Rtreemix: an R package for estimating evolutionary pathways and genetic progression scores

Resolving tumor evolution: a phylogenetic approach

The evolutionary dynamics of cancer, characterized by its profound heterogeneity, demand sophisticated tools for a holistic understanding. This review delves into tumor phylogenetics, an essential approach bridging evolutionary biology with oncology, offering unparalleled insights into cancer's evolutionary trajectory. We provide an overview of the workflow, encompassing study design, data acquisition, and phylogeny reconstruction. Notably, the integration of diverse data sets emerges as a transformative step, enhancing the depth and breadth of evolutionary insights. With this integrated perspective, tumor phylogenetics stands poised to redefine our understanding of cancer evolution and influence therapeutic strategies.

An artificial intelligence system applied to recurrent cytogenetic aberrations and genetic progression scores predicts MYC rearrangements in large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL), the most common type of non-Hodgkin lymphoma, is characterized by MYC rearrangements (MYC R) in up to 15% of cases, and these have unfavorable prognosis. Due to cryptic rearrangements and variations in MYC breakpoints, MYC R may be undetectable by conventional methods in up to 10%-15% of cases. In this study, a retrospective proof of concept study, we sought to identify recurrent cytogenetic aberrations (RCAs), generate genetic progression scores (GP) from RCAs and apply these to an artificial intelligence (AI) algorithm to predict MYC status in the karyotypes of published cases. The developed AI algorithm is validated for its performance on our institutional cases. In addition, cytogenetic evolution pattern and clinical impact of RCAs was performed. Chromosome losses were associated with MYC-, while partial gain of chromosome 1 was significant in MYC R tumors. MYC R was the sole driver alteration in MYC-rearranged tumors, and evolution patterns revealed RCAs associated with gene expression signatures. A higher GPS value was associated with MYC R tumors. A subsequent AI algorithm (composed of RCAs + GPS) obtained a sensitivity of 91.4 and specificity of 93.8 at predicting MYC R. Analysis of an additional 59 institutional cases with the AI algorithm showed a sensitivity and specificity of 100% and 87% each with positive predictive value of 92%, and a negative predictive value of 100%. Cases with a MYC R showed a shorter survival.

Bone marrow-derived mesenchymal stem cells modulate autophagy in RAW264.7 macrophages via the phosphoinositide 3-kinase/protein kinase B/heme oxygenase-1 signaling pathway under oxygen-glucose deprivation/restoration conditions

Background

Autophagy of alveolar macrophages is a crucial process in ischemia/reperfusion injury-induced acute lung injury (ALI). Bone marrow-derived mesenchymal stem cells (BM-MSCs) are multipotent cells with the potential for repairing injured sites and regulating autophagy. This study was to investigate the influence of BM-MSCs on autophagy of macrophages in the oxygen-glucose deprivation/restoration (OGD/R) microenvironment and to explore the potential mechanism.

Methods

We established a co-culture system of macrophages (RAW264.7) with BM-MSCs under OGD/R conditions in vitro. RAW264.7 cells were transfected with recombinant adenovirus (Ad-mCherry-GFP-LC3B) and autophagic status of RAW264.7 cells was observed under a fluorescence microscope. Autophagy-related proteins light chain 3 (LC3)-I, LC3-II, and p62 in RAW264.7 cells were detected by Western blotting. We used microarray expression analysis to identify the differently expressed genes between OGD/R treated macrophages and macrophages co-culture with BM-MSCs. We investigated the gene heme oxygenase-1 (HO-1), which is downstream of the phosphoinositide 3-kinase/protein kinase B (PI3K/Akt) signaling pathway.

Results

The ratio of LC3-II/LC3-I of OGD/R treated RAW264.7 cells was increased (1.27 ± 0.20 vs. 0.44 ± 0.08, t = 6.67, P < 0.05), while the expression of p62 was decreased (0.77 ± 0.04 vs. 0.95 ± 0.10, t = 2.90, P < 0.05), and PI3K (0.40 ± 0.06 vs. 0.63 ± 0.10, t = 3.42, P < 0.05) and p-Akt/Akt ratio was also decreased (0.39 ± 0.02 vs. 0.58 ± 0.03, t = 9.13, P < 0.05). BM-MSCs reduced the LC3-II/LC3-I ratio of OGD/R treated RAW264.7 cells (0.68 ± 0.14 vs. 1.27 ± 0.20, t = 4.12, P < 0.05), up-regulated p62 expression (1.10 ± 0.20 vs. 0.77 ± 0.04, t = 2.80, P < 0.05), and up-regulated PI3K (0.54 ± 0.05 vs. 0.40 ± 0.06, t = 3.11, P < 0.05) and p-Akt/Akt ratios (0.52 ± 0.05 vs. 0.39 ± 0.02, t = 9.13, P < 0.05). A whole-genome microarray assay screened the differentially expressed gene HO-1, which is downstream of the PI3K/Akt signaling pathway, and the alteration of HO-1 mRNA and protein expression was consistent with the data on PI3K/Akt pathway.

Conclusions

Our results suggest the existence of the PI3K/Akt/HO-1 signaling pathway in RAW264.7 cells under OGD/R circumstances in vitro, revealing the mechanism underlying BM-MSC-mediated regulation of autophagy and enriching the understanding of potential therapeutic targets for the treatment of ALI.

Role of Gag mutations in PI resistance in the Swiss HIV cohort study: bystanders or contributors?

Background

HIV Gag mutations have been reported to confer PI drug resistance. However, clinical implications are still controversial and most current genotyping algorithms consider solely the protease gene for assessing PI resistance.

Objectives

Our goal was to describe for HIV infections in Switzerland the potential role of the C-terminus of Gag (NC-p6) in PI resistance. We aimed to characterize resistance-relevant mutational patterns in Gag and protease and their possible interactions.

Methods

Resistance information on plasma samples from 2004-12 was collected for patients treated by two diagnostic centres of the Swiss HIV Cohort Study. Sequence information on protease and the C-terminal Gag region was paired with the corresponding patient treatment history. The prevalence of Gag and protease mutations was analysed for PI treatment-experienced patients versus PI treatment-naive patients. In addition, we modelled multiple paths of an assumed ordered accumulation of genetic changes using random tree mixture models.

Results

More than half of all PI treatment-experienced patients in our sample set carried HIV variants with at least one of the known Gag mutations, and 17.9% (66/369) carried at least one Gag mutation for which a phenotypic proof of PI resistance by in vitro mutagenesis has been reported. We were able to identify several novel Gag mutations that are associated with PI exposure and therapy failure.

Conclusions

Our analysis confirmed the association of Gag mutations, well known and new, with PI exposure. This could have clinical implications, since the level of potential PI drug resistance might be underestimated.

Variable selection for disease progression models: methods for oncogenetic trees and application to cancer and HIV

Background

Disease progression models are important for understanding the critical steps during the development of diseases. The models are imbedded in a statistical framework to deal with random variations due to biology and the sampling process when observing only a finite population. Conditional probabilities are used to describe dependencies between events that characterise the critical steps in the disease process.

Many different model classes have been proposed in the literature, from simple path models to complex Bayesian networks. A popular and easy to understand but yet flexible model class are oncogenetic trees. These have been applied to describe the accumulation of genetic aberrations in cancer and HIV data. However, the number of potentially relevant aberrations is often by far larger than the maximal number of events that can be used for reliably estimating the progression models. Still, there are only a few approaches to variable selection, which have not yet been investigated in detail.

Results

We fill this gap and propose specifically for oncogenetic trees ten variable selection methods, some of these being completely new. We compare them in an extensive simulation study and on real data from cancer and HIV. It turns out that the preselection of events by clique identification algorithms performs best. Here, events are selected if they belong to the largest or the maximum weight subgraph in which all pairs of vertices are connected.

Conclusions

The variable selection method of identifying cliques finds both the important frequent events and those related to disease pathways.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1762-1) contains supplementary material, which is available to authorized users.

The evolution of tumour phylogenetics: principles and practice

Rapid advances in high-throughput sequencing and a growing realization of the importance of evolutionary theory to cancer genomics have led to a proliferation of phylogenetic studies of tumour progression. These studies have yielded not only new insights but also a plethora of experimental approaches, sometimes reaching conflicting or poorly supported conclusions. Here, we consider this body of work in light of the key computational principles underpinning phylogenetic inference, with the goal of providing practical guidance on the design and analysis of scientifically rigorous tumour phylogeny studies. We survey the range of methods and tools available to the researcher, their key applications, and the various unsolved problems, closing with a perspective on the prospects and broader implications of this field.

Identifying restrictions in the order of accumulation of mutations during tumor progression: effects of passengers, evolutionary models, and sampling

BACKGROUND

Cancer progression is caused by the sequential accumulation of mutations, but not all orders of accumulation 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. The purpose of this study is to conduct a comprehensive comparison of the performance of all available methods to identify these restrictions from cross-sectional data. I used simulated data sets (where the true restrictions are known) but, 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 assess, for the first time, the effects of having to filter out passengers, of sampling schemes (when, how, and how many samples), and of deviations from order restrictions.

RESULTS

Poor choices of method, filtering, and sampling lead to large errors in all performance measures. 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.

CONCLUSIONS

This paper provides practical recommendations for using these methods with experimental data. It also identifies key areas of future methodological work and, in particular, 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.

Algorithms to model single gene, single chromosome, and whole genome copy number changes jointly in tumor phylogenetics

We present methods to construct phylogenetic models of tumor progression at the cellular level that include copy number changes at the scale of single genes, entire chromosomes, and the whole genome. The methods are designed for data collected by fluorescence in situ hybridization (FISH), an experimental technique especially well suited to characterizing intratumor heterogeneity using counts of probes to genetic regions frequently gained or lost in tumor development. Here, we develop new provably optimal methods for computing an edit distance between the copy number states of two cells given evolution by copy number changes of single probes, all probes on a chromosome, or all probes in the genome. We then apply this theory to develop a practical heuristic algorithm, implemented in publicly available software, for inferring tumor phylogenies on data from potentially hundreds of single cells by this evolutionary model. We demonstrate and validate the methods on simulated data and published FISH data from cervical cancers and breast cancers. Our computational experiments show that the new model and algorithm lead to more parsimonious trees than prior methods for single-tumor phylogenetics and to improved performance on various classification tasks, such as distinguishing primary tumors from metastases obtained from the same patient population.

Cancer is an evolutionary system whose growth and development is attributed to aberrations in well-known genes and to cancer-type specific genomic imbalances. Here, we present methods for reconstructing the evolution of individual tumors based on cell-to-cell variations between copy numbers of targeted regions of the genome. The methods are designed to work with fluorescence in situ hybridization (FISH), a technique that allows one to profile copy number changes in potentially thousands of single cells per study. Our work advances the prior art by developing theory and practical algorithms for building evolutionary trees of single tumors that can model gain or loss of genetic regions at the scale of single genes, whole chromosomes, or the entire genome, all common events in tumor evolution. We apply these methods on simulated and real tumor data to demonstrate substantial improvements in tree-building accuracy and in our ability to accurately classify tumors from their inferred evolutionary models. The newly developed algorithms have been released through our publicly available software, FISHtrees.

Cumulative disease progression models for cross-sectional data: a review and comparison

A better understanding of disease progression is beneficial for early diagnosis and appropriate individual therapy. Many different approaches for statistical modelling of cumulative disease progression have been proposed in the literature, including simple path models up to complex restricted Bayesian networks. Important fields of application are diseases such as cancer and HIV. Tumour progression is measured by means of chromosome aberrations, whereas people infected with HIV develop drug resistances because of genetic changes of the HI-virus. These two very different diseases have typical courses of disease progression, which can be modelled partly by consecutive and partly by independent steps. This paper gives an overview of the different progression models and points out their advantages and drawbacks. Different models are compared via simulations to analyse how they work if some of their assumptions are violated. In a simulation study, we evaluate how models perform in terms of fitting induced multivariate probability distributions and topological relationships. We often find that the true model class used for generating data is outperformed by either a less or a more complex model class. The more flexible conjunctive Bayesian networks can be used to fit oncogenetic trees, whereas mixtures of oncogenetic trees with three tree components can be well fitted by mixture models with only two tree components.