Biology subjects

Diaz-Uriarte, R.

Publications and source records attributed to Diaz-Uriarte, R..

8 recordsLinked to original sources

bioRxiv2026

The hypercubic Mk model in reduced state space for the coupled, reversible coevolution of multiple binary characters

Many scientific questions involve the coevolution of coupled, binary features over time - from phenotypes in evolutionary biology to mutations in cancer development. Evolutionary accumulation models (EvAMs) often neglect reversibility in these systems, uncertainty in observations, and/or phylogenetic connections between observations. By contrast, the Mk model from phylogenetic comparative methods supports reversibility, uncertainty, and relatedness, but compute time scales like O(4L) in number of features L, making it challenging to apply to more than about six coupled, coevolving binary characters. Here, we introduce HyperMk2, a method using output from a Fitch-like parsimony algorithm to reduce the state space associated with many coevolving characters while retaining flexibility, reversibility, and phylogenetic information. This approach, while approximate, scales linearly in the number of distinct observations rather than exponentially in the number of characters, supporting the investigation of much larger systems than previously possible. We demonstrate how this method allows the inference of evolutionary dynamics of anti-microbial resistance in bacteria, including the identification of potential influences between characters, and discuss its broader application.

evolutionary biology↗

bioRxiv2024

A hypercubic Mk model framework for capturing reversibility in disease, cancer, and evolutionary accumulation modelling

Accumulation models, where a system progressively acquires binary features over time, are common in the study of cancer progression, evolutionary biology, and other fields. Many approaches have been developed to infer the accumulation pathways by which features (for example, mutations) are acquired over time. However, most of these approaches do not support reversibility: the loss of a feature once it has been acquired (for example, the clearing of a mutation from a tumour or population). Here, we demonstrate how the well-established Mk model from evolutionary biology, embedded on a hypercubic transition graph, can be used to infer the dynamics of accumulation processes, including the possibility of reversible transitions, from data which may be uncertain and cross-sectional, longitudinal, or phylogenetically / phylogenomically embedded. Positive and negative interactions between arbitrary sets of features (not limited to pairwise interactions) are supported. We demonstrate this approach with synthetic datasets and real data on bacterial drug resistance and cancer progression. While this implementation is limited in the number of features that can be considered, we discuss how this limitation may be relaxed to deal with larger systems.

bioinformatics↗

bioRxiv2024

HyperTraPS-CT: Inference and prediction for accumulation pathways with flexible data and model structures

Accumulation processes, where many potentially coupled features are acquired over time, occur throughout the sciences, from evolutionary biology to disease progression, and particularly in the study of cancer progression. Existing methods for learning the dynamics of such systems typically assume limited (often pairwise) relationships between feature subsets, cross-sectional or untimed observations, small feature sets, or discrete orderings of events. Here we introduce HyperTraPS-CT (Hypercubic Transition Path Sampling in Continuous Time) to compute posterior distributions on continuous-time dynamics of many, arbitrarily coupled, traits in unrestricted state spaces, accounting for uncertainty in observations and their timings. We demonstrate the capacity of HyperTraPS-CT to deal with cross-sectional, longitudinal, and phylogenetic data, which may have no, uncertain, or precisely specified sampling times. HyperTraPS-CT allows positive and negative interactions between arbitrary subsets of features (not limited to pairwise interactions), supporting Bayesian and maximum-likelihood inference approaches to identify these interactions, consequent pathways, and predictions of future and unobserved features. We also introduce a range of visualisations for the inferred outputs of these processes and demonstrate model selection and regularisation for feature interactions. We apply this approach to case studies on the accumulation of mutations in cancer progression and the acquisition of anti-microbial resistance genes in tuberculosis, demonstrating its flexibility and capacity to produce predictions aligned with applied priorities.

bioinformatics↗

bioRxiv2023

Adaptive therapy in cancer: the role of restrictions in the accumulation of mutations

BACKGROUNDCancer is currently one of the leading causes of premature death in the world, and is predicted to continue rising even despite the continuous discovery of novel treatments. New approaches, like adaptive therapy, try to minimize the problem of drug resistance, but there are still many open questions and unstudied phenomena that need to be tackled in order to make this approaches viable in real patients; among these, the possible effects that restrictions in the order of accumulation of mutations could have. RESULTSWe have developed a spatially explicit agent-based model capable of simulating tumor growth and adaptive therapy in a highly flexible way. We show that when we consider restrictions in the order of accumulation of mutations and their effect in tumor architecture, the predicted genotypes of the cells that are inhibiting the growth of resistant cells can be very different to the ones predicted by perfectly mixed models. CONCLUSIONWe identify a divergence between the expected and real genotypes of the cells inhibiting the growth of the resistant population that has not been previously documented. This effect, if not taken into account, could negatively affect our predictions of adaptive therapy success and could hinder our advances in the development of new approaches to improve adaptive therapy. This discovery suggests the need for more studies that take into account the spatial component of cancer, specially when dealing with tumors with high heterogeneity. Furthermore, our model is able to simulate scenarios of tumor development and adaptive therapy, making it useful both for research and for education.

bioinformatics↗

bioRxiv2022

EvAM-Tools: tools for evolutionary accumulation and cancer progression models

EvAM-Tools is an R package and web application that provides a unified interface to state-of-the-art cancer progression models (CPMs) and, more generally, evolutionary models of event accumulation. The output includes, in addition to the fitted models, the transition (and transition rate) matrices between genotypes and the probabilities of evolutionary paths. Generation of random cancer progression models is also available. Using the GUI in the web application, users can easily construct models (modifying Directed Acyclic Graphs --DAGs-- of restrictions, matrices of mutual hazards, or specifying genotype composition), generate data from them (with user-specified observational/genotyping error), and analyze the data. Availability and ImplementationImplemented in R and C; open source code available under the GNU Affero General Public License v3.0 at https://github.com/rdiaz02/EvAM-Tools. Docker images freely available from https://hub.docker.com/u/rdiaz02. Web app freely accessible at https://iib.uam.es/evamtools. Contactr.diaz@uam.es

bioinformatics↗

bioRxiv2020

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

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 methods use to local genotype characteristics and obtaining reliable indicators of performance; it will also be necessary to clarify the interpretation of the methods results when key assumptions do not hold.

bioinformatics↗

bioRxiv2018

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

Successful 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.\n\nAuthor 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.

bioinformatics↗

bioRxiv2017

Cancer Progression Models And Fitness Landscapes: A Many-To-Many Relationship

The 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.

bioinformatics↗