Algorithmic methods to infer the evolutionary trajectories in cancer progression

The genomic evolution inherent to cancer relates directly to a renewed focus on the voluminous next-generation sequencing data and machine learning for the inference of explanatory models of how the (epi)genomic events are choreographed in cancer initiation and development. However, despite the increasing availability of multiple additional -omics data, this quest has been frustrated by various theoretical and technical hurdles, mostly stemming from the dramatic heterogeneity of the disease. In this paper, we build on our recent work on the 'selective advantage' relation among driver mutations in cancer progression and investigate its applicability to the modeling problem at the population level. Here, we introduce PiCnIc (Pipeline for Cancer Inference), a versatile, modular, and customizable pipeline to extract ensemble-level progression models from cross-sectional sequenced cancer genomes. The pipeline has many translational implications because it combines state-of-the-art techniques for sample stratification, driver selection, identification of fitness-equivalent exclusive alterations, and progression model inference. We demonstrate PiCnIc's ability to reproduce much of the current knowledge on colorectal cancer progression as well as to suggest novel experimentally verifiable hypotheses

Design of the TRONCO BioConductor Package for TRanslational ONCOlogy

Models of cancer progression provide insights on the order of accumulation of genetic alterations during cancer development. Algorithms to infer such models from the currently available mutational profiles collected from different cancer patients (cross-sectional data) have been defined in the literature since late the 90s. These algorithms differ in the way they extract a graphical model of the events modelling the progression, e.g., somatic mutations or copy-number alterations. TRONCO is an R package for TRanslational ONcology which provides a series of functions to assist the user in the analysis of cross-sectional genomic data and, in particular, it implements algorithms that aim to model cancer progression by means of the notion of selective advantage. These algorithms are proved to outperform the current state-of-the-art in the inference of cancer progression models. TRONCO also provides functionalities to load input cross-sectional data, set up the execution of the algorithms, assess the statistical confidence in the results, and visualize the models

TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data

Motivation: We introduce TRONCO (TRanslational ONCOlogy), an open-source R package that implements the state-of-the-art algorithms for the inference of cancer progression models from (epi)genomic mutational profiles. TRONCO can be used to extract population-level models describing the trends of accumulation of alterations in a cohort of cross-sectional samples, e.g., retrieved from publicly available databases, and individual-level models that reveal the clonal evolutionary history in single cancer patients, when multiple samples, e.g., multiple biopsies or single-cell sequencing data, are available. The resulting models can provide key hints in uncovering the evolutionary trajectories of cancer, especially for precision medicine or personalized therapy. Availability: TRONCO is released under the GPL license, it is hosted in the Software section at http://bimib.disco.unimib.it/ and archived also at bioconductor.org. Contact: [email protected]

Algorithmic methods to infer the evolutionary trajectories in cancer progression

The genomic evolution inherent to cancer relates directly to a renewed focus on the voluminous next generation sequencing (NGS) data, and machine learning for the inference of explanatory models of how the (epi)genomic events are choreographed in cancer initiation and development. However, despite the increasing availability of multiple additional -omics data, this quest has been frustrated by various theoretical and technical hurdles, mostly stemming from the dramatic heterogeneity of the disease. In this paper, we build on our recent works on "selective advantage" relation among driver mutations in cancer progression and investigate its applicability to the modeling problem at the population level. Here, we introduce PiCnIc (Pipeline for Cancer Inference), a versatile, modular and customizable pipeline to extract ensemble-level progression models from cross-sectional sequenced cancer genomes. The pipeline has many translational implications as it combines state-of-the-art techniques for sample stratification, driver selection, identification of fitness-equivalent exclusive alterations and progression model inference. We demonstrate PiCnIc's ability to reproduce much of the current knowledge on colorectal cancer progression, as well as to suggest novel experimentally verifiable hypotheses

A somatic coliphage threshold approach to improve the management of activated sludge wastewater treatment plant effluents in resource-limited regions

Versión aceptada para publicaciónEffective wastewater management is crucial to ensure the safety of water reuse projects and 29 effluent discharge into surface waters. Multiple studies have demonstrated that municipal 30 wastewater treatment with conventional activated sludge processes is inefficient for the removal 31 of the wide spectrum of viruses in sewage. In this study, a well-accepted statistical approach was 32 used to investigate the relationship between viral indicators and human enteric viruses during 33 wastewater treatment in a resource-limited region. Influent and effluent samples from five urban 34 wastewater treatment plants (WWTP) in Costa Rica were analyzed for somatic coliphage and 35 human enterovirus, hepatitis A virus, norovirus genotype I and II, and rotavirus. All WWTP 36 provide primary treatment followed by conventional activated sludge treatment prior to 37 discharge into surface waters that are indirectly used for agricultural irrigation. The results 38 revealed a statistically significant relationship between the detection of at least one of the five 39 human enteric viruses and somatic coliphage. Multiple logistic regression and Receiver Operating Characteristic curve analysis identified a threshold of 3.0 ×103 40 (3.5-log10) somatic 41 coliphage plaque forming unit per 100 mL, which corresponded to an increased likelihood of encountering enteric viruses above the limit of detection (>1.83×102 42 virus target/100 mL). 43 Additionally, quantitative microbial risk assessment was executed for famers indirectly reusing 44 WWTP effluent that met the proposed threshold. The resulting estimated median cumulative 45 annual disease burden complied with World Health Organization recommendations. Future 46 studies are needed to validate the proposed threshold for use in Costa Rica and other regions.Universidad de Costa Rica/[]/UCR/Costa RicaNational Science Foundation/[OCE-1566562]/NSF/Estados UnidosUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias de la Salud::Instituto de Investigaciones en Salud (INISA)UCR::Vicerrectoría de Docencia::Salud::Facultad de Microbiologí

Southern African Large Telescope Spectroscopy of BL Lacs for the CTA project

In the last two decades, very-high-energy gamma-ray astronomy has reached maturity: over 200 sources have been detected, both Galactic and extragalactic, by ground-based experiments. At present, Active Galactic Nuclei (AGN) make up about 40% of the more than 200 sources detected at very high energies with ground-based telescopes, the majority of which are blazars, i.e. their jets are closely aligned with the line of sight to Earth and three quarters of which are classified as high-frequency peaked BL Lac objects. One challenge to studies of the cosmological evolution of BL Lacs is the difficulty of obtaining redshifts from their nearly featureless, continuum-dominated spectra. It is expected that a significant fraction of the AGN to be detected with the future Cherenkov Telescope Array (CTA) observatory will have no spectroscopic redshifts, compromising the reliability of BL Lac population studies, particularly of their cosmic evolution. We started an effort in 2019 to measure the redshifts of a large fraction of the AGN that are likely to be detected with CTA, using the Southern African Large Telescope (SALT). In this contribution, we present two results from an on-going SALT program focused on the determination of BL Lac object redshifts that will be relevant for the CTA observatory