The temporal order of genetic and pathway alterations in tumorigenesis

Synergistic epistasis among cancer drivers can rescue early tumors from the accumulation of deleterious passengers

Epistasis among driver mutations is pervasive and explains relevant features of cancer, such as differential therapy response and convergence towards well-characterized molecular subtypes. Furthermore, a growing body of evidence suggests that tumor development could be hampered by the accumulation of slightly deleterious passenger mutations. In this work, we combined empirical epistasis networks, computer simulations, and mathematical models to explore how synergistic interactions among driver mutations affect cancer progression under the burden of slightly deleterious passengers. We found that epistasis plays a crucial role in tumor development by promoting the transformation of precancerous clones into rapidly growing tumors through a process that is analogous to evolutionary rescue. The triggering of epistasis-driven rescue is strongly dependent on the intensity of epistasis and could be a key rate-limiting step in many tumors, contributing to their unpredictability. As a result, central genes in cancer epistasis networks appear as key intervention targets for cancer therapy.

Tumor-derived cell-free DNA and circulating tumor cells: partners or rivals in metastasis formation?

The origin of metastases is a topic that has sparked controversy. Despite recent advancements, metastatic disease continues to pose challenges. The first admitted model of how metastases develop revolves around cells breaking away from the primary tumor, known as circulating tumor cells (CTCs). These cells survive while circulating through the bloodstream and subsequently establish themselves in secondary organs, a process often referred to as the "metastatic cascade". This intricate and dynamic process involves various steps, but all the mechanisms behind metastatic dissemination are not yet comprehensively elucidated. The "seed and soil" theory has shed light on the phenomenon of metastatic organotropism and the existence of pre-metastatic niches. It is now established that these niches can be primed by factors secreted by the primary tumor before the arrival of CTCs. In particular, exosomes have been identified as important contributors to this priming. Another concept then emerged, i.e. the "genometastasis" theory, which challenged all other postulates. It emphasizes the intriguing but promising role of cell-free DNA (cfDNA) in metastasis formation through oncogenic formation of recipient cells. However, it cannot be ruled out that all these theories are intertwined. This review outlines the primary theories regarding the metastases formation that involve CTCs, and depicts cfDNA, a potential second player in the metastasis formation. We discuss the potential interrelationships between CTCs and cfDNA, and propose both in vitro and in vivo experimental strategies to explore all plausible theories.

A phylogenetic approach to inferring the order in which mutations arise during cancer progression

Although the role of evolutionary process in cancer progression is widely accepted, increasing attention is being given to the evolutionary mechanisms that can lead to differences in clinical outcome. Recent studies suggest that the temporal order in which somatic mutations accumulate during cancer progression is important. Single-cell sequencing (SCS) provides a unique opportunity to examine the effect that the mutation order has on cancer progression and treatment effect. However, the error rates associated with single-cell sequencing are known to be high, which greatly complicates the task. We propose a novel method for inferring the order in which somatic mutations arise within an individual tumor using noisy data from single-cell sequencing. Our method incorporates models at two levels in that the evolutionary process of somatic mutation within the tumor is modeled along with the technical errors that arise from the single-cell sequencing data collection process. Through analyses of simulations across a wide range of realistic scenarios, we show that our method substantially outperforms existing approaches for identifying mutation order. Most importantly, our method provides a unique means to capture and quantify the uncertainty in the inferred mutation order along a given phylogeny. We illustrate our method by analyzing data from colorectal and prostate cancer patients, in which our method strengthens previously reported mutation orders. Our work is an important step towards producing meaningful prediction of mutation order with high accuracy and measuring the uncertainty of predicted mutation order in cancer patients, with the potential to lead to new insights about the evolutionary trajectories of cancer.

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

SUMMARY

EvAM-Tools is an R package and web application that provides a unified interface to state-of-the-art cancer progression models 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 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 IMPLEMENTATION

Implemented 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.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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 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.

From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics

Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is arguably the next major missing piece in a fully predictive theory of evolution. We refer to this generally as the problem of the genotype-phenotype map. Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures, has revealed important facts that deeply affect the dynamical description of evolutionary processes. Empirical evidence supporting the fundamental relevance of features such as phenotypic bias is mounting as well, while the synthesis of conceptual and experimental progress leads to questioning current assumptions on the nature of evolutionary dynamics-cancer progression models or synthetic biology approaches being notable examples. This work delves with a critical and constructive attitude into our current knowledge of how genotypes map onto molecular phenotypes and organismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension. As a final goal, this community should aim at deriving an updated picture of evolutionary processes soundly relying on the structural properties of genotype spaces, as revealed by modern techniques of molecular and functional analysis.

Use of signals of positive and negative selection to distinguish cancer genes and passenger genes

A major goal of cancer genomics is to identify all genes that play critical roles in carcinogenesis. Most approaches focused on genes positively selected for mutations that drive carcinogenesis and neglected the role of negative selection. Some studies have actually concluded that negative selection has no role in cancer evolution. We have re-examined the role of negative selection in tumor evolution through the analysis of the patterns of somatic mutations affecting the coding sequences of human genes. Our analyses have confirmed that tumor suppressor genes are positively selected for inactivating mutations, oncogenes, however, were found to display signals of both negative selection for inactivating mutations and positive selection for activating mutations. Significantly, we have identified numerous human genes that show signs of strong negative selection during tumor evolution, suggesting that their functional integrity is essential for the growth and survival of tumor cells.

Deep phylogeny of cancer drivers and compensatory mutations

Driver mutations (DM) are the genetic impetus for most cancers. The DM are assumed to be deleterious in species evolution, being eliminated by purifying selection unless compensated by other mutations. We present deep phylogenies for 84 cancer driver genes and investigate the prevalence of 434 DM across gene-species trees. The DM are rare in species evolution, and 181 are completely absent, validating their negative fitness effect. The DM are more common in unicellular than in multicellular eukaryotes, suggesting a link between these mutations and cell proliferation control. 18 DM appear as the ancestral state in one or more major clades, including 3 among mammals. We identify within-gene, compensatory mutations for 98 DM and infer likely interactions between the DM and compensatory sites in protein structures. These findings elucidate the evolutionary status of DM and are expected to advance the understanding of the functions and evolution of oncogenes and tumor suppressors.

Rochman et al. present deep phylogenies for 84 cancer driver genes and examine the prevalence of driver mutations across gene-species trees. Their results show that driver mutations are rare in species evolution and give insight into the evolution of driver mutations and oncogenes.

Modelling cancer progression using Mutual Hazard Networks

MOTIVATION

Cancer progresses by accumulating genomic events, such as mutations and copy number alterations, whose chronological order is key to understanding the disease but difficult to observe. Instead, cancer progression models use co-occurrence patterns in cross-sectional data to infer epistatic interactions between events and thereby uncover their most likely order of occurrence. State-of-the-art progression models, however, are limited by mathematical tractability and only allow events to interact in directed acyclic graphs, to promote but not inhibit subsequent events, or to be mutually exclusive in distinct groups that cannot overlap.

RESULTS

Here we propose Mutual Hazard Networks (MHN), a new Machine Learning algorithm to infer cyclic progression models from cross-sectional data. MHN model events by their spontaneous rate of fixation and by multiplicative effects they exert on the rates of successive events. MHN compared favourably to acyclic models in cross-validated model fit on four datasets tested. In application to the glioblastoma dataset from The Cancer Genome Atlas, MHN proposed a novel interaction in line with consecutive biopsies: IDH1 mutations are early events that promote subsequent fixation of TP53 mutations.

AVAILABILITY AND IMPLEMENTATION

Implementation and data are available at https://github.com/RudiSchill/MHN.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Mathematical model of colorectal cancer initiation

Quantifying evolutionary dynamics of cancer initiation and progression can provide insights into more effective strategies of early detection and treatment. Here we develop a mathematical model of colorectal cancer initiation through inactivation of two tumor suppressor genes and activation of one oncogene, accounting for the well-known path to colorectal cancer through loss of tumor suppressors APC and TP53 and gain of the KRAS oncogene. In the model, we allow mutations to occur in any order, leading to a complex network of premalignant mutational genotypes on the way to colorectal cancer. We parameterize the model using experimentally measured parameter values, many of them only recently available, and compare its predictions to epidemiological data on colorectal cancer incidence. We find that the reported lifetime risk of colorectal cancer can be recovered using a mathematical model of colorectal cancer initiation together with experimentally measured mutation rates in colorectal tissues and proliferation rates of premalignant lesions. We demonstrate that the order of driver events in colorectal cancer is determined primarily by the fitness effects that they provide, rather than their mutation rates. Our results imply that there may not be significant immune suppression of untreated benign and malignant colorectal lesions.

Clonal evolution driven by superdriver mutations

Background

Tumors are widely recognized to progress through clonal evolution by sequentially acquiring selectively advantageous genetic alterations that significantly contribute to tumorigenesis and thus are termned drivers. Some cancer drivers, such as TP53 point mutation or EGFR copy number gain, provide exceptional fitness gains, which, in time, can be sufficient to trigger the onset of cancer with little or no contribution from additional genetic alterations. These key alterations are called superdrivers.

Results

In this study, we employ a Wright-Fisher model to study the interplay between drivers and superdrivers in tumor progression. We demonstrate that the resulting evolutionary dynamics follow global clonal expansions of superdrivers with periodic clonal expansions of drivers. We find that the waiting time to the accumulation of a set of superdrivers and drivers in the tumor cell population can be approximated by the sum of the individual waiting times.

Conclusions

Our results suggest that superdriver dynamics dominate over driver dynamics in tumorigenesis. Furthermore, our model allows studying the interplay between superdriver and driver mutations both empirically and theoretically.

Effects of ordered mutations on dynamics in signaling networks

BACKGROUND

Many previous clinical studies have found that accumulated sequential mutations are statistically related to tumorigenesis. However, they are limited in fully elucidating the significance of the ordered-mutation because they did not focus on the network dynamics. Therefore, there is a pressing need to investigate the dynamics characteristics induced by ordered-mutations.

METHODS

To quantify the ordered-mutation-inducing dynamics, we defined the mutation-sensitivity and the order-specificity that represent if the network is sensitive against a double knockout mutation and if mutation-sensitivity is specific to the mutation order, respectively, using a Boolean network model.

RESULTS

Through intensive investigations, we found that a signaling network is more sensitive when a double-mutation occurs in the direction order inducing a longer path and a smaller number of paths than in the reverse order. In addition, feedback loops involving a gene pair decreased both the mutation-sensitivity and the order-specificity. Next, we investigated relationships of functionally important genes with ordered-mutation-inducing dynamics. The network is more sensitive to mutations subject to drug-targets, whereas it is less specific to the mutation order. Both the sensitivity and specificity are increased when different-drug-targeted genes are mutated. Further, we found that tumor suppressors can efficiently suppress the amplification of oncogenes when the former are mutated earlier than the latter.

CONCLUSION

Taken together, our results help to understand the importance of the order of mutations with respect to the dynamical effects in complex biological systems.

HyperTraPS: Inferring Probabilistic Patterns of Trait Acquisition in Evolutionary and Disease Progression Pathways

The explosion of data throughout the biomedical sciences provides unprecedented opportunities to learn about the dynamics of evolution and disease progression, but harnessing these large and diverse datasets remains challenging. Here, we describe a highly generalizable statistical platform to infer the dynamic pathways by which many, potentially interacting, traits are acquired or lost over time. We use HyperTraPS (hypercubic transition path sampling) to efficiently learn progression pathways from cross-sectional, longitudinal, or phylogenetically linked data, readily distinguishing multiple competing pathways, and identifying the most parsimonious mechanisms underlying given observations. This Bayesian approach allows inclusion of prior knowledge, quantifies uncertainty in pathway structure, and allows predictions, such as which symptom a patient will acquire next. We provide visualization tools for intuitive assessment of multiple, variable pathways. We apply the method to ovarian cancer progression and the evolution of multidrug resistance in tuberculosis, demonstrating its power to reveal previously undetected dynamic pathways.

The landscape of the mesenchymal signature in brain tumours

The complexity of glioblastoma multiforme, the most common and lethal variant of gliomas, is reflected by cellular and molecular heterogeneity at both the inter- and intra-tumoural levels. Molecular subtyping has arisen in the past two decades as a promising strategy to give better predictions of glioblastoma multiforme evolution, common disease pathways, and rational treatment options. The Cancer Genome Atlas network initially identified four molecular subtypes of glioblastoma multiforme: proneural, neural, mesenchymal and classical. However, further studies, also investigated glioma stem cells, have only identified two to three subtypes: proneural, mesenchymal and classical. The proneural-mesenchymal transition upon tumour recurrence has been suggested as a mechanism of tumour resistance to radiation and chemotherapy treatment. Glioblastoma multiforme patients with the mesenchymal subtype tend to survive shorter than other subtypes when analysis is restricted to samples with low transcriptional heterogeneity. Although the mesenchymal signature in malignant glioma may seem at odds with the common idea of the ectodermal origin of neural-glial lineages, the presence of the mesenchymal signature in glioma is supported by several studies suggesting that it can result from: (i) intrinsic expression of tumour cells affected with accumulated genetic mutations and cell of origin; (ii) tumour micro-environments with recruited macrophages or microglia, mesenchymal stem cells or pericytes, and other progenitors; (iii) resistance to tumour treatment, including radiotherapy, antiangiogenic therapy and possibly chemotherapy. Genetic abnormalities, mainly NF1 mutations, together with NF-κB transcriptional programs, are the main driver of acquiring mesenchymal-signature. This signature is far from being simply tissue artefacts, as it has been identified in single cell glioma, circulating tumour cells, and glioma stem cells that are released from the tumour micro-environment. All these together suggest that the mesenchymal signature in glioblastoma multiforme is induced and sustained via cell intrinsic mechanisms and tumour micro-environment factors. Although patients with the mesenchymal subtype tend to have poorer prognosis, they may have favourable response to immunotherapy and intensive radio- and chemotherapy.

A pan-cancer analysis of progression mechanisms and drug sensitivity in cancer cell lines

Biomarker discovery involves identifying genetic abnormalities within a tumor. However, one of the main challenges in defining such therapeutic targets is accounting for the molecular heterogeneity of cancer. By integrating somatic mutation and gene expression data from hundreds of heterogeneous cell lines from the Cancer Cell Line Encyclopedia (CCLE), we identify sequences of genetic events that may help explain common patterns of oncogenesis across 22 tumor types, and evaluate the general effect of late-stage mutations on drug sensitivity and resistance mechanisms. Through gene enrichment analysis, we find several cancer-specific and immune pathways that are significantly enriched in each of our three proposed phases of cancer progression. By further analyzing the drug activity area associated with compounds that target the BRAF oncogene, a known predictor of drug sensitivity for several compounds used in cancer treatment, we verify that the acquisition of new driver mutations interferes with the targeted drug mechanism, meaning that cells without late-stage mutations generally respond better to drugs.

A probabilistic method for leveraging functional annotations to enhance estimation of the temporal order of pathway mutations during carcinogenesis

BACKGROUND

Cancer arises through accumulation of somatically acquired genetic mutations. An important question is to delineate the temporal order of somatic mutations during carcinogenesis, which contributes to better understanding of cancer biology and facilitates identification of new therapeutic targets. Although a number of statistical and computational methods have been proposed to estimate the temporal order of mutations, they do not account for the differences in the functional impacts of mutations and thus are likely to be obscured by the presence of passenger mutations that do not contribute to cancer progression. In addition, many methods infer the order of mutations at the gene level, which have limited power due to the low mutation rate in most genes.

RESULTS

In this paper, we develop a Probabilistic Approach for estimating the Temporal Order of Pathway mutations by leveraging functional Annotations of mutations (PATOPA). PATOPA infers the order of mutations at the pathway level, wherein it uses a probabilistic method to characterize the likelihood of mutational events from different pathways occurring in a certain order. The functional impact of each mutation is incorporated to weigh more on a mutation that is more integral to tumor development. A maximum likelihood method is used to estimate parameters and infer the probability of one pathway being mutated prior to another. Simulation studies and analysis of whole exome sequencing data from The Cancer Genome Atlas (TCGA) demonstrate that PATOPA is able to accurately estimate the temporal order of pathway mutations and provides new biological insights on carcinogenesis of colorectal and lung cancers.

CONCLUSIONS

PATOPA provides a useful tool to estimate temporal order of mutations at the pathway level while leveraging functional annotations of mutations.

Epigenetic profiling of MUTYH, KLF6, WNT1 and KLF4 genes in carcinogenesis and tumorigenesis of colorectal cancer

Colorectal cancer (CRC) is distinguished by epigenetic elements like DNA methylation, histone modification, histone acetylation and RNA remodeling which is related with genomic instability and tumor initiation. Correspondingly, as a main epigenetic regulation, DNA methylation has an impressive ability in order to be used in CRC targeted therapy. Meaningly, DNA methylation is identified as one of most important epigenetic regulators in gene expression and is considered as a notable potential driver in tumorigenesis and carcinogenesis through gene-silencing of tumor suppressors genes. Abnormal methylation situation, even in the level of promoter regions, does not essentially change the gene expression levels, particularly if the gene was become silenced, leaving the mechanisms of methylation without any response. According to the methylation situation which has a strong eagerness to be highly altered on CpG islands in carcinogenesis and tumorigenesis, considering its epigenetic fluctuations in finding new biomarkers is of great importance. Modifications in DNA methylation pattern and also enrichment of methylated histone signs in the promoter regions of some certain genes like MUTYH, KLF4/6 and WNT1 in different signaling pathways could be a notable key contributors to the upregulation of tumor initiation in CRC. These epigenetic alterations could be employed as a practical diagnostic biomarkers for colorectal cancer. In this review, we will be discuss these fluctuations of MUTYH, KLF4/6 and WNT1 genes in CRC.

Uncovering the subtype-specific temporal order of cancer pathway dysregulation

Cancer is driven by genetic mutations that dysregulate pathways important for proper cell function. Therefore, discovering these cancer pathways and their dysregulation order is key to understanding and treating cancer. However, the heterogeneity of mutations between different individuals makes this challenging and requires that cancer progression is studied in a subtype-specific way. To address this challenge, we provide a mathematical model, called Subtype-specific Pathway Linear Progression Model (SPM), that simultaneously captures cancer subtypes and pathways and order of dysregulation of the pathways within each subtype. Experiments with synthetic data indicate the robustness of SPM to problem specifics including noise compared to an existing method. Moreover, experimental results on glioblastoma multiforme and colorectal adenocarcinoma show the consistency of SPM's results with the existing knowledge and its superiority to an existing method in certain cases. The implementation of our method is available at https://github.com/Dalton386/SPM.

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 unpredictability 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.

Estimating the predictability of cancer evolution

MOTIVATION

How predictable is the evolution of cancer? This fundamental question is of immense relevance for the diagnosis, prognosis and treatment of cancer. Evolutionary biologists have approached the question of predictability based on the underlying fitness landscape. However, empirical fitness landscapes of tumor cells are impossible to determine in vivo. Thus, in order to quantify the predictability of cancer evolution, alternative approaches are required that circumvent the need for fitness landscapes.

RESULTS

We developed a computational method based on conjunctive Bayesian networks (CBNs) to quantify the predictability of cancer evolution directly from mutational data, without the need for measuring or estimating fitness. Using simulated data derived from >200 different fitness landscapes, we show that our CBN-based notion of evolutionary predictability strongly correlates with the classical notion of predictability based on fitness landscapes under the strong selection weak mutation assumption. The statistical framework enables robust and scalable quantification of evolutionary predictability. We applied our approach to driver mutation data from the TCGA and the MSK-IMPACT clinical cohorts to systematically compare the predictability of 15 different cancer types. We found that cancer evolution is remarkably predictable as only a small fraction of evolutionary trajectories are feasible during cancer progression.

AVAILABILITY AND IMPLEMENTATION

https://github.com/cbg-ethz/predictability\_of\_cancer\_evolution.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An Integrated Framework for Identifying Mutated Driver Pathway and Cancer Progression

Next-generation sequencing (NGS) technologies provide amount of somatic mutation data in a large number of patients. The identification of mutated driver pathway and cancer progression from these data is a challenging task because of the heterogeneity of interpatient. In addition, cancer progression at the pathway level has been proved to be more reasonable than at the gene level. In this paper, we introduce an integrated framework to identify mutated driver pathways and cancer progression (iMDPCP) at the pathway level from somatic mutation data. First, we use uncertainty coefficient to quantify mutual exclusivity on gene driver pathways and develop a computational framework to identify mutated driver pathways based on the adaptive discrete differential evolution algorithm. Then, we construct cancer progression model for driver pathways based on the Bayesian Network. Finally, we evaluate the performance of iMDPCP on real cancer somatic mutation datasets. The experimental results indicate that iMDPCP is more accurate than state-of-the-art methods according to the enrichment of KEGG pathways, and it also provides new insights on identifying cancer progression at the pathway level.

In vitro cytotoxicity and anticancer effects of citral nanostructured lipid carrier on MDA MBA-231 human breast cancer cells

Very recently, we postulated that the incorporation of citral into nanostructured lipid carrier (NLC-Citral) improves solubility and delivery of the citral without toxic effects in vivo. Thus, the objective of this study is to evaluate anti-cancer effects of NLC-Citral in MDA MB-231 cells in vitro through the Annexin V, cell cycle, JC-1 and fluorometric assays. Additionally, this study is aimed to effects of NLC-Citral in reducing the tumor weight and size in 4T1 induced murine breast cancer model. Results showed that NLC-Citral induced apoptosis and G2/M arrest in MDA MB-231 cells. Furthermore, a prominent anti-metastatic ability of NLC-Citral was demonstrated in vitro using scratch, migration and invasion assays. A significant reduction of migrated and invaded cells was observed in the NLC-Citral treated MDA MB-231 cells. To further evaluate the apoptotic and anti-metastatic mechanism of NLC-Citral at the molecular level, microarray-based gene expression and proteomic profiling were conducted. Based on the result obtained, NLC-Citral was found to regulate several important signaling pathways related to cancer development such as apoptosis, cell cycle, and metastasis signaling pathways. Additionally, gene expression analysis was validated through the targeted RNA sequencing and real-time polymerase chain reaction. In conclusion, the NLC-Citral inhibited the proliferation of breast cancer cells in vitro, majorly through the induction of apoptosis, anti-metastasis, anti-angiogenesis potentials, and reducing the tumor weight and size without altering the therapeutic effects of citral.

Mutational interactions define novel cancer subgroups

Large-scale genomic data highlight the complexity and diversity of the molecular changes that drive cancer progression. Statistical analysis of cancer data from different tissues can guide drug repositioning as well as the design of targeted treatments. Here, we develop an improved Bayesian network model for tumour mutational profiles and apply it to 8198 patient samples across 22 cancer types from TCGA. For each cancer type, we identify the interactions between mutated genes, capturing signatures beyond mere mutational frequencies. When comparing mutation networks, we find genes which interact both within and across cancer types. To detach cancer classification from the tissue type we perform de novo clustering of the pancancer mutational profiles based on the Bayesian network models. We find 22 novel clusters which significantly improve survival prediction beyond clinical information. The models highlight key gene interactions for each cluster potentially allowing genomic stratification for clinical trials and identifying drug targets.

Tumour heterogeneity hinders translation of large-scale genomic data into the clinic. Here the authors develop a method for the stratification of cancer patients based on the molecular gene status, including genetic interactions, rather than clinico-histological data, and apply it to TCGA data for over 8000 cases across 22 cancer types.

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

Motivation

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 identify these constraints, and return Directed Acyclic Graphs (DAGs) of restrictions where arrows indicate dependencies or constraints. 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.

Results

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. Using three cancer datasets, I show that these problems strongly affect the analysis of empirical data: fitness landscapes that are widely different from each other produce data similar to the empirically observed ones and lead to DAGs that infer very different restrictions. Because reciprocal sign epistasis can be common in cancer, these results question the use and interpretation of CPMs.

Availability and implementation

Code available from Supplementary Material.

Contact

ramon.diaz@iib.uam.es.

Supplementary information

Supplementary data are available at Bioinformatics online.

Timing somatic events in the evolution of cancer

Cancer arises through the accumulation of somatic mutations over time. An understanding of the sequence of events during this process should allow both earlier diagnosis and better prediction of cancer progression. However, the pathways of tumor evolution have not yet been comprehensively characterized. With the advent of whole genome sequencing, it is now possible to infer the evolutionary history of single tumors from the snapshot of their genome taken at diagnosis, giving new insights into the biology of tumorigenesis.

Filling the gap between chemical carcinogenesis and the hallmarks of cancer: A temporal perspective

BACKGROUND

Cancer is believed to arise through the perturbation of pathways and the order of pathway perturbation events can enhance understanding and evaluation of carcinogenicity. This order has not been examined so far, and this study aimed to fill this gap by attempting to gather evidence on the potential temporal sequence of events in carcinogenesis.

DESIGN

The methodology followed was to discuss first the temporal sequence of hallmarks of cancer from the point of view of pathological specimens of cancer (essentially branched mutations) and then to consider the hallmarks of cancer that one well-known carcinogen, benzo(a)pyrene, can modify.

RESULTS

Even though the sequential order of driving genetic alterations can vary between and within tumours, the main cancer pathways affected are almost ubiquitous and follow a generally common sequence: resisting cell death, insensitivity to antigrowth signals, sustained proliferation, deregulated energetics, replicative immortality and activation of invasion and metastasis. The first 3 hallmarks can be regarded as almost simultaneous while angiogenesis and avoiding immune destruction are perhaps the only hallmarks with a varying position in the above sequence.

CONCLUSIONS

Our review of hallmarks of cancer and their temporal sequence, based on mutational spectra in biopsies from different cancer sites, allowed us to propose a hypothetical temporal sequence of the hallmarks. This sequence can add molecular support to the evaluation of an agent as a carcinogen as it can be used as a conceptual framework for organising and evaluating the strength of existing evidence.

Comprehensive molecular characterization of multifocal glioblastoma proves its monoclonal origin and reveals novel insights into clonal evolution and heterogeneity of glioblastomas

Background

The evolution of primary glioblastoma (GBM) is poorly understood. Multifocal GBM (ie, multiple synchronous lesions in one patient) could elucidate GBM development.

Methods

We present the first comprehensive study of 12 GBM foci from 6 patients using array-CGH, spectral karyotyping, gene expression arrays, and next-generation sequencing.

Results

Multifocal GBMs genetically resemble primary GBMs. Comparing foci from the same patient proved their monoclonal origin. All tumors harbored alterations in the 3 GBM core pathways: RTK/PI3K, p53, and RB regulatory pathways with aberrations of EGFR and CDKN2A/B in all (100%) patients. This unexpected high frequency reflects a distinct genetic signature of multifocal GBMs and might account for their highly malignant and invasive phenotype. Surprisingly, the types of mutations in these genes/pathways were different in tumor foci from the same patients. For example, we found distinct mutations/aberrations in PTEN, TP53, EGFR, and CDKN2A/B, which therefore must have occurred independently and late during tumor development. We also identified chromothripsis as a late event and in tumors with wild-type TP53. Only 2 events were found to be early in all patients: single copy loss of PTEN and TERT promoter point mutations.

Conclusions

Multifocal GBMs develop through parallel genetic evolution. The high frequency of alterations in 3 main pathways suggests that these are essential steps in GBM evolution; however, their late occurrence indicates that they are not founder events but rather subclonal drivers. This might account for the marked genetic heterogeneity seen in primary GBM and therefore has important implications for GBM therapy.

Multiscale Modeling of Inflammation-Induced Tumorigenesis Reveals Competing Oncogenic and Oncoprotective Roles for Inflammation

Chronic inflammation is a serious risk factor for cancer; however, the routes from inflammation to cancer are poorly understood. On the basis of the processes implicated by frequently mutated genes associated with inflammation and cancer in three organs (stomach, colon, and liver) extracted from the Gene Expression Omnibus, The Cancer Genome Atlas, and Gene Ontology databases, we present a multiscale model of the long-term evolutionary dynamics leading from inflammation to tumorigenesis. The model incorporates cross-talk among interactions on several scales, including responses to DNA damage, gene mutation, cell-cycle behavior, population dynamics, inflammation, and metabolism-immune balance. Model simulations revealed two stages of inflammation-induced tumorigenesis: a precancerous state and tumorigenesis. The precancerous state was mainly caused by mutations in the cell proliferation pathway; the transition from the precancerous to tumorigenic states was induced by mutations in pathways associated with apoptosis, differentiation, and metabolism-immune balance. We identified opposing effects of inflammation on tumorigenesis. Mild inflammation removed cells with DNA damage through DNA damage-induced cell death, whereas severe inflammation accelerated accumulation of mutations and hence promoted tumorigenesis. These results provide insight into the evolutionary dynamics of inflammation-induced tumorigenesis and highlight the combinatorial effects of inflammation and metabolism-immune balance. This approach establishes methods for quantifying cancer risk, for the discovery of driver pathways in inflammation-induced tumorigenesis, and has direct relevance for early detection and prevention and development of new treatment regimes. Cancer Res; 77(22); 6429-41. ©2017 AACR.

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.

Large-scale inference of conjunctive Bayesian networks

The continuous time conjunctive Bayesian network (CT-CBN) is a graphical model for analyzing the waiting time process of the accumulation of genetic changes (mutations). CT-CBN models have been successfully used in several biological applications such as HIV drug resistance development and genetic progression of cancer. However, current approaches for parameter estimation and network structure learning of CBNs can only deal with a small number of mutations (<20). Here, we address this limitation by presenting an efficient and accurate approximate inference algorithm using a Monte Carlo expectation-maximization algorithm based on importance sampling. The new method can now be used for a large number of mutations, up to one thousand, an increase by two orders of magnitude. In simulation studies, we present the accuracy as well as the running time efficiency of the new inference method and compare it with a MLE method, expectation-maximization, and discrete time CBN model, i.e. a first-order approximation of the CT-CBN model. We also study the application of the new model on HIV drug resistance datasets for the combination therapy with zidovudine plus lamivudine (AZT + 3TC) as well as under no treatment, both extracted from the Swiss HIV Cohort Study database.

AVAILABILITY AND IMPLEMENTATION

The proposed method is implemented as an R package available at https://github.com/cbg-ethz/MC-CBN CONTACT: niko.beerenwinkel@bsse.ethz.ch

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genetic load makes cancer cells more sensitive to common drugs: evidence from Cancer Cell Line Encyclopedia

Genetic alterations initiate tumors and enable the evolution of drug resistance. The pro-cancer view of mutations is however incomplete, and several studies show that mutational load can reduce tumor fitness. Given its negative effect, genetic load should make tumors more sensitive to anticancer drugs. Here, we test this hypothesis across all major types of cancer from the Cancer Cell Line Encyclopedia, which provides genetic and expression data of 496 cell lines together with their response to 24 common anticancer drugs. We found that the efficacy of 9 out of 24 drugs showed significant association with genetic load in a pan-cancer analysis. The associations for some tissue-drug combinations were remarkably strong, with genetic load explaining up to 83% of the variance in the drug response. Overall, the role of genetic load depended on both the drug and the tissue type with 10 tissues being particularly vulnerable to genetic load. We also identified changes in gene expression associated with increased genetic load, which included cell-cycle checkpoints, DNA damage and apoptosis. Our results show that genetic load is an important component of tumor fitness and can predict drug sensitivity. Beyond being a biomarker, genetic load might be a new, unexplored vulnerability of cancer.

Progression inference for somatic mutations in cancer

Computational methods were employed to determine progression inference of genomic alterations in commonly occurring cancers. Using cross-sectional TCGA data, we computed evolutionary trajectories involving selectivity relationships among pairs of gene-specific genomic alterations such as somatic mutations, deletions, amplifications, downregulation, and upregulation among the top 20 driver genes associated with each cancer. Results indicate that the majority of hierarchies involved TP53, PIK3CA, ERBB2, APC, KRAS, EGFR, IDH1, VHL, etc. Research into the order and accumulation of genomic alterations among cancer driver genes will ever-increase as the costs of nextgen sequencing subside, and personalized/precision medicine incorporates whole-genome scans into the diagnosis and treatment of cancer.

Characteristic patterns of cancer incidence: Epidemiological data, biological theories, and multistage models

We investigate and classify several patterns in cancer incidence and relative risk data which persist across different countries and multiple published studies. We then explore biological hypotheses as well as many mathematical models in the literature that attempt to explain these patterns. A general modeling framework is presented which is general enough to model most of observed behaviors. It is our belief that this model has sufficient flexibility to be adapted to new information as it is discovered. As one application of this framework, we give a model for the effect of aging on the process of carcinogenesis.

Computational approaches for the identification of cancer genes and pathways

High‐throughput DNA sequencing techniques enable large‐scale measurement of somatic mutations in tumors. Cancer genomics research aims at identifying all cancer‐related genes and solid interpretation of their contribution to cancer initiation and development. However, this venture is characterized by various challenges, such as the high number of neutral passenger mutations and the complexity of the biological networks affected by driver mutations. Based on biological pathway and network information, sophisticated computational methods have been developed to facilitate the detection of cancer driver mutations and pathways. They can be categorized into (1) methods using known pathways from public databases, (2) network‐based methods, and (3) methods learning cancer pathways de novo. Methods in the first two categories use and integrate different types of data, such as biological pathways, protein interaction networks, and gene expression measurements. The third category consists of de novo methods that detect combinatorial patterns of somatic mutations across tumor samples, such as mutual exclusivity and co‐occurrence. In this review, we discuss recent advances, current limitations, and future challenges of these approaches for detecting cancer genes and pathways. We also discuss the most important current resources of cancer‐related genes. WIREs Syst Biol Med 2017, 9:e1364. doi: 10.1002/wsbm.1364

For further resources related to this article, please visit the WIREs website.

Network-Based Method for Inferring Cancer Progression at the Pathway Level from Cross-Sectional Mutation Data

Large-scale cancer genomics projects are providing a wealth of somatic mutation data from a large number of cancer patients. However, it is difficult to obtain several samples with a temporal order from one patient in evaluating the cancer progression. Therefore, one of the most challenging problems arising from the data is to infer the temporal order of mutations across many patients. To solve the problem efficiently, we present a Network-based method (NetInf) to Infer cancer progression at the pathway level from cross-sectional data across many patients, leveraging on the exclusive property of driver mutations within a pathway and the property of linear progression between pathways. To assess the robustness of NetInf, we apply it on simulated data with the addition of different levels of noise. To verify the performance of NetInf, we apply it to analyze somatic mutation data from three real cancer studies with large number of samples. Experimental results reveal that the pathways detected by NetInf show significant enrichment. Our method reduces computational complexity by constructing gene networks without assigning the number of pathways, which also provides new insights on the temporal order of somatic mutations at the pathway level rather than at the gene level.

The Role of Epigenomics in the Study of Cancer Biomarkers and in the Development of Diagnostic Tools

Epigenetics plays a key role in cancer development. Genetics alone cannot explain sporadic cancer and cancer development in individuals with no family history or a weak family history of cancer. Epigenetics provides a mechanism to explain the development of cancer in such situations. Alterations in epigenetic profiling may provide important insights into the etiology and natural history of cancer. Because several epigenetic changes occur before histopathological changes, they can serve as biomarkers for cancer diagnosis and risk assessment. Many cancers may remain asymptomatic until relatively late stages; in managing the disease, efforts should be focused on early detection, accurate prediction of disease progression, and frequent monitoring. This chapter describes epigenetic biomarkers as they are expressed during cancer development and their potential use in cancer diagnosis and prognosis. Based on epigenomic information, biomarkers have been identified that may serve as diagnostic tools; some such biomarkers also may be useful in identifying individuals who will respond to therapy and survive longer. The importance of analytical and clinical validation of biomarkers is discussed, along with challenges and opportunities in this field.

Identification of neutral tumor evolution across cancer types

Despite extraordinary efforts to profile cancer genomes, interpreting the vast amount of genomic data in the light of cancer evolution remains challenging. Here we demonstrate that neutral tumor evolution results in a power-law distribution of the mutant allele frequencies reported by next-generation sequencing of tumor bulk samples. We find that the neutral power law fits with high precision 323 of 904 cancers from 14 types and from different cohorts. In malignancies identified as evolving neutrally, all clonal selection seemingly occurred before the onset of cancer growth and not in later-arising subclones, resulting in numerous passenger mutations that are responsible for intratumoral heterogeneity. Reanalyzing cancer sequencing data within the neutral framework allowed the measurement, in each patient, of both the in vivo mutation rate and the order and timing of mutations. This result provides a new way to interpret existing cancer genomic data and to discriminate between functional and non-functional intratumoral heterogeneity.

Integrating mutation and gene expression cross-sectional data to infer cancer progression

Background

A major problem in identifying the best therapeutic targets for cancer is the molecular heterogeneity of the disease. Cancer is often caused by an accumulation of mutations which produce irreversible damage to the cell’s control mechanisms of survival and proliferation. Different mutations may affect these cellular anachronisms through a combination of molecular interactions which may be dynamically changing during cancer progression. It has been previously shown that cancer accumulates mutations over time. In this paper we address the problem of cancer heterogeneity by modeling cancer progression using somatic mutation and gene expression cross-sectional data.

Results

We propose a novel formulation of integrating somatic mutation and gene expression data to infer the temporal sequence of events from cross-sectional data. Using a mixed integer linear program we model the interaction between groups of different mutated genes and the resulting modifications at the gene expression level. Our approach identifies a partition of mutation events which gradually produce gene expression changes to a partition of genes over time. The proposed formulation is tested using both simulated data and real breast cancer data with matched somatic mutations and gene expression measurements from The Cancer Genome Atlas. First, we classify the genes as oncogenes or tumor suppressors based on the frequency of driver mutations. As expected, the most frequently mutated genes in breast cancer are PIK3CA and TP53 genes. Then, we select those genes with most frequent driver mutations and a set of genes known to play roles in cancer development. Furthermore, we apply the proposed mixed integer linear program to identify the temporal order in which genes mutate and, simultaneously, the changes they produce at the gene expression level during cancer progression. In addition, we are able to identify known causal relationships between mutations and gene expression changes in PI3K/AKT and TP53 pathways.

Conclusions

This paper proposes a new model to infer the temporal sequence in which mutations occur and lead to changes at the gene expression level during cancer progression. The approach is general and can be applied to any data sets with available somatic mutations and gene expression measurements.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0255-6) contains supplementary material, which is available to authorized users.

The mathematics of cancer: integrating quantitative models

Mathematical modelling approaches have become increasingly abundant in cancer research. The complexity of cancer is well suited to quantitative approaches as it provides challenges and opportunities for new developments. In turn, mathematical modelling contributes to cancer research by helping to elucidate mechanisms and by providing quantitative predictions that can be validated. The recent expansion of quantitative models addresses many questions regarding tumour initiation, progression and metastases as well as intra-tumour heterogeneity, treatment responses and resistance. Mathematical models can complement experimental and clinical studies, but also challenge current paradigms, redefine our understanding of mechanisms driving tumorigenesis and shape future research in cancer biology.

Defining order and timing of mutations during cancer progression: the TO-DAG probabilistic graphical model

Somatic mutations arise and accumulate both during tumor genesis and progression. However, the order in which mutations occur is an open question and the inference of the temporal ordering at the gene level could potentially impact on patient treatment. Thus, exploiting recent observations suggesting that the occurrence of mutations is a non-memoryless process, we developed a computational approach to infer timed oncogenetic directed acyclic graphs (TO-DAGs) from human tumor mutation data. Such graphs represent the path and the waiting times of alterations during tumor evolution. The probability of occurrence of each alteration in a path is the probability that the alteration occurs when all alterations prior to it have occurred. The waiting time between an alteration and the subsequent is modeled as a stochastic function of the conditional probability of the event given the occurrence of the previous one. TO-DAG performances have been evaluated both on synthetic data and on somatic non-silent mutations from prostate cancer and melanoma patients and then compared with those of current well-established approaches. TO-DAG shows high performance scores on synthetic data and recognizes mutations in gatekeeper tumor suppressor genes as trigger for several downstream mutational events in the human tumor data.

Inferring Sequential Order of Somatic Mutations during Tumorgenesis based on Markov Chain Model

Tumors are developed and worsen with the accumulated mutations on DNA sequences during tumorigenesis. Identifying the temporal order of gene mutations in cancer initiation and development is a challenging topic. It not only provides a new insight into the study of tumorigenesis at the level of genome sequences but also is an effective tool for early diagnosis of tumors and preventive medicine. In this paper, we develop a novel method to accurately estimate the sequential order of gene mutations during tumorigenesis from genome sequencing data based on Markov chain model as TOMC (Temporal Order based on Markov Chain), and also provide a new criterion to further infer the order of samples or patients, which can characterize the severity or stage of the disease. We applied our method to the analysis of tumors based on several high-throughput datasets. Specifically, first, we revealed that tumor suppressor genes (TSG) tend to be mutated ahead of oncogenes, which are considered as important events for key functional loss and gain during tumorigenesis. Second, the comparisons of various methods demonstrated that our approach has clear advantages over the existing methods due to the consideration on the effect of mutation dependence among genes, such as co-mutation. Third and most important, our method is able to deduce the ordinal sequence of patients or samples to quantitatively characterize their severity of tumors. Therefore, our work provides a new way to quantitatively understand the development and progression of tumorigenesis based on high throughput sequencing data.

Phylogenetic analysis of multiple FISH markers in oral tongue squamous cell carcinoma suggests that a diverse distribution of copy number changes is associated with poor prognosis

Oral tongue squamous cell carcinoma (OTSCC) is associated with poor prognosis. To improve prognostication, we analyzed four gene probes (TERC, CCND1, EGFR and TP53) and the centromere probe CEP4 as a marker of chromosomal instability, using fluorescence in situ hybridization (FISH) in single cells from the tumors of sixty-five OTSCC patients (Stage I, n = 15; Stage II, n = 30; Stage III, n = 7; Stage IV, n = 13). Unsupervised hierarchical clustering of the FISH data distinguished three clusters related to smoking status. Copy number increases of all five markers were found to be correlated to non-smoking habits, while smokers in this cohort had low-level copy number gains. Using the phylogenetic modeling software FISHtrees, we constructed models of tumor progression for each patient based on the four gene probes. Then, we derived test statistics on the models that are significant predictors of disease-free and overall survival, independent of tumor stage and smoking status in multivariate analysis. The patients whose tumors were modeled as progressing by a more diverse distribution of copy number changes across the four genes have poorer prognosis. This is consistent with the view that multiple genetic pathways need to become deregulated in order for cancer to progress.

Dynamics of preventive vs post-diagnostic cancer control using low-impact measures

Cancer poses danger because of its unregulated growth, development of resistance, and metastatic spread to vital organs. We currently lack quantitative theory for how preventive measures and post-diagnostic interventions are predicted to affect risks of a life threatening cancer. Here we evaluate how continuous measures, such as life style changes and traditional treatments, affect both neoplastic growth and the frequency of resistant clones. We then compare and contrast preventive and post-diagnostic interventions assuming that only a single lesion progresses to invasive carcinoma during the life of an individual, and resection either leaves residual cells or metastases are undetected. Whereas prevention generally results in more positive therapeutic outcomes than post-diagnostic interventions, this advantage is substantially lowered should prevention initially fail to arrest tumour growth. We discuss these results and other important mitigating factors that should be taken into consideration in a comparative understanding of preventive and post-diagnostic interventions.

DOI:http://dx.doi.org/10.7554/eLife.06266.001

About one person in every two will get cancer during their lives. Surgery and chemotherapy have long been mainstays of cancer treatment. Both, however, have substantial downsides. Surgery may leave behind undetected cancer cells that can grow into new tumours. Furthermore, in response to chemotherapy drugs, some cancer cells may emerge that resist further treatment. There is therefore interest in whether preventive strategies—including lifestyle changes and medications—could reduce the likelihood of confronting a life-threatening cancer.

Now, Akhmetzhanov and Hochberg have developed a mathematical model to help compare the effectiveness of preventive strategies and traditional cancer treatments. The model—which assumes that a person can only develop a single cancer from a single region of pre-cancerous cells—suggests that long-term cancer prevention strategies reduce the risk of a life-threatening cancer by more than traditional treatment that begins after a tumour is discovered. The preventive measures may be less effective in some cases compared to traditional treatments if they initially fail to stop a tumour growing, although on average they still work better than treating the cancer after detection.

According to Akhmetzhanov and Hochberg's model, surgical removal followed by chemotherapy is less likely to be successful than prevention, and when successful, requires larger impacts on the cancer (and therefore creates more side-effects for the patient) to achieve the same level of control as prevention. The model also suggests that even at very low levels of impact on residual cancer cells, chemotherapies are likely to be counterproductive by boosting the subsequent emergence of treatment-resistant tumours.

Akhmetzhanov and Hochberg's model predicts how effective preventive measures need to be in terms of slowing the growth of cancer cells to result in given reductions in the future risk of a life-threatening cancer. Future work should test this model by measuring the effects on tumour growth of prevention and of traditional therapies.

DOI:http://dx.doi.org/10.7554/eLife.06266.002

Combined inhibition of MEK and mTOR has a synergic effect on angiosarcoma tumorgrafts

Angiosarcoma (AS) is a rare neoplasm of endothelial origin that has limited treatment options and poor five-year survival. Using tumorgraft models, we previously showed that AS is sensitive to small-molecule inhibitors that target mitogen-activated/extracellular-signal-regulated protein kinase kinases 1 and 2 (MEK). The objective of this study was to identify drugs that combine with MEK inhibitors to more effectively inhibit AS growth. We examined the in vitro synergy between the MEK inhibitor PD0325901 and inhibitors of eleven common cancer pathways in melanoma cell lines and canine angiosarcoma cell isolates. Combination indices were calculated using the Chou-Talalay method. Optimized combination therapies were evaluated in vivo for toxicity and efficacy using canine angiosarcoma tumorgrafts. Among the drugs we tested, rapamycin stood out because it showed strong synergy with PD0325901 at nanomolar concentrations. We observed that angiosarcomas are insensitive to mTOR inhibition. However, treatment with nanomolar levels of mTOR inhibitor renders these cells as sensitive to MEK inhibition as a melanoma cell line with mutant BRAF. Similar results were observed in B-Raf wild-type melanoma cells as well as in vivo, where treatment of canine AS tumorgrafts with MEK and mTOR inhibitors was more effective than monotherapy. Our data show that a low dose of an mTOR inhibitor can dramatically enhance angiosarcoma and melanoma response to MEK inhibition, potentially widening the field of applications for MEK-targeted therapy.

Simultaneous inference of cancer pathways and tumor progression from cross-sectional mutation data

Recent cancer sequencing studies provide a wealth of somatic mutation data from a large number of patients. One of the most intriguing and challenging questions arising from this data is to determine whether the temporal order of somatic mutations in a cancer follows any common progression. Since we usually obtain only one sample from a patient, such inferences are commonly made from cross-sectional data from different patients. This analysis is complicated by the extensive variation in the somatic mutations across different patients, variation that is reduced by examining combinations of mutations in various pathways. Thus far, methods to reconstruct tumor progression at the pathway level have restricted attention to known, a priori defined pathways. In this work we show how to simultaneously infer pathways and the temporal order of their mutations from cross-sectional data, leveraging on the exclusivity property of driver mutations within a pathway. We define the pathway linear progression model, and derive a combinatorial formulation for the problem of finding the optimal model from mutation data. We show that with enough samples the optimal solution to this problem uniquely identifies the correct model with high probability even when errors are present in the mutation data. We then formulate the problem as an integer linear program (ILP), which allows the analysis of datasets from recent studies with large numbers of samples. We use our algorithm to analyze somatic mutation data from three cancer studies, including two studies from The Cancer Genome Atlas (TCGA) on large number of samples on colorectal cancer and glioblastoma. The models reconstructed with our method capture most of the current knowledge of the progression of somatic mutations in these cancer types, while also providing new insights on the tumor progression at the pathway level.

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.

Biological and therapeutic impact of intratumor heterogeneity in cancer evolution

Precision medicine requires an understanding of cancer genes and mutational processes, as well as an appreciation of the extent to which these are found heterogeneously in cancer cells during tumor evolution. Here, we explore the processes shaping the cancer genome, placing these within the context of tumor evolution and their impact on intratumor heterogeneity and drug development. We review evidence for constraints and contingencies to tumor evolution and highlight the clinical implications of diversity within tumors. We outline the limitations of genome-driven targeted therapies and explore future strategies, including immune and adaptive approaches, to address this therapeutic challenge.