Estimating the order of mutations during tumorigenesis from tumor genome sequencing data

A Probabilistic Approach to Estimate the Temporal Order of Pathway Mutations Accounting for Intra-Tumor Heterogeneity

The development of cancer involves the accumulation of somatic mutations in several essential biological pathways. Delineating the temporal order of pathway mutations during tumorigenesis is crucial for comprehending the biological mechanisms underlying cancer development and identifying potential targets for therapeutic intervention. Several computational and statistical methods have been introduced for estimating the order of somatic mutations based on mutation profile data from a cohort of patients. However, one major issue of current methods is that they do not take into account intra-tumor heterogeneity (ITH), which limits their ability to accurately discern the order of pathway mutations. To address this problem, we propose PATOPAI, a probabilistic approach to estimate the temporal order of mutations at the pathway level by incorporating ITH information as well as pathway and functional annotation information of mutations. PATOPAI uses a maximum likelihood approach to estimate the probability of pathway mutational events occurring in a specific sequence, wherein it focuses on the orders that are consistent with the phylogenetic structure of the tumors. Applications to whole exome sequencing data from The Cancer Genome Atlas (TCGA) illustrate our method's ability to recover the temporal order of pathway mutations in several cancer types.

Chromosome 7 to the rescue: overcoming chromosome 10 loss in gliomas

The co-occurrence of chromosome 10 loss and chromosome 7 gain in gliomas is the most frequent loss-gain co-aneuploidy pair in human cancers, a phenomenon that has been investigated without resolution since the late 1980s. Expanding beyond previous gene-centric studies, we investigate the co-occurrence in a genome-wide manner taking an evolutionary perspective. First, by mining large tumor aneuploidy data, we predict that the more likely order is 10 loss followed by 7 gain. Second, by analyzing extensive genomic and transcriptomic data from both patients and cell lines, we find that this co-occurrence can be explained by functional rescue interactions that are highly enriched on 7, which can possibly compensate for any detrimental consequences arising from the loss of 10. Finally, by analyzing transcriptomic data from normal, non-cancerous, human brain tissues, we provide a plausible reason why this co-occurrence happens preferentially in cancers originating in certain regions of the brain.

HyperHMM: efficient inference of evolutionary and progressive dynamics on hypercubic transition graphs

MOTIVATION

The evolution of bacterial drug resistance and other features in biology, the progression of cancer and other diseases and a wide range of broader questions can often be viewed as the sequential stochastic acquisition of binary traits (e.g. genetic changes, symptoms or characters). Using potentially noisy or incomplete data to learn the sequences by which such traits are acquired is a problem of general interest. The problem is complicated for large numbers of traits, which may, individually or synergistically, influence the probability of further acquisitions both positively and negatively. Hypercubic inference approaches, based on hidden Markov models on a hypercubic transition network, address these complications, but previous Bayesian instances can consume substantial time for converged results, limiting their practical use.

RESULTS

Here, we introduce HyperHMM, an adapted Baum-Welch (expectation-maximization) algorithm for hypercubic inference with resampling to quantify uncertainty, and show that it allows orders-of-magnitude faster inference while making few practical sacrifices compared to previous hypercubic inference approaches. We show that HyperHMM allows any combination of traits to exert arbitrary positive or negative influence on the acquisition of other traits, relaxing a common limitation of only independent trait influences. We apply this approach to synthetic and biological datasets and discuss its more general application in learning evolutionary and progressive pathways.

AVAILABILITY AND IMPLEMENTATION

Code for inference and visualization, and data for example cases, is freely available at https://github.com/StochasticBiology/hypercube-hmm.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

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.

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.

Functional Prediction of Candidate MicroRNAs for CRC Management Using in Silico Approach

Approximately 30–50% of malignant growths can be prevented by avoiding risk factors and implementing evidence-based strategies. Colorectal cancer (CRC) accounted for the second most common cancer and the third most common cause of cancer death worldwide. This cancer subtype can be reduced by early detection and patients’ management. In this study, the functional roles of the identified microRNAs were determined using an in silico pipeline. Five microRNAs identified using an in silico approach alongside their seven target genes from our previous study were used as datasets in this study. Furthermore, the secondary structure and the thermodynamic energies of the microRNAs were revealed by Mfold algorithm. The triplex binding ability of the oligonucleotide with the target promoters were analyzed by Trident. Finally, evolutionary stage-specific somatic events and co-expression analysis of the target genes in CRC were analyzed by SEECancer and GeneMANIA plugin in Cytoscape. Four of the five microRNAs have the potential to form more than one secondary structure. The ranges of the observed/expected ratio of CpG dinucleotides of these genes range from 0.60 to 1.22. Three of the candidate microRNA were capable of forming multiple triplexes along with three of the target mRNAs. Four of the total targets were involved in either early or metastatic stage-specific events while three other genes were either a product of antecedent or subsequent events of the four genes implicated in CRC. The secondary structure of the candidate microRNAs can be used to explain the different degrees of genetic regulation in CRC due to their conformational role to modulate target interaction. Furthermore, due to the regulation of important genes in the CRC pathway and the enrichment of the microRNA with triplex binding sites, they may be a useful diagnostic biomarker for the disease subtype.

Genetic instability and recurrent MYC amplification in ALK-translocated NSCLC: a central role of TP53 mutations

The anaplastic lymphoma kinase (ALK) rearrangement defines a distinct molecular subtype of non-small cell lung cancer (NSCLC). Despite the excellent initial efficacy of ALK inhibitors in patients with ALK+ lung cancer, resistance occurs almost inevitably. To date, there is no reliable biomarker allowing the identification of patients at higher risk of relapse. Here, we analysed a subset of 53 ALK+ tumours with and without TP53 mutation and ALK+ NSCLC cell lines by NanoString nCounter technology. We found that the co-occurrence of early TP53 mutations in ALK+ NSCLC can lead to chromosomal instability: 24% of TP53-mutated patients showed amplifications of known cancer genes such as MYC (14%), CCND1 (10%), TERT (5%), BIRC2 (5%), ORAOV1 (5%), and YAP1 (5%). MYC-overexpressing ALK+ TP53-mutated cells had a proliferative advantage compared to wild-type cells. ChIP-Seq data revealed MYC-binding sites within the promoter region of EML4, and MYC overexpression in ALK+ TP53-mutated cells resulted in an upregulation of EML4-ALK, indicating a potential MYC-dependent resistance mechanism in patients with increased MYC copy number. Our study reveals that ALK+ NSCLC represents a more heterogeneous subgroup of tumours than initially thought, and that TP53 mutations in that particular cancer type define a subset of tumours that harbour chromosomal instability, leading to the co-occurrence of pathogenic aberrations. © 2018 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Somatic evolutionary timings of driver mutations

BACKGROUND

A unified analysis of DNA sequences from hundreds of tumors concluded that the driver mutations primarily occur in the earliest stages of cancer formation, with relatively few driver mutation events detected in the late-arising subclones. However, emerging evidence from the sequencing of multiple tumors and tumor regions per individual suggests that late-arising subclones with additional driver mutations are underestimated in single-sample analyses.

METHODS

To test whether driver mutations generally map to early tumor development, we examined multi-regional tumor sequencing data from 101 individuals reported in 11 published studies. Following previous studies, we annotated mutations as early-arising when all tumors/regions had those mutations (ubiquitous). We then inferred the fraction of mutations occurring early and compared it with late-arising mutations that were found in only single tumors/regions.

RESULTS

While a large fraction of driver mutations in tumors occurred relatively early in cancers, later driver mutations occurred at least as frequently as the early drivers in a substantial number of patients. This result was robust to many different approaches to annotate driver mutations. The relative frequency of early and late driver mutations varied among patients of the same cancer type and in different cancer types. We found that previous reports of the preponderance of early driver mutations were primarily informed by analysis of single tumor variant allele profiles, with which it is challenging to clearly distinguish between early and late drivers.

CONCLUSIONS

The origin and preponderance of new driver mutations are not limited to early stages of tumor evolution, with different tumors and regions showing distinct driver mutations and, consequently, distinct characteristics. Therefore, tumors with extensive intratumor heterogeneity appear to have many newly acquired drivers.

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.

Age-related mutations and chronic myelomonocytic leukemia

Chronic myelomonocytic leukemia (CMML) is a hematologic malignancy nearly confined to the elderly. Previous studies to determine incidence and prognostic significance of somatic mutations in CMML have relied on candidate gene sequencing, although an unbiased mutational search has not been conducted. As many of the genes commonly mutated in CMML were recently associated with age-related clonal hematopoiesis (ARCH) and aged hematopoiesis is characterized by a myelomonocytic differentiation bias, we hypothesized that CMML and aged hematopoiesis may be closely related. We initially established the somatic mutation landscape of CMML by whole exome sequencing followed by gene-targeted validation. Genes mutated in ⩾10% of patients were SRSF2, TET2, ASXL1, RUNX1, SETBP1, KRAS, EZH2, CBL and NRAS, as well as the novel CMML genes FAT4, ARIH1, DNAH2 and CSMD1. Most CMML patients (71%) had mutations in ⩾2 ARCH genes and 52% had ⩾7 mutations overall. Higher mutation burden was associated with shorter survival. Age-adjusted population incidence and reported ARCH mutation rates are consistent with a model in which clinical CMML ensues when a sufficient number of stochastically acquired age-related mutations has accumulated, suggesting that CMML represents the leukemic conversion of the myelomonocytic-lineage-biased aged hematopoietic system.

An Evolutionary Approach for Identifying Driver Mutations in Colorectal Cancer

The traditional view of cancer as a genetic disease that can successfully be treated with drugs targeting mutant onco-proteins has motivated whole-genome sequencing efforts in many human cancer types. However, only a subset of mutations found within the genomic landscape of cancer is likely to provide a fitness advantage to the cell. Distinguishing such “driver” mutations from innocuous “passenger” events is critical for prioritizing the validation of candidate mutations in disease-relevant models. We design a novel statistical index, called the Hitchhiking Index, which reflects the probability that any observed candidate gene is a passenger alteration, given the frequency of alterations in a cross-sectional cancer sample set, and apply it to a mutational data set in colorectal cancer. Our methodology is based upon a population dynamics model of mutation accumulation and selection in colorectal tissue prior to cancer initiation as well as during tumorigenesis. This methodology can be used to aid in the prioritization of candidate mutations for functional validation and contributes to the process of drug discovery.

Evolutionary dynamic models have been intensively studied to elucidate the process of tumorigenesis. One key aspect of studying tumorigenesis is to distinguish the “driver” mutations providing a fitness advantage to cancer cells against neutral “passenger” or “hitchhiking” mutations. Many statistical models to address this question have been developed. Evolutionary models, however, add another layer of complexity by taking into account the process of mutation accumulation and selection within the tissue. Here we present a novel approach combining both statistical and evolutionary thinking to identify driver mutations in cancer genomes using cross-sectional mutation data. Our method considers the process of mutation accumulation and selection before and during colorectal cancer initiation. This work demonstrates the importance of using evolutionary population dynamic models to study driver events of tumorigenesis.

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.

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.

Cancer evolution: mathematical models and computational inference

Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy.

Using passenger mutations to estimate the timing of driver mutations and identify mutator alterations

Background

Recent developments in high-throughput genomic technologies make it possible to have a comprehensive view of genomic alterations in tumors on a whole genome scale. Only a small number of somatic alterations detected in tumor genomes are driver alterations which drive tumorigenesis. Most of the somatic alterations are passengers that are neutral to tumor cell selection. Although most research efforts are focused on analyzing driver alterations, the passenger alterations also provide valuable information about the history of tumor development.

Results

In this paper, we develop a method for estimating the age of the tumor lineage and the timing of the driver alterations based on the number of passenger alterations. This method also identifies mutator genes which increase genomic instability when they are altered and provides estimates of the increased rate of alterations caused by each mutator gene. We applied this method to copy number data and DNA sequencing data for ovarian and lung tumors. We identified well known mutators such as TP53, PRKDC, BRCA1/2 as well as new mutator candidates PPP2R2A and the chromosomal region 22q13.33. We found that most mutator genes alter early during tumorigenesis and were able to estimate the age of individual tumor lineage in cell generations.

Conclusions

This is the first computational method to identify mutator genes and to take into account the increase of the alteration rate by mutator genes, providing more accurate estimates of the tumor age and the timing of driver alterations.

Genetic mutation screen in early non--small-cell lung cancer (NSCLC) specimens

BACKGROUND

Testing for genetic abnormalities in epithelial growth factor receptor (EGFR), anaplastic lymphoma receptor tyrosine kinase (ALK), and potentially additional genes is a critical tool in the care of advanced NSCLC. There is conflicting evidence for the role of such tests in early NSCLC. We report a single-institute Sequenom testing for a wide range of mutations and their clinical correlations in early-resected NSCLC specimens.

MATERIALS AND METHODS

Early NSCLC paraffin-embedded, formalin-fixed (FFPE) specimens were collected, DNA extracted, and using Sequenom-based matrix-assisted laser desorption/ionization-time of flight analysis, mutations in 22 oncogenes and tumor suppressor genes were evaluated. Clinical data was collected retrospectively.

RESULTS

The technique was found to be feasible. Thirty-six of 96 patients (37.5%) had any genetic abnormality identified, and 8 (8.3%) had 2 or more mutations. Kirsten rat sarcoma viral oncogene homolog (KRAS) and EGFR were the most common genes to appear mutated (15.6%); phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA) was the gene to be found most commonly in tumors with co-mutations. Transversions were found mostly in KRAS gene mutations and to be nonprognostic. No difference in the spectrum of mutations was found between squamous-cell and non-squamous-cell lung cancers. Ever-smokers showed a trend for worse prognosis, with a similar spectrum of mutations.

CONCLUSION

Sequenom-based mutation screen is feasible using FFPE samples. More than a third of the patients were found to harbor some genetic abnormality, and 8% were found to have more than a single mutated gene. Wide-range gene screens using large sample depositories are required for further insight into the important genes at play in early NSCLC.

Inhibition of BRCA2 and Thymidylate Synthase Creates Multidrug Sensitive Tumor Cells via the Induction of Combined "Complementary Lethality"

A high mutation rate leading to tumor cell heterogeneity is a driver of malignancy in human cancers. Paradoxically, however, genomic instability can also render tumors vulnerable to therapeutic attack. Thus, targeting DNA repair may induce an intolerable level of DNA damage in tumor cells. BRCA2 mediates homologous recombination repair, and BRCA2 polymorphisms increase cancer risk. However, tumors with BRCA2 mutations respond better to chemotherapy and are associated with improved patient prognosis. Thymidylate synthase (TS) is also involved in DNA maintenance and generates cellular thymidylate. We determined that antisense downregulation of BRCA2 synergistically potentiated drugs with mechanisms of action related to BRCA2 function (cisplatin, melphalan), a phenomenon we named “complementary lethality.” TS knockdown induced complementary lethality to TS-targeting drugs (5-FUdR and pemetrexed) but not DNA cross-linking agents. Combined targeting of BRCA2 and TS induced complementary lethality to both DNA-damaging and TS-targeting agents, thus creating multidrug sensitive tumors. In addition, we demonstrated for the first time that simultaneous downregulation of both targets induced combined complementary lethality to multiple mechanistically different drugs in the same cell population. In this study, we propose and define the concept of “complementary lethality” and show that actively targeting BRCA2 and TS is of potential therapeutic benefit in multidrug treatment of human tumors. This work has contributed to the development of a BRCA2-targeting antisense oligdeoxynucleotide (ASO) “BR-1” which we will test in vivo in combination with our TS-targeting ASO “SARI 83” and attempt early clinical trials in the future.

How to develop treatments for biologically heterogeneous "diseases"

The standard paradigm for the design of phase III clinical trials is not suitable for evaluation of molecularly targeted treatments in biologically heterogeneous groups of patients. Here, we comment on alternative clinical trial designs and propose a prospective discovery/evaluation framework for using tumor genomics in the design of phase III trials.