scholarly journals Inferring restrictions in the temporal order of mutations during tumor progression: effects of passengers, evolutionary models, and sampling

2014 ◽  
Author(s):  
Ramon Diaz-Uriarte
Keyword(s):  
Tumor Progression ◽  
Cancer Progression ◽  
Performance Metrics ◽  
Simulated Data ◽  
Evolutionary Model ◽  
Data Sets ◽  
Evolutionary Models ◽  
Cross Sectional ◽  
Order Restrictions ◽  
Sampling Schemes

Cancer progression is caused by the sequential accumulation of mutations, but not all orders of accumulation of mutations are equally likely. When the fixation of some mutations depends on the presence of previous ones, identifying restrictions in the order of accumulation of mutations can lead to the discovery of therapeutic targets and diagnostic markers. Using simulated data sets, I conducted a comprehensive comparison of the performance of all available methods to identify these restrictions from cross-sectional data. In contrast to previous work, I embedded restrictions within evolutionary models of tumor progression that included passengers (mutations not responsible for the development of cancer, known to be very common). This allowed me to asses the effects of having to filter out passengers, of sampling schemes, and of deviations from order restrictions. Poor choices of method, filtering, and sampling lead to large errors in all performance metrics. Having to filter passengers lead to decreased performance, especially because true restrictions were missed. Overall, the best method for identifying order restrictions were Oncogenetic Trees, a fast and easy to use method that, although unable to recover dependencies of mutations on more than one mutation, showed good performance in most scenarios, superior to Conjunctive Bayesian Networks and Progression Networks. Single cell sampling provided no advantage, but sampling in the final stages of the disease vs.\ sampling at different stages had severe effects. Evolutionary model and deviations from order restrictions had major, and sometimes counterintuitive, interactions with other factors that affected performance. This paper provides practical recommendations for using these methods with experimental data. Moreover, it shows that it is both possible and necessary to embed assumptions about order restrictions and the nature of driver status within evolutionary models of cancer progression to evaluate the performance of inferential approaches.

scholarly journals Every which way? On predicting tumor evolution using cancer progression models

2018 ◽  
Author(s):  
Ramon Diaz-Uriarte ◽  
Claudia Vasallo
Keyword(s):  
Tumor Progression ◽  
Disease Progression ◽  
Cancer Progression ◽  
Genetic Alterations ◽  
Fitness Landscapes ◽  
Data Sets ◽  
Sample Sizes ◽  
Cross Sectional ◽  
Cancer Data ◽  
Therapeutic Decisions

AbstractSuccessful prediction of the likely paths of tumor progression is valuable for diagnostic, prognostic, and treatment purposes. Cancer progression models (CPMs) use cross-sectional samples to identify restrictions in the order of accumulation of driver mutations and thus CPMs encode the paths of tumor progression. Here we analyze the performance of four CPMs to examine whether they can be used to predict the true distribution of paths of tumor progression and to estimate evolutionary unpredictability. Employing simulations we show that if fitness landscapes are single peaked (have a single fitness maximum) there is good agreement between true and predicted distributions of paths of tumor progression when sample sizes are large, but performance is poor with the currently common much smaller sample sizes. Under multi-peaked fitness landscapes (i.e., those with multiple fitness maxima), performance is poor and improves only slightly with sample size. In all cases, detection regime (when tumors are sampled) is a key determinant of performance. Estimates of evolutionary unpredictability from the best performing CPM, among the four examined, tend to overestimate the true un-predictability and the bias is affected by detection regime; CPMs could be useful for estimating upper bounds to the true evolutionary unpredictability. Analysis of twenty-two cancer data sets shows low evolutionary unpredictability for several of the data sets. But most of the predictions of paths of tumor progression are very unreliable, and unreliability increases with the number of features analyzed. Our results indicate that CPMs could be valuable tools for predicting cancer progression but that, currently, obtaining useful predictions of paths of tumor progression from CPMs is dubious, and emphasize the need for methodological work that can account for the probably multi-peaked fitness landscapes in cancer.Author SummaryKnowing the likely paths of tumor progression is instrumental for cancer precision medicine as it would allow us to identify genetic targets that block disease progression and to improve therapeutic decisions. Direct information about paths of tumor progression is scarce, but cancer progression models (CPMs), which use as input cross-sectional data on genetic alterations, can be used to predict these paths. CPMs, however, make assumptions about fitness landscapes (genotype-fitness maps) that might not be met in cancer. We examine if four CPMs can be used to predict successfully the distribution of tumor progression paths; we find that some CPMs work well when sample sizes are large and fitness landscapes have a single fitness maximum, but in fitness landscapes with multiple fitness maxima prediction is poor. However, the best performing CPM in our study could be used to estimate evolutionary unpredictability. When we apply the best performing CPM in our study to twenty-two cancer data sets we find that predictions are generally unreliable but that some cancer data sets show low unpredictability. Our results highlight that CPMs could be valuable tools for predicting disease progression, but emphasize the need for methodological work to account for multi-peaked fitness landscapes.

scholarly journals Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next?

2020 ◽  
Author(s):  
Juan Diaz-Colunga ◽  
Ramon Diaz-Uriarte
Keyword(s):  
Tumor Progression ◽  
Cancer Progression ◽  
Fitness Landscape ◽  
Tumor Evolution ◽  
Data Sets ◽  
Short Term ◽  
Global Features ◽  
Cross Sectional ◽  
Cancer Data ◽  
Landscape Characteristics

AbstractAccurate prediction of tumor progression is key for adaptive therapy and precision medicine. Cancer progression models (CPMs) can be used to infer dependencies in mutation accumulation from cross-sectional data and provide predictions of tumor progression paths. But their performance when predicting the complete evolutionary paths is limited by violations of assumptions and the size of available data sets. Instead of predicting full tumor progression paths, we can focus on short-term predictions, more relevant for diagnostic and therapeutic purposes. Here we examine if five distinct CPMs can be used to answer the question “Given that a genotype with n mutations has been observed, what genotype with n + 1 mutations is next in the path of tumor progression” or, shortly, “What genotype comes next”. Using simulated data we find that under specific combinations of genotype and fitness landscape characteristics CPMs can provide predictions of short-term evolution that closely match the true probabilities, and that some genotype characteristics (fitness and probability of being a local fitness maximum) can be much more relevant than global features. Thus, CPMs can provide short-term predictions even when global, long-term predictions are not possible because fitness landscape- and evolutionary model-specific assumptions are violated. When good performance is possible, we observe significant variation in the quality of predictions of different methods. Genotype-specific and global fitness landscape characteristics are required to determine which method provides best results in each case. Application of these methods to 25 cancer data sets shows that their use is hampered by lack of the information needed to make principled decisions about method choice and what predictions to trust. Fruitful use of these methods for short-term predictions requires adapting method’s use to local genotype characteristics and obtaining reliable indicators of performance; it will also be necessary to clarify the interpretation of the method’s results when key assumptions do not hold.

scholarly journals Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next?

2021 ◽  
Vol 17 (12) ◽  
pp. e1009055
Author(s):  
Juan Diaz-Colunga ◽  
Ramon Diaz-Uriarte
Keyword(s):  
Tumor Progression ◽  
Cancer Progression ◽  
Fitness Landscape ◽  
Tumor Evolution ◽  
Data Sets ◽  
Short Term ◽  
Global Features ◽  
Cross Sectional ◽  
Cancer Data ◽  
Landscape Characteristics

Accurate prediction of tumor progression is key for adaptive therapy and precision medicine. Cancer progression models (CPMs) can be used to infer dependencies in mutation accumulation from cross-sectional data and provide predictions of tumor progression paths. However, their performance when predicting complete evolutionary trajectories is limited by violations of assumptions and the size of available data sets. Instead of predicting full tumor progression paths, here we focus on short-term predictions, more relevant for diagnostic and therapeutic purposes. We examine whether five distinct CPMs can be used to answer the question “Given that a genotype with n mutations has been observed, what genotype with n + 1 mutations is next in the path of tumor progression?” or, shortly, “What genotype comes next?”. Using simulated data we find that under specific combinations of genotype and fitness landscape characteristics CPMs can provide predictions of short-term evolution that closely match the true probabilities, and that some genotype characteristics can be much more relevant than global features. Application of these methods to 25 cancer data sets shows that their use is hampered by a lack of information needed to make principled decisions about method choice. Fruitful use of these methods for short-term predictions requires adapting method’s use to local genotype characteristics and obtaining reliable indicators of performance; it will also be necessary to clarify the interpretation of the method’s results when key assumptions do not hold.

scholarly journals Efficient detection of repeating sites to accelerate phylogenetic likelihood calculations

2016 ◽  
Author(s):  
Kassian Kobert ◽  
Alexandros Stamatakis ◽  
Tomáš Flouri
Keyword(s):  
Evolutionary Biology ◽  
Likelihood Function ◽  
Simulated Data ◽  
Evolutionary Model ◽  
Identical Result ◽  
Model Parameters ◽  
Data Sets ◽  
Efficient Detection ◽  
Novel Method ◽  
Computational Bottleneck

The phylogenetic likelihood function is the major computational bottleneck in several applications of evolutionary biology such as phylogenetic inference, species delimitation, model selection and divergence times estimation. Given the alignment, a tree and the evolutionary model parameters, the likelihood function computes the conditional likelihood vectors for every node of the tree. Vector entries for which all input data are identical result in redundant likelihood operations which, in turn, yield identical conditional values. Such operations can be omitted for improving run-time and, using appropriate data structures, reducing memory usage. We present a fast, novel method for identifying and omitting such redundant operations in phylogenetic likelihood calculations, and assess the performance improvement and memory saving attained by our method. Using empirical and simulated data sets, we show that a prototype implementation of our method yields up to 10-fold speedups and uses up to 78% less memory than one of the fastest and most highly tuned implementations of the phylogenetic likelihood function currently available. Our method is generic and can seamlessly be integrated into any phylogenetic likelihood implementation.

scholarly journals Cancer progression models and fitness landscapes: a many-to-many relationship

2017 ◽  
Author(s):  
Ramon Diaz-Uriarte
Keyword(s):  
Cancer Progression ◽  
Fitness Landscape ◽  
Simulated Data ◽  
Directed Acyclic Graphs ◽  
Fitness Landscapes ◽  
Gene Interactions ◽  
Cross Sectional ◽  
Data Set ◽  
Large Variability ◽  
Acyclic Graphs

AbstractThe identification of constraints, due to gene interactions, in the order of accumulation of mutations during cancer progression can allow us to single out therapeutic targets. Cancer progression models (CPMs) use genotype frequency data from cross-sectional samples to try to identify these constraints, and return Directed Acyclic Graphs (DAGs) of genes. On the other hand, fitness landscapes, which map genotypes to fitness, contain all possible paths of tumor progression. Thus, we expect a correspondence between DAGs from CPMs and the fitness landscapes where evolution happened. But many fitness landscapes —e.g., those with reciprocal sign epistasis— cannot be represented by CPMs. Using simulated data under 500 fitness landscapes, I show that CPMs’ performance (prediction of genotypes that can exist) degrades with reciprocal sign epistasis. There is large variability in the DAGs inferred from each landscape, which is also affected by mutation rate, detection regime, and fitness landscape features, in ways that depend on CPM method. And the same DAG is often observed in very different landscapes, which differ in more than 50% of their accessible genotypes. Using a pancreatic data set, I show that this many-to-many relationship affects the analysis of empirical data. Fitness landscapes that are widely different from each other can, when evolutionary processes run repeatedly on them, both produce data similar to the empirically observed one, and lead to DAGs that are very different among themselves. Because reciprocal sign epistasis can be common in cancer, these results question the use and interpretation of CPMs.

Mean reversion in corporate leverage: evidence from India

2019 ◽  
Vol 45 (9) ◽  
pp. 1183-1198
Author(s):  
Gaurav S. Chauhan ◽  
Pradip Banerjee
Keyword(s):  
Capital Structure ◽  
Emerging Market ◽  
Simulated Data ◽  
Mean Reversion ◽  
Developed Countries ◽  
Data Sets ◽  
Debt Ratio ◽  
Testing Strategy ◽  
Content Type ◽  
Financing Behavior

Purpose Recent papers on target capital structure show that debt ratio seems to vary widely in space and time, implying that the functional specifications of target debt ratios are of little empirical use. Further, target behavior cannot be adjudged correctly using debt ratios, as they could revert due to mechanical reasons. The purpose of this paper is to develop an alternative testing strategy to test the target capital structure. Design/methodology/approach The authors make use of a major “shock” to the debt ratios as an event and think of a subsequent reversion as a movement toward a mean or target debt ratio. By doing this, the authors no longer need to identify target debt ratios as a function of firm-specific variables or any other rigid functional form. Findings Similar to the broad empirical evidence in developed economies, there is no perceptible and systematic mean reversion by Indian firms. However, unlike developed countries, proportionate usage of debt to finance firms’ marginal financing deficits is extensive; equity is used rather sparingly. Research limitations/implications The trade-off theory could be convincingly refuted at least for the emerging market of India. The paper here stimulated further research on finding reasons for specific financing behavior of emerging market firms. Practical implications The results show that the firms’ financing choices are not only depending on their own firm’s specific variables but also on the financial markets in which they operate. Originality/value This study attempts to assess mean reversion in debt ratios in a unique but reassuring manner. The results are confirmed by extensive calibration of the testing strategy using simulated data sets.

scholarly journals The Role of Cancer-Associated Fibroblasts in Tumor Progression

Cancers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1399
Author(s):  
Rushikesh S. Joshi ◽  
Samanvi S. Kanugula ◽  
Sweta Sudhir ◽  
Matheus P. Pereira ◽  
Saket Jain ◽  
...  
Keyword(s):  
Cancer Treatment ◽  
Tumor Progression ◽  
Cancer Progression ◽  
Immune Cell ◽  
Systemic Disease ◽  
Genomic Medicine ◽  
Therapeutic Targets ◽  
Therapy Resistance ◽  
Cancer Associated Fibroblasts ◽  
Breast Cancers

In the era of genomic medicine, cancer treatment has become more personalized as novel therapeutic targets and pathways are identified. Research over the past decade has shown the increasing importance of how the tumor microenvironment (TME) and the extracellular matrix (ECM), which is a major structural component of the TME, regulate oncogenic functions including tumor progression, metastasis, angiogenesis, therapy resistance, and immune cell modulation, amongst others. Within the TME, cancer-associated fibroblasts (CAFs) have been identified in several systemic cancers as critical regulators of the malignant cancer phenotype. This review of the literature comprehensively profiles the roles of CAFs implicated in gastrointestinal, endocrine, head and neck, skin, genitourinary, lung, and breast cancers. The ubiquitous presence of CAFs highlights their significance as modulators of cancer progression and has led to the subsequent characterization of potential therapeutic targets, which may help advance the cancer treatment paradigm to determine the next generation of cancer therapy. The aim of this review is to provide a detailed overview of the key roles that CAFs play in the scope of systemic disease, the mechanisms by which they enhance protumoral effects, and the primary CAF-related markers that may offer potential targets for novel therapeutics.

scholarly journals Iron-Bound Lipocalin-2 from Tumor-Associated Macrophages Drives Breast Cancer Progression Independent of Ferroportin

Metabolites ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 180
Author(s):  
Christina Mertens ◽  
Matthias Schnetz ◽  
Claudia Rehwald ◽  
Stephan Grein ◽  
Eiman Elwakeel ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Tumor Progression ◽  
Tumor Cells ◽  
Cancer Progression ◽  
Lung Metastases ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Lipocalin 2 ◽  
Tumor Associated Macrophages

Macrophages supply iron to the breast tumor microenvironment by enforced secretion of lipocalin-2 (Lcn-2)-bound iron as well as the increased expression of the iron exporter ferroportin (FPN). We aimed at identifying the contribution of each pathway in supplying iron for the growing tumor, thereby fostering tumor progression. Analyzing the expression profiles of Lcn-2 and FPN using the spontaneous polyoma-middle-T oncogene (PyMT) breast cancer model as well as mining publicly available TCGA (The Cancer Genome Atlas) and GEO Series(GSE) datasets from the Gene Expression Omnibus database (GEO), we found no association between tumor parameters and Lcn-2 or FPN. However, stromal/macrophage-expression of Lcn-2 correlated with tumor onset, lung metastases, and recurrence, whereas FPN did not. While the total iron amount in wildtype and Lcn-2−/− PyMT tumors showed no difference, we observed that tumor-associated macrophages from Lcn-2−/− compared to wildtype tumors stored more iron. In contrast, Lcn-2−/− tumor cells accumulated less iron than their wildtype counterparts, translating into a low migratory and proliferative capacity of Lcn-2−/− tumor cells in a 3D tumor spheroid model in vitro. Our data suggest a pivotal role of Lcn-2 in tumor iron-management, affecting tumor growth. This study underscores the role of iron for tumor progression and the need for a better understanding of iron-targeted therapy approaches.

scholarly journals Aging of Bone Marrow Mesenchymal Stromal Cells: Hematopoiesis Disturbances and Potential Role in the Development of Hematologic Cancers

Cancers ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 68
Author(s):  
Fulvio Massaro ◽  
Florent Corrillon ◽  
Basile Stamatopoulos ◽  
Nathalie Meuleman ◽  
Laurence Lagneaux ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Tumor Progression ◽  
Cancer Progression ◽  
Mesenchymal Stromal Cells ◽  
Stromal Cells ◽  
Cell Communication ◽  
Bone Marrow Niche ◽  
Hematopoietic Stem ◽  
Wide Range ◽  
Major Player

Aging of bone marrow is a complex process that is involved in the development of many diseases, including hematologic cancers. The results obtained in this field of research, year after year, underline the important role of cross-talk between hematopoietic stem cells and their close environment. In bone marrow, mesenchymal stromal cells (MSCs) are a major player in cell-to-cell communication, presenting a wide range of functionalities, sometimes opposite, depending on the environmental conditions. Although these cells are actively studied for their therapeutic properties, their role in tumor progression remains unclear. One of the reasons for this is that the aging of MSCs has a direct impact on their behavior and on hematopoiesis. In addition, tumor progression is accompanied by dynamic remodeling of the bone marrow niche that may interfere with MSC functions. The present review presents the main features of MSC senescence in bone marrow and their implications in hematologic cancer progression.

scholarly journals Stromal CCL5 Promotes Breast Cancer Progression by Interacting with CCR3 in Tumor Cells

2021 ◽  
Vol 22 (4) ◽  
pp. 1918
Author(s):  
Mio Yamaguchi ◽  
Kiyoshi Takagi ◽  
Koki Narita ◽  
Yasuhiro Miki ◽  
Yoshiaki Onodera ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Carcinoma ◽  
Tumor Progression ◽  
Tumor Cells ◽  
Cancer Progression ◽  
Stromal Cells ◽  
Human Breast Carcinoma ◽  
Breast Cancer Patients ◽  
Breast Carcinomas ◽  
Human Breast

Chemokines secreted from stromal cells have important roles for interactions with carcinoma cells and regulating tumor progression. C-C motif chemokine ligand (CCL) 5 is expressed in various types of stromal cells and associated with tumor progression, interacting with C-C chemokine receptor (CCR) 1, 3 and 5 expressed in tumor cells. However, the expression on CCL5 and its receptors have so far not been well-examined in human breast carcinoma tissues. We therefore immunolocalized CCL5, as well as CCR1, 3 and 5, in 111 human breast carcinoma tissues and correlated them with clinicopathological characteristics. Stromal CCL5 immunoreactivity was significantly correlated with the aggressive phenotype of breast carcinomas. Importantly, this tendency was observed especially in the CCR3-positive group. Furthermore, the risk of recurrence was significantly higher in the patients with breast carcinomas positive for CCL5 and CCR3 but negative for CCR1 and CCR5, as compared with other patients. In summary, the CCL5-CCR3 axis might contribute to a worse prognosis in breast cancer patients, and these findings will contribute to a better understanding of the significance of the CCL5/CCRs axis in breast carcinoma microenvironment.

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