scholarly journals Algorithmic Methods to Infer the Evolutionary Trajectories in Cancer Progression

2015 ◽  
Author(s):  
Giulio Caravagna ◽  
Alex Graudenzi ◽  
DANIELE RAMAZZOTTI ◽  
Rebeca Sanz-Pamplona ◽  
Luca De Sano ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Current Knowledge ◽  
Population Level ◽  
Selective Advantage ◽  
Explanatory Models ◽  
Driver Mutations ◽  
Cross Sectional ◽  
Progression Model ◽  
Colorectal Cancer Progression ◽  
Ngs Data

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.

scholarly journals Algorithmic methods to infer the evolutionary trajectories in cancer progression

2016 ◽  
Vol 113 (28) ◽  
pp. E4025-E4034 ◽  
Author(s):  
Giulio Caravagna ◽  
Alex Graudenzi ◽  
Daniele Ramazzotti ◽  
Rebeca Sanz-Pamplona ◽  
Luca De Sano ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Current Knowledge ◽  
Population Level ◽  
Selective Advantage ◽  
Explanatory Models ◽  
Next Generation Sequencing Data ◽  
Driver Mutations ◽  
Sequencing Data ◽  
Cross Sectional ◽  
Progression Model

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.

scholarly journals CAPRI: Efficient Inference of Cancer Progression Models from Cross-sectional Data

2014 ◽  
Author(s):  
Daniele Ramazzotti ◽  
Giulio Caravagna ◽  
Loes Olde Loohuis ◽  
Alex Graudenzi ◽  
Ilya Korsunsky ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Directed Acyclic Graphs ◽  
The Cancer Genome Atlas ◽  
Scoring Method ◽  
Cross Sectional ◽  
Progression Model ◽  
Acyclic Graphs ◽  
Cancer Genome Atlas ◽  
Atypical Chronic Myeloid Leukemia ◽  
Insight Into

We devise a novel inference algorithm to effectively solve the cancer progression model reconstruction problem. Our empirical analysis of the accuracy and convergence rate of our algorithm, CAncer PRogression Inference (CAPRI), shows that it outperforms the state-of-the-art algorithms addressing similar problems. Motivation: Several cancer-related genomic data have become available (e.g., The Cancer Genome Atlas, TCGA) typically involving hundreds of patients. At present, most of these data are aggregated in a cross-sectional fashion providing all measurements at the time of diagnosis. Our goal is to infer cancer ?progression? models from such data. These models are represented as directed acyclic graphs (DAGs) of collections of ?selectivity? relations, where a mutation in a gene A ?selects? for a later mutation in a gene B. Gaining insight into the structure of such progressions has the potential to improve both the stratification of patients and personalized therapy choices. Results: The CAPRI algorithm relies on a scoring method based on a probabilistic theory developed by Suppes, coupled with bootstrap and maximum likelihood inference. The resulting algorithm is efficient, achieves high accuracy, and has good complexity, also, in terms of convergence properties. CAPRI performs especially well in the presence of noise in the data, and with limited sample sizes. Moreover CAPRI, in contrast to other approaches, robustly reconstructs different types of confluent trajectories despite irregularities in the data. We also report on an ongoing investigation using CAPRI to study atypical Chronic Myeloid Leukemia, in which we uncovered non trivial selectivity relations and exclusivity patterns among key genomic events.

scholarly journals Genetic and Epigenetic Events Generate Multiple Pathways in Colorectal Cancer Progression

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Massimo Pancione ◽  
Andrea Remo ◽  
Vittorio Colantuoni
Keyword(s):  
Colorectal Cancer ◽  
Cancer Progression ◽  
Cancer Biology ◽  
Current Knowledge ◽  
Alternative Pathway ◽  
Epigenetic Events ◽  
Cells Of Origin ◽  
Common Causes ◽  
Colorectal Cancer Progression

Colorectal cancer (CRC) is one of the most common causes of death, despite decades of research. Initially considered as a disease due to genetic mutations, it is now viewed as a complex malignancy because of the involvement of epigenetic abnormalities. A functional equivalence between genetic and epigenetic mechanisms has been suggested in CRC initiation and progression. A hallmark of CRC is its pathogenetic heterogeneity attained through at least three distinct pathways: a traditional (adenoma-carcinoma sequence), an alternative, and more recently the so-called serrated pathway. While the alternative pathway is more heterogeneous and less characterized, the traditional and serrated pathways appear to be more homogeneous and clearly distinct. One unsolved question in colon cancer biology concerns the cells of origin and from which crypt compartment the different pathways originate. Based on molecular and pathological evidences, we propose that the traditional and serrated pathways originate from different crypt compartments explaining their genetic/epigenetic and clinicopathological differences. In this paper, we will discuss the current knowledge of CRC pathogenesis and, specifically, summarize the role of genetic/epigenetic changes in the origin and progression of the multiple CRC pathways. Elucidation of the link between the molecular and clinico-pathological aspects of CRC would improve our understanding of its etiology and impact both prevention and treatment.

scholarly journals Design of the TRONCO BioConductor Package for TRanslational ONCOlogy

2015 ◽  
Author(s):  
Marco Antoniotti ◽  
Giulio Caravagna ◽  
Luca De Sano ◽  
Alex Graudenzi ◽  
Giancarlo Mauri ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Graphical Model ◽  
Selective Advantage ◽  
R Package ◽  
Genetic Alterations ◽  
Cross Sectional ◽  
Data Set ◽  
Statistical Confidence ◽  
Translational Oncology ◽  
Set Up

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 patiens (cross-sectional data) have been defined in the literature since late 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 serie 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. Availability. Freely available at http://www.bioconductor.org/ under GPL license; project hosted at http://bimib.disco.unimib.it/ and https://github.com/BIMIB-DISCo/TRONCO. Contact. [email protected]

Diet and colorectal cancer prevention

2000 ◽  
Vol 28 (2) ◽  
pp. 12-16 ◽  
Author(s):  
S. A. Bingham
Keyword(s):  
Colorectal Cancer ◽  
Cancer Progression ◽  
Prospective Studies ◽  
Phase 1 ◽  
Nitroso Compounds ◽  
Case Control Studies ◽  
Cross Sectional ◽  
Guanine Adducts ◽  
Colorectal Cancer Prevention ◽  
Colorectal Cancer Progression

The majority of cancers are sporadic and epidemiological estimates suggest that up to 80% of colorectal cancer is attributable to diet. Epidemiologically, cross-sectional comparisons, case-control studies and trends in food intake show high rates of colorectal cancer in populations consuming diets high in meat and fat, and low in starch, NSP (non-starch polysaccharides, fibre) and vegetables. In general, prospective studies tend to support these findings although estimates of relative risk are not high. Existing prospective studies have however used crude indices of diet subject to substantial measurement error, and interactions with genetic polymorphisms in, for example, phase-1 and -II enzymes have been studied only rarely. The association between meat consumption and colorectal cancer is usually attributed to the formation of heterocyclic amines in meat when it is cooked. In addition, in humans high-meat diets increase the level of nitrosatable material entering the colon so that faecal N-nitroso compounds (NOCs) increase in a dose-responsive manner following endogenous synthesis in the colon. Some of the mutations and guanine adducts accumulated during colorectal cancer progression are characteristic of alkylative damage, which would be compatible with NOC exposure. To date, NSP, resistant starch and vegetables have not reduced faecal NOC levels.

scholarly journals Mechanism of Apoptosis Induced by Curcumin in Colorectal Cancer

2019 ◽  
Vol 20 (10) ◽  
pp. 2454 ◽  
Author(s):  
Nor Isnida Ismail ◽  
Iekhsan Othman ◽  
Faridah Abas ◽  
Nordin H. Lajis ◽  
Rakesh Naidu
Keyword(s):  
Colorectal Cancer ◽  
Side Effects ◽  
Signaling Pathways ◽  
Cancer Progression ◽  
Current Knowledge ◽  
Current Treatment ◽  
Treatment Modalities ◽  
Ancient Medicine ◽  
Colorectal Cancer Progression ◽  
Apoptosis Regulation

Colorectal cancer (CRC) is among the top three cancer with higher incident and mortality rate worldwide. It is estimated that about over than 1.1 million of death and 2.2 million new cases by the year 2030. The current treatment modalities with the usage of chemo drugs such as FOLFOX and FOLFIRI, surgery and radiotherapy, which are usually accompanied with major side effects, are rarely cured along with poor survival rate and at higher recurrence outcome. This trigger the needs of exploring new natural compounds with anti-cancer properties which possess fewer side effects. Curcumin, a common spice used in ancient medicine was found to induce apoptosis by targeting various molecules and signaling pathways involved in CRC. Disruption of the homeostatic balance between cell proliferation and apoptosis could be one of the promoting factors in colorectal cancer progression. In this review, we describe the current knowledge of apoptosis regulation by curcumin in CRC with regard to molecular targets and associated signaling pathways.

scholarly journals Oncogenetic Network Estimation with Disjunctive Bayesian Networks: Learning from Unstratified Samples while Preserving Mutual Exclusivity Relations

2020 ◽  
Author(s):  
Phillip B. Nicol ◽  
Kevin R. Coombes ◽  
Courtney Deaver ◽  
Oksana A. Chkrebtii ◽  
Subhadeep Paul ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Synthetic Data ◽  
Ground Truth ◽  
Genetic Alterations ◽  
Mutual Exclusivity ◽  
Cross Sectional ◽  
Cancer Subtypes ◽  
Cancer Data ◽  
Progression Model ◽  
Network Estimation

ABSTRACTCancer is the process of accumulating genetic alterations that confer selective advantages to tumor cells. The order in which aberrations occur is not arbitrary, and inferring the order of events is a challenging problem due to the lack of longitudinal samples from tumors. Moreover, a network model of oncogenesis should capture biological facts such as distinct progression trajectories of cancer subtypes and patterns of mutual exclusivity of alterations in the same pathways. In this paper, we present the Disjunctive Bayesian Network (DBN), a novel cancer progression model. Unlike previous models of oncogenesis, DBN naturally captures mutually exclusive alterations. Besides, DBN is flexible enough to represent progression trajectories of cancer subtypes, therefore allowing one to learn the progression network from unstratified data, i.e., mixed samples from multiple subtypes. We provide a scalable genetic algorithm to learn the structure of DBN from cross-sectional cancer data. To test our model, we simulate synthetic data from known progression networks and show that our algorithm infers the ground truth network with high accuracy. Finally, we apply our model to copy number data for colon cancer and mutation data for bladder cancer and observe that the recovered progression network matches known biological facts.

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

2015 ◽  
Author(s):  
Luca De Sano ◽  
Giulio Caravagna ◽  
Daniele Ramazzotti ◽  
Alex Graudenzi ◽  
Giancarlo Mauri ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Population Level ◽  
R Package ◽  
Sequencing Data ◽  
Cross Sectional ◽  
Individual Level ◽  
Link Type ◽  
Multiple Biopsies ◽  
Evolutionary Trajectories ◽  
Multiple Samples

AbstractMotivationWe 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.AvailabilityTRONCO is released under the GPL license, it is hosted in the Software section at http://bimib.disco.unimib.it/ and archived also at [email protected]

scholarly journals Behavioural Difficulties in Children and Adolescents with Mental Disorders under Extreme Situations

2021 ◽  
Vol 10 (21) ◽  
pp. 4876
Author(s):  
Pablo Gonzalez-Domenech ◽  
José Luis Romero-Béjar ◽  
Luis Gutierrez-Rojas ◽  
Sara Jimenez-Fernandez ◽  
Francisco Diaz-Atienza
Keyword(s):  
Mental Disorders ◽  
Children And Adolescents ◽  
Psychological Impact ◽  
Population Level ◽  
Cross Sectional Study ◽  
Explanatory Models ◽  
Cross Sectional ◽  
Explanatory Variables ◽  
Behavioural Difficulties ◽  
Categorical Response

In 2020, the Governments of many countries maintained different levels of confinement of the population due to the pandemic that produced the COVID-19. There are few studies published on the psychological impact in the child and adolescent population diagnosed with mental disorders, especially during the home confinement stage. Explanatory models based on socio-demographic and clinical variables provide an approximation to level changes in different dimensions of behavioural difficulties. A categorical-response logistic ordinal regression model, based on a cross-sectional study with 139 children and adolescents diagnosed with mental disorders is performed for each dimension under analysis. Most of the socio-demographic and clinical explanatory variables considered (24 of 26) were significant at population level for at least one of the four dimensions of behavioural difficulties (15 response variables) under analysis. Odds-ratios were interpreted to identify risk or protective factors increasing or decreasing severity in the response variable. This analysis provides useful information, making it possible to more readily anticipate critical situations due to extreme events, such as a confinement, in this population.

scholarly journals Bone morphogenetic proteins, breast cancer, and bone metastases: striking the right balance

2017 ◽  
Vol 24 (10) ◽  
pp. R349-R366 ◽  
Author(s):  
Catherine Zabkiewicz ◽  
Jeyna Resaul ◽  
Rachel Hargest ◽  
Wen Guo Jiang ◽  
Lin Ye
Keyword(s):  
Breast Cancer ◽  
Cancer Progression ◽  
Bone Morphogenetic Proteins ◽  
Therapeutic Potential ◽  
Current Knowledge ◽  
Breast Tumour ◽  
Cellular Functions ◽  
Foetal Development ◽  
Key Factor ◽  
The Right

Bone morphogenetic proteins (BMPs) belong to the TGF-β super family, and are essential for the regulation of foetal development, tissue differentiation and homeostasis and a multitude of cellular functions. Naturally, this has led to the exploration of aberrance in this highly regulated system as a key factor in tumourigenesis. Originally identified for their role in osteogenesis and bone turnover, attention has been turned to the potential role of BMPs in tumour metastases to, and progression within, the bone niche. This is particularly pertinent to breast cancer, which commonly metastasises to bone, and in which studies have revealed aberrations of both BMP expression and signalling, which correlate clinically with breast cancer progression. Ultimately a BMP profile could provide new prognostic disease markers. As the evidence suggests a role for BMPs in regulating breast tumour cellular function, in particular interactions with tumour stroma and the bone metastatic microenvironment, there may be novel therapeutic potential in targeting BMP signalling in breast cancer. This review provides an update on the current knowledge of BMP abnormalities and their implication in the development and progression of breast cancer, particularly in the disease-specific bone metastasis.

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