The development of the sequencing technologies allowed the generation of huge amounts of molecular data from a single cancer specimen, allowing the clinical oncology to enter the era of the precision medicine. This massive amount of data is highlighting new details on cancer pathogenesis but still relies on tissue biopsies, which are unable to capture the dynamic nature of cancer through its evolution. This assumption led to the exploration of non-tissue sources of tumoral material opening the field of liquid biopsies. Blood, together with body fluids such as urines, or stool, from cancer patients, are analyzed applying the techniques used for the generation of omics data. With blood, this approach would allow to take into account tumor heterogeneity (since the circulating components such as CTCs, ctDNA, or ECVs derive from each cancer clone) in a time dependent manner, resulting in a somehow “real-time” understanding of cancer evolution. Liquid biopsies are beginning nowdays to be applied in many cancer contexts and are at the basis of many clinical trials in oncology.

Cancer genomes have been explored from the early 2000s through massive exome sequencing efforts, leading to the publication of The Cancer Genome Atlas in 2013. Sequencing techniques have been developed alongside this project and have allowed scientists to bypass the limitation of costs for whole-genome sequencing (WGS) of single specimens by developing more accurate and extensive cancer sequencing projects, such as deep sequencing of whole genomes and transcriptomic analysis. The Pan Cancer Analysis of Whole Genomes recently published WGS data from more than 2600 human cancers together with almost 1200 related transcriptomes. The

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BackgroundIn the last decade, several studies have reported an unexpected and seemingly paradoxical inverse correlation between BMI and incidence of cardiovascular diseases. This so called “obesity paradox effect” has been mainly investigated through imaging methods instead of histologic evaluation, which is still the best method to study the instability of carotid plaque. Therefore, the purpose of our study was to evaluate by histology the role of obesity in destabilization of carotid plaques and the interaction with age, gender and other major cerebrovascular risk factors.MethodsA total of 390 carotid plaques from symptomatic and asymptomatic patients submitted to endarterectomy, for whom complete clinical and laboratory assessment of major cardiovascular risk factors was available, were studied by histology. Patients with a BMI ≥ 30.0 kg/m2 were considered as obese. Data were analyzed by multivariate logistic regression and for each variable in the equation the estimated odds ratio (OR) was calculated.ResultsUnstable carotid plaque OR for obese patients with age < 70 years was 5.91 (95% CI 1.17–29.80), thus being the highest OR compared to that of other risk factors. Unstable carotid plaque OR decreased to 4.61 (95% CI 0.54–39.19) in males ≥ 70 years, being only 0.93 (95% CI 0.25–3.52) among women. When obesity featured among metabolic syndrome risk factors, the OR for plaque destabilization was 3.97 (95% CI 1.81–6.22), a significantly higher value compared to OR in non-obese individuals with metabolic syndrome (OR = 1.48; 95% CI 0.86–2.31). Similar results were obtained when assessing the occurrence of acute cerebrovascular symptoms.ConclusionsResults from our study appear to do not confirm any paradoxical effect of obesity on the carotid artery district. Conversely, obesity is confirmed to be an independent risk factor for carotid plaque destabilization, particularly in males aged < 70 years, significantly increasing such risk among patients with metabolic syndrome.

The identification of individual or clusters of predictive genetic alterations might help in defining the outcome of cancer treatment, allowing for the stratification of patients into distinct cohorts for selective therapeutic protocols. Neuroblastoma (NB) is the most common extracranial childhood tumour, clinically defined in five distinct stages (1–4 & 4S), where stages 3–4 define chemotherapy-resistant, highly aggressive disease phases. NB is a model for geneticists and molecular biologists to classify genetic abnormalities and identify causative disease genes. Despite highly intensive basic research, improvements on clinical outcome have been predominantly observed for less aggressive cancers, that is stages 1,2 and 4S. Therefore, stages 3–4 NB are still complicated at the therapeutic level and require more intense fundamental research. Using neuroblastoma as a model system, here we herein outline how cancer prediction studies can help at steering preclinical and clinical research toward the identification and exploitation of specific genetic landscape. This might result in maximising the therapeutic success and minimizing harmful effects in cancer patients.