median of 10 points. Large variation in subgroups, methods, and primer locations was observed. None of the reported biomarkers exceeded LoE III, and the majority of studies reported inadequately. CONCLUSIONS: None of the reported biomarkers exceeded LoE III, indicating their limited clinical utility. Moreover, study reproducibility and further development of these RCC biomarkers are greatly hampered by inadequate reporting. PATIENT SUMMARY: In this report, we reviewed whether specific biomarkers could be used to diagnose the most common form of kidney cancer. We conclude that due to limited evidence and reporting inconsistencies, none of these biomarkers can be used in clinical practice, and further development towards clinical use is hindered

When transcriptome meets metabolome: fast cellular responses of yeast to sudden relief of glucose limitation

Within the first 5 min after a sudden relief from glucose limitation, Saccharomyces cerevisiae exhibited fast changes of intracellular metabolite levels and a major transcriptional reprogramming. Integration of transcriptome and metabolome data revealed tight relationships between the changes at these two levels. Transcriptome as well as metabolite changes reflected a major investment in two processes: adaptation from fully respiratory to respiro-fermentative metabolism and preparation for growth acceleration. At the metabolite level, a severe drop of the AXP pools directly after glucose addition was not accompanied by any of the other three NXP. To counterbalance this loss, purine biosynthesis and salvage pathways were transcriptionally upregulated in a concerted manner, reflecting a sudden increase of the purine demand. The short-term dynamics of the transcriptome revealed a remarkably fast decrease in the average half-life of downregulated genes. This acceleration of mRNA decay can be interpreted both as an additional nucleotide salvage pathway and an additional level of glucose-induced regulation of gene expression

Was There Shortening of the Interval Between Diagnosis and Treatment of Colorectal Cancer in Southern Netherlands Between 2005 and 2008?

Background: The Dutch Cancer Society proposed that the interval between diagnosis and start of treatment should be less than 15 working days. The purpose of this study was to determine whether the interval from diagnosis to treatment for patients with colorectal cancer (CRC) shortened between 2005 and 2008 in hospitals in southern Netherlands. Methods: Patients with CRC diagnosed in six hospitals in southern Netherlands during January to December in 2005 (n = 445) and January to July in 2008 (n = 353) were included. The time between diagnosis and start of treatment was assessed, and the proportion of patients treated within the recommended time (70 years and those with stage I disease. Substantial variation was seen among hospitals. Conclusions: Time to treatment for patients with CRC in southern Netherlands did not shorten between 2005 and 2008. The time to treatment should be reduced to meet the advice of the Dutch Cancer Society

A statistical approach to virtual cellular experiments: improved causal discovery using accumulation IDA (aIDA)

Motivation: We address the following question: Does inhibition of the expression of a gene X in a cellular assay affect the expression of another gene Y? Rather than inhibiting gene X experimentally, we aim at answering this question computationally using as the only input observational gene expression data. Recently, a new statistical algorithm called Intervention calculus when the Directed acyclic graph is Absent (IDA), has been proposed for this problem. For several biological systems, IDA has been shown to outcompete regression-based methods with respect to the number of true positives versus the number of false positives for the top 5000 predicted effects. Further improvements in the performance of IDA have been realized by stability selection, a resampling method wrapped around IDA that enhances the discovery of true causal effects. Nevertheless, the rate of false positive and false negative predictions is still unsatisfactorily high. Results: We introduce a new resampling approach for causal discovery called accumulation IDA (aIDA). We show that aIDA improves the performance of causal discoveries compared to existing variants of IDA on both simulated and real yeast data. The higher reliability of top causal effect predictions achieved by aIDA promises to increase the rate of success of wet lab intervention experiments for functional studies

A 50% higher prevalence of life-shortening chronic conditions among cancer patients with low socioeconomic status

Background: Comorbidity and socioeconomic status (SES) may be related among cancer patients. Method : Population-based cancer registry study among 72 153 patients diagnosed during 1997-2006. Results : Low SES patients had 50% higher risk of serious comorbidity than those with high SES. Prevalence was increased for each cancer site. Low SES cancer patients had significantly higher risk of also having cardiovascular disease, chronic obstructive pulmonary diseases, diabetes mellitus, cerebrovascular disease, tuberculosis, dementia, and gastrointestinal disease. One-year survival was significantly worse in lowest vs highest SES, partly explained by comorbidity. Conclusion : This illustrates the enormous heterogeneity of cancer patients and stresses the need for optimal treatment of cancer patients with a variety of concomitant chronic conditions

Stage III Non-Small Cell Lung Cancer in the elderly: Patient characteristics predictive for tolerance and survival of chemoradiation in daily clinical practice

Background: In unselected elderly with stage III Non-Small Cell Lung Cancer (NSCLC), evidence is scarce regarding motives and effects of treatment modalities. Methods: Hospital-based multicenter retrospective study including unresectable stage III NSCLC patients aged >= 70 and diagnosed between 2009 and 2013 (N = 216). Treatment motives and tolerance (no unplanned hospitalizations and completion of treatment), and survival were derived from medical records and the Netherlands Cancer Registry. Results: Patients received concurrent chemoradiation (cCHRT, 33%), sequential chemoradiation (sCHRT, 24%), radical radiotherapy (RT, 16%) or no curative treatment (27%). Comorbidity, performance status (58%) and patient refusal (15%) were the most common motives for omitting cCHRT. Treatment tolerance for cCHRT and sCHRT was worse in case of severe comorbidity (OR 6.2 (95%Cl 1.6-24) and OR 6.4 (95%CI 1.8-22), respectively). One-year survival was 57%, 50%, 49% and 26% for cCHRT, sCHRT, RT and no curative treatment, respectively. Compared to cCHRT, survival was worse for no curative treatment (P = 0.000), but not significantly worse for sCHRT and RT (P = 0.38). Conclusion: Although relatively fit elderly were assigned to cCHRT, treatment tolerance was worse, especially for those with severe comorbidity. Survival seemed not significantly better as compared to sCHRT or RT. Prospective studies in this vital and understudied area are needed

Evaluation of rate law approximations in bottom-up kinetic models of metabolism.

BackgroundThe mechanistic description of enzyme kinetics in a dynamic model of metabolism requires specifying the numerical values of a large number of kinetic parameters. The parameterization challenge is often addressed through the use of simplifying approximations to form reaction rate laws with reduced numbers of parameters. Whether such simplified models can reproduce dynamic characteristics of the full system is an important question.ResultsIn this work, we compared the local transient response properties of dynamic models constructed using rate laws with varying levels of approximation. These approximate rate laws were: 1) a Michaelis-Menten rate law with measured enzyme parameters, 2) a Michaelis-Menten rate law with approximated parameters, using the convenience kinetics convention, 3) a thermodynamic rate law resulting from a metabolite saturation assumption, and 4) a pure chemical reaction mass action rate law that removes the role of the enzyme from the reaction kinetics. We utilized in vivo data for the human red blood cell to compare the effect of rate law choices against the backdrop of physiological flux and concentration differences. We found that the Michaelis-Menten rate law with measured enzyme parameters yields an excellent approximation of the full system dynamics, while other assumptions cause greater discrepancies in system dynamic behavior. However, iteratively replacing mechanistic rate laws with approximations resulted in a model that retains a high correlation with the true model behavior. Investigating this consistency, we determined that the order of magnitude differences among fluxes and concentrations in the network were greatly influential on the network dynamics. We further identified reaction features such as thermodynamic reversibility, high substrate concentration, and lack of allosteric regulation, which make certain reactions more suitable for rate law approximations.ConclusionsOverall, our work generally supports the use of approximate rate laws when building large scale kinetic models, due to the key role that physiologically meaningful flux and concentration ranges play in determining network dynamics. However, we also showed that detailed mechanistic models show a clear benefit in prediction accuracy when data is available. The work here should help to provide guidance to future kinetic modeling efforts on the choice of rate law and parameterization approaches