Identification of neutral tumor evolution across cancer types

A.S. is supported by The Chris Rokos Fellowship in Evolution and Cancer. B.W. is supported by the Geoffrey W. Lewis Post-Doctoral Training fellowship. This work was supported by the Wellcome Trust (105104/Z/14/Z). C.P.B. acknowledges funding from the Wellcome Trust through a Research Career Development Fellowship (097319/Z/11/Z). This work was supported by a Cancer Research UK Career Development Award to T.A.G. M.J.W. is supported by a UK Medical Research Council student fellowship

Simulation-supported framework for job shop scheduling with genetic algorithm

The Job Shop Scheduling Problem (JSSP) is recognized to be one of the most difficult scheduling problems, being NP-complete. During years, many different solving techniques were developed: some techniques are focused on the development of optimization algorithms, whilst others are based on simulation models. Since the 80s, it was recognized that a combination of the two could be of big advantage, matching advantages from both sides. However, this research stream has not been followed to a great extent. The goal of this study is to propose a novel scheduling tool able to match these two really different techniques in one common framework in order to fill this gap in literature. The base of the framework is composed by a genetic algorithm (GA) and a simulation model is introduced into the evaluation of the fitness function, due to the inability of GAs in taking into account the real performances of a production system. An additional purpose of this research is to improve the collaboration between academic and industrial worlds on the topic, through an application of the novel scheduling framework to an industrial case. The implementation to the industrial case also suggested an improvement of the tool: The introduction of the stochasticity into the proposed scheduling framework in order to consider the variable nature of the production systems

Analysis of tumour ecological balance reveals resource-dependent adaptive strategies of ovarian cancer.

BACKGROUND:Despite treatment advances, there remains a significant risk of recurrence in ovarian cancer, at which stage it is usually incurable. Consequently, there is a clear need for improved patient stratification. However, at present clinical prognosticators remain largely unchanged due to the lack of reproducible methods to identify high-risk patients. METHODS:In high-grade serous ovarian cancer patients with advanced disease, we spatially define a tumour ecological balance of stromal resource and immune hazard using high-throughput image and spatial analysis of routine histology slides. On this basis an EcoScore is developed to classify tumours by a shift in this balance towards cancer-favouring or inhibiting conditions. FINDINGS:The EcoScore provides prognostic value stronger than, and independent of, known risk factors. Crucially, the clinical relevance of mutational burden and genomic instability differ under different stromal resource conditions, suggesting that the selective advantage of these cancer hallmarks is dependent on the context of stromal spatial structure. Under a high resource condition defined by a high level of geographical intermixing of cancer and stromal cells, selection appears to be driven by point mutations; whereas, in low resource tumours featured with high hypoxia and low cancer-immune co-localization, selection is fuelled by aneuploidy. INTERPRETATION:Our study offers empirical evidence that cancer fitness depends on tumour spatial constraints, and presents a biological basis for developing better assessments of tumour adaptive strategies in overcoming ecological constraints including immune surveillance and hypoxia

Quantification of spatial subclonal interactions enhancing the invasive phenotype of pediatric glioma.

Diffuse midline gliomas (DMGs) are highly aggressive, incurable childhood brain tumors. They present a clinical challenge due to many factors, including heterogeneity and diffuse infiltration, complicating disease management. Recent studies have described the existence of subclonal populations that may co-operate to drive pro-tumorigenic processes such as cellular invasion. However, a precise quantification of subclonal interactions is lacking, a problem that extends to other cancers. In this study, we combine spatial computational modeling of cellular interactions during invasion with co-evolution experiments of clonally disassembled patient-derived DMG cells. We design a Bayesian inference framework to quantify spatial subclonal interactions between molecular and phenotypically distinct lineages with different patterns of invasion. We show how this approach could discriminate genuine interactions, where one clone enhanced the invasive phenotype of another, from those apparently only due to the complex dynamics of spatially restricted growth. This study provides a framework for the quantification of subclonal interactions in DMG

Resolving genetic heterogeneity in cancer

To a large extent, cancer conforms to evolutionary rules defined by the rates at which clones mutate, adapt and grow. Next-generation sequencing has provided a snapshot of the genetic landscape of most cancer types, and cancer genomics approaches are driving new insights into cancer evolutionary patterns in time and space. In contrast to species evolution, cancer is a particular case owing to the vast size of tumour cell populations, chromosomal instability and its potential for phenotypic plasticity. Nevertheless, an evolutionary framework is a powerful aid to understand cancer progression and therapy failure. Indeed, such a framework could be applied to predict individual tumour behaviour and support treatment strategies

Il principio della rilevanza nella redazione del bilancio di esercizio

Il contributo analizza la formalizzazione del principio della rilevanza nella redazione del bilancio di esercizio alla luce del recepimento della Direttiva 2013/34/UE; l\u2019analisi \ue8 aggiornata con le previsioni del principio contabile nazionale OIC 11 approvato a marzo 2018 e con il Conceptual Framework 2018 dello IASB.The paper analyzes the formalization of the materiality principle in the preparation of the financial statements in light of the implementation of Directive 2013/34 / EU; the paper is updated with the provisions of the OIC 11 approved in March 2018 and with the 2018 Conceptual Framework of the IASB