Phylogenetic quantification of intra-tumour heterogeneity

QuantumClone: clonal assessment of functional mutations in cancer based on a genotype-aware method for clonal reconstruction

Motivation

In cancer, clonal evolution is assessed based on information coming from single nucleotide variants and copy number alterations. Nonetheless, existing methods often fail to accurately combine information from both sources to truthfully reconstruct clonal populations in a given tumor sample or in a set of tumor samples coming from the same patient. Moreover, previously published methods detect clones from a single set of variants. As a result, compromises have to be done between stringent variant filtering [reducing dispersion in variant allele frequency estimates (VAFs)] and using all biologically relevant variants.

Results

We present a framework for defining cancer clones using most reliable variants of high depth of coverage and assigning functional mutations to the detected clones. The key element of our framework is QuantumClone, a method for variant clustering into clones based on VAFs, genotypes of corresponding regions and information about tumor purity. We validated QuantumClone and our framework on simulated data. We then applied our framework to whole genome sequencing data for 19 neuroblastoma trios each including constitutional, diagnosis and relapse samples. We confirmed an enrichment of damaging variants within such pathways as MAPK (mitogen-activated protein kinases), neuritogenesis, epithelial-mesenchymal transition, cell survival and DNA repair. Most pathways had more damaging variants in the expanding clones compared to shrinking ones, which can be explained by the increased total number of variants between these two populations. Functional mutational rate varied for ancestral clones and clones shrinking or expanding upon treatment, suggesting changes in clone selection mechanisms at different time points of tumor evolution.

Availability and implementation

Source code and binaries of the QuantumClone R package are freely available for download at https://CRAN.R-project.org/package=QuantumClone.

Contact

gudrun.schleiermacher@curie.fr or valentina.boeva@inserm.fr.

Supplementary information

Supplementary data are available at Bioinformatics online.

The Genomic and Immune Landscapes of Lethal Metastatic Breast Cancer

The detailed molecular characterization of lethal cancers is a prerequisite to understanding resistance to therapy and escape from cancer immunoediting. We performed extensive multi-platform profiling of multi-regional metastases in autopsies from 10 patients with therapy-resistant breast cancer. The integrated genomic and immune landscapes show that metastases propagate and evolve as communities of clones, reveal their predicted neo-antigen landscapes, and show that they can accumulate HLA loss of heterozygosity (LOH). The data further identify variable tumor microenvironments and reveal, through analyses of T cell receptor repertoires, that adaptive immune responses appear to co-evolve with the metastatic genomes. These findings reveal in fine detail the landscapes of lethal metastatic breast cancer.

Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data

BACKGROUND

A large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data. Most methods can analyze multiple samples collected either through bulk multi-region sequencing experiments or the sequencing of individual cancer cells. However, rarely the same method can support both data types.

RESULTS

We introduce TRaIT, a computational framework to infer mutational graphs that model the accumulation of multiple types of somatic alterations driving tumour evolution. Compared to other tools, TRaIT supports multi-region and single-cell sequencing data within the same statistical framework, and delivers expressive models that capture many complex evolutionary phenomena. TRaIT improves accuracy, robustness to data-specific errors and computational complexity compared to competing methods.

CONCLUSIONS

We show that the application of TRaIT to single-cell and multi-region cancer datasets can produce accurate and reliable models of single-tumour evolution, quantify the extent of intra-tumour heterogeneity and generate new testable experimental hypotheses.

Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks

BACKGROUND

Advances in large-scale tumor sequencing have led to an understanding that there are combinations of genomic and transcriptomic alterations specific to tumor types, shared across many patients. Unfortunately, computational identification of functionally meaningful and recurrent alteration patterns within gene/protein interaction networks has proven to be challenging.

FINDINGS

We introduce a novel combinatorial method, cd-CAP (combinatorial detection of conserved alteration patterns), for simultaneous detection of connected subnetworks of an interaction network where genes exhibit conserved alteration patterns across tumor samples. Our method differentiates distinct alteration types associated with each gene (rather than relying on binary information of a gene being altered or not) and simultaneously detects multiple alteration profile conserved subnetworks.

CONCLUSIONS

In a number of The Cancer Genome Atlas datasets, cd-CAP identified large biologically significant subnetworks with conserved alteration patterns, shared across many tumor samples.

Translating genomics to the clinical diagnosis of disorders/differences of sex development

The medical and psychosocial challenges faced by patients living with Disorders/Differences of Sex Development (DSD) and their families can be alleviated by a rapid and accurate diagnostic process. Clinical diagnosis of DSD is limited by a lack of standardization of anatomical and endocrine phenotyping and genetic testing, as well as poor genotype/phenotype correlation. Historically, DSD genes have been identified through positional cloning of disease-associated variants segregating in families and validation of candidates in animal and in vitro modeling of variant pathogenicity. Owing to the complexity of conditions grouped under DSD, genome-wide scanning methods are better suited for identifying disease causing gene variant(s) and providing a clinical diagnosis. Here, we review a number of established genomic tools (karyotyping, chromosomal microarrays and exome sequencing) used in clinic for DSD diagnosis, as well as emerging genomic technologies such as whole-genome (short-read) sequencing, long-read sequencing, and optical mapping used for novel DSD gene discovery. These, together with gene expression and epigenetic studies can potentiate the clinical diagnosis of DSD diagnostic rates and enhance the outcomes for patients and families.

Phylogenetic Reconstruction for Copy-Number Evolution Problems

Cancer is known for its heterogeneity and is regarded as an evolutionary process driven by somatic mutations and clonal expansions. This evolutionary process can be modeled by a phylogenetic tree and phylogenetic analysis of multiple subclones of cancer cells can facilitate the study of the tumor variants progression. Copy-number aberration occurs frequently in many types of tumors in terms of segmental amplifications and deletions. In this paper, we developed a distance-based method for reconstructing phylogenies from copy-number profiles of cancer cells. We demonstrate the importance of distance correction from the edit (minimum) distance to the estimated actual number of events. Experimental results show that our approaches provide accurate and scalable results in estimating the actual number of evolutionary events between copy number profiles and in reconstructing phylogenies.

Clonal replacement and heterogeneity in breast tumors treated with neoadjuvant HER2-targeted therapy

Genomic changes observed across treatment may result from either clonal evolution or geographically disparate sampling of heterogeneous tumors. Here we use computational modeling based on analysis of fifteen primary breast tumors and find that apparent clonal change between two tumor samples can frequently be explained by pre-treatment heterogeneity, such that at least two regions are necessary to detect treatment-induced clonal shifts. To assess for clonal replacement, we devise a summary statistic based on whole-exome sequencing of a pre-treatment biopsy and multi-region sampling of the post-treatment surgical specimen and apply this measure to five breast tumors treated with neoadjuvant HER2-targeted therapy. Two tumors underwent clonal replacement with treatment, and mathematical modeling indicates these two tumors had resistant subclones prior to treatment and rates of resistance-related genomic changes that were substantially larger than previous estimates. Our results provide a needed framework to incorporate primary tumor heterogeneity in investigating the evolution of resistance.

Computational Methods for Analysis of Tumor Clonality and Evolutionary History

Cancer is an evolutionary process. Recent advances in sequencing technologies have allowed us to investigate intratumor heterogeneity at the single nucleotide level. Here, we describe computational methods that use sequencing data to identify genetically distinct tumor subclones and reconstruct tumor evolutionary histories.

Intratumoral Heterogeneity in Ductal Carcinoma In Situ: Chaos and Consequence

Ductal carcinoma in situ (DCIS) is a non-invasive proliferative growth in the breast that serves as a non-obligate precursor to invasive ductal carcinoma. The widespread adoption of screening mammography has led to a steep increase in the detection of DCIS, which now comprises approximately 20% of new breast cancer diagnoses in the United States. Interestingly, the intratumoral heterogeneity (ITH) that has been observed in invasive breast cancers may have been established early in tumorigenesis, given the vast and varied ITH that has been detected in DCIS. This review will discuss the intratumoral heterogeneity of DCIS, focusing on the phenotypic and genomic heterogeneity of tumor cells, as well as the compositional heterogeneity of the tumor microenvironment. In addition, we will assess the spatial heterogeneity that is now being appreciated in these lesions, and summarize new approaches to evaluate heterogeneity of tumor and stromal cells in the context of their spatial organization. Importantly, we will discuss how a growing understanding of ITH has led to a more holistic appreciation of the complex biology of DCIS, specifically its evolution and natural history. Finally, we will consider ways in which our knowledge of DCIS ITH might be translated in the future to guide clinical care for DCIS patients.

TargetClone: A multi-sample approach for reconstructing subclonal evolution of tumors

Most tumors are composed of a heterogeneous population of subclones. A more detailed insight into the subclonal evolution of these tumors can be helpful to study progression and treatment response. Problematically, tumor samples are typically very heterogeneous, making deconvolving individual tumor subclones a major challenge. To overcome this limitation, reducing heterogeneity, such as by means of microdissections, coupled with targeted sequencing, is a viable approach. However, computational methods that enable reconstruction of the evolutionary relationships require unbiased read depth measurements, which are commonly challenging to obtain in this setting. We introduce TargetClone, a novel method to reconstruct the subclonal evolution tree of tumors from single-nucleotide polymorphism allele frequency and somatic single-nucleotide variant measurements. Furthermore, our method infers copy numbers, alleles and the fraction of the tumor component in each sample. TargetClone was specifically designed for targeted sequencing data obtained from microdissected samples. We demonstrate that our method obtains low error rates on simulated data. Additionally, we show that our method is able to reconstruct expected trees in a testicular germ cell cancer and ovarian cancer dataset. The TargetClone package including tree visualization is written in Python and is publicly available at https://github.com/UMCUGenetics/targetclone.

Evolutionary Distance Predicts Recurrence After Liver Transplantation in Multifocal Hepatocellular Carcinoma

Background

Liver transplantation (LTx) is a potentially curative treatment option for hepatocellular carcinoma (HCC) in cirrhosis. However, patients, where HCC is already a systemic disease, LTx may be individually harmful and has a negative impact on donor organ usage. Thus, there is a need for improved selection criteria beyond nodule morphology to select patients with a favorable outcome for LTx in multifocal HCC. Evolutionary distance measured from genome-wide single-nucleotide polymorphism data between tumor nodules and the cirrhotic liver may be a prognostic marker of survival after LTx for multifocal HCC.

Methods

In a retrospective multicenter study, clinical data and formalin-fixed paraffin-embedded specimens of the liver and 2 tumor nodules were obtained from explants of 30 patients in the discovery and 180 patients in the replication cohort. DNA was extracted from formalin-fixed paraffin-embedded specimens followed by genome wide single-nucleotide polymorphism genotyping.

Results

Genotype quality criteria allowed for analysis of 8 patients in the discovery and 17 patients in the replication set. DNA concentrations of a total of 25 patients fulfilled the quality criteria and were included in the analysis. Both, in the discovery (P = 0.04) and in the replication data sets (P = 0.01), evolutionary distance was associated with the risk of recurrence of HCC after transplantation (combined P = 0.0002). In a univariate analysis, evolutionary distance (P = 7.4 × 10⁻⁶) and microvascular invasion (P = 1.31 × 10⁻⁵) were significantly associated with survival in a Cox regression analysis.

Conclusions

Evolutionary distance allows for the determination of a high-risk group of recurrence if preoperative liver biopsy is considered.

The authors of this multicenter retrospective study assess whether the evolutionary distance measured from genome-wide single nucleotide polymorphism (SNP) data between tumor nodules and the cirrhotic liver may be a prognostic marker of survival after liver transplantation for multifocal hepatocellular carcinoma. Supplemental digital content is available in the text.

Phylogenetic Copy-Number Factorization of Multiple Tumor Samples

Cancer is an evolutionary process driven by somatic mutations. This process can be represented as a phylogenetic tree. Constructing such a phylogenetic tree from genome sequencing data is a challenging task due to the many types of mutations in cancer and the fact that nearly all cancer sequencing is of a bulk tumor, measuring a superposition of somatic mutations present in different cells. We study the problem of reconstructing tumor phylogenies from copy-number aberrations (CNAs) measured in bulk-sequencing data. We introduce the Copy-Number Tree Mixture Deconvolution (CNTMD) problem, which aims to find the phylogenetic tree with the fewest number of CNAs that explain the copy-number data from multiple samples of a tumor. We design an algorithm for solving the CNTMD problem and apply the algorithm to both simulated and real data. On simulated data, we find that our algorithm outperforms existing approaches that either perform deconvolution/factorization of mixed tumor samples or build phylogenetic trees assuming homogeneous tumor samples. On real data, we analyze multiple samples from a prostate cancer patient, identifying clones within these samples and a phylogenetic tree that relates these clones and their differing proportions across samples. This phylogenetic tree provides a higher resolution view of copy-number evolution of this cancer than published analyses.

Sorting cancer karyotypes using double-cut-and-joins, duplications and deletions

Motivation

Problems of genome rearrangement are central in both evolution and cancer research. Most genome rearrangement models assume that the genome contains a single copy of each gene and the only changes in the genome are structural, i.e., reordering of segments. In contrast, tumor genomes also undergo numerical changes such as deletions and duplications, and thus the number of copies of genes varies. Dealing with unequal gene content is a very challenging task, addressed by few algorithms to date. More realistic models are needed to help trace genome evolution during tumorigenesis.

Results

Here we present a model for the evolution of genomes with multiple gene copies using the operation types double-cut-and-joins, duplications and deletions. The events supported by the model are reversals, translocations, tandem duplications, segmental deletions, and chromosomal amplifications and deletions, covering most types of structural and numerical changes observed in tumor samples. Our goal is to find a series of operations of minimum length that transform one karyotype into the other. We show that the problem is NP-hard and give an integer linear programming formulation that solves the problem exactly under some mild assumptions. We test our method on simulated genomes and on ovarian cancer genomes. Our study advances the state of the art in two ways: It allows a broader set of operations than extant models, thus being more realistic, and it is the first study attempting to reconstruct the full sequence of structural and numerical events during cancer evolution.

Availability

Code and data are available in https://github.com/Shamir-Lab/Sorting-Cancer-Karyotypes.

Contact

ronzeira@post.tau.ac.il, rshamir@tau.ac.il.

Supplementary information

Supplementary data are available at Bioinformatics online.

Delineation of human prostate cancer evolution identifies chromothripsis as a polyclonal event and FKBP4 as a potential driver of castration resistance

Understanding the evolutionary mechanisms and genomic events leading to castration-resistant (CR) prostate cancer (PC) is key to improve the outcome of this otherwise deadly disease. Here, we delineated the tumour history of seven patients progressing to castration resistance by analysing matched prostate cancer tissues before and after castration. We performed genomic profiling of DNA content-based flow-sorted populations in order to define the different evolutionary patterns. In one patient, we discovered that a catastrophic genomic event, known as chromothripsis, resulted in multiple CRPC tumour populations with distinct, potentially advantageous copy number aberrations, including an amplification of FK506 binding protein 4 (FKBP4, also known as FKBP52), a protein enhancing the transcriptional activity of androgen receptor signalling. Analysis of FKBP4 protein expression in more than 500 prostate cancer samples revealed increased expression in CRPC in comparison to hormone-naïve (HN) PC. Moreover, elevated FKBP4 expression was associated with poor survival of patients with HNPC. We propose FKBP4 amplification and overexpression as a selective advantage in the process of tumour evolution and as a potential mechanism associated with the development of CRPC. Furthermore, FKBP4 interaction with androgen receptor may provide a potential therapeutic target in PC. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Phylogenetic Quantification of Intratumor Heterogeneity

As sequencing efforts continue to reveal the extent of the intratumor heterogeneity (ITH) present in human cancers, the importance of evolutionary studies attempting to trace its etiology has increased. Sequencing multiple samples or tumor regions from the same patient has become affordable and is an effective way of tracing these evolutionary pathways, understanding selection, and detecting clonal expansions in ways impractical with single samples alone. In this article, we discuss and show the benefits of such multisample studies. We describe how multiple samples can guide tree inference through accurate phasing of germline variants and copy-number profiles. We show their relevance in detecting clonal expansions and deriving summary statistics quantifying the overall degree of ITH, and discuss how the relationship of metastatic clades might give us insight into the dominant mode of cancer progression. We further outline how multisample studies might help us better understand selective processes acting on cancer genomes and help to detect neutral evolution and mutator phenotypes.

Integrating Analysis of Cellular Heterogeneity in High-Content Dose-Response Studies

Heterogeneity is a complex property of cellular systems and therefore presents challenges to the reliable identification and characterization. Large-scale biology projects may span many months, requiring a systematic approach to quality control to track reproducibility and correct for instrumental variation and assay drift that could mask biological heterogeneity and preclude comparisons of heterogeneity between runs or even between plates. However, presently there is no standard approach to the tracking and analysis of heterogeneity. Previously, we demonstrated the use of the Kolmogorov-Smirnov statistic as a metric for monitoring the reproducibility of heterogeneity in a screen and described the use of three heterogeneity indices as a means to characterize, filter, and browse cellular heterogeneity in big data sets (Gough et al., Methods 96:12-26, 2016). In this chapter, we present a detailed method for integrating the analysis of cellular heterogeneity in assay development, validation, screening, and post screen. Importantly, we provide a detailed method for quality control, to normalize cellular data, track heterogeneity over time, and analyze heterogeneity in big data sets, along with software tools to assist in that process. The example screen for this method is from an HCS project, but the approach applies equally to other experimental methods that measure populations of cells.

Genetic alterations driving metastatic colony formation are acquired outside of the primary tumour in melanoma

Mouse models indicate that metastatic dissemination occurs extremely early; however, the timing in human cancers is unknown. We therefore determined the time point of metastatic seeding relative to tumour thickness and genomic alterations in melanoma. Here, we find that lymphatic dissemination occurs shortly after dermal invasion of the primary lesion at a median thickness of ~0.5 mm and that typical driver changes, including BRAF mutation and gained or lost regions comprising genes like MET or CDKNA2, are acquired within the lymph node at the time of colony formation. These changes define a colonisation signature that was linked to xenograft formation in immunodeficient mice and death from melanoma. Thus, melanoma cells leave primary tumours early and evolve at different sites in parallel. We propose a model of metastatic melanoma dormancy, evolution and colonisation that will inform direct monitoring of adjuvant therapy targets.

Cell subpopulation deconvolution reveals breast cancer heterogeneity based on DNA methylation signature

Tumour heterogeneity describes the coexistence of divergent tumour cell clones within tumours, which is often caused by underlying epigenetic changes. DNA methylation is commonly regarded as a significant regulator that differs across cells and tissues. In this study, we comprehensively reviewed research progress on estimating of tumour heterogeneity. Bioinformatics-based analysis of DNA methylation has revealed the evolutionary relationships between breast cancer cell lines and tissues. Further analysis of the DNA methylation profiles in 33 breast cancer-related cell lines identified cell line-specific methylation patterns. Next, we reviewed the computational methods in inferring clonal evolution of tumours from different perspectives and then proposed a deconvolution strategy for modelling cell subclonal populations dynamics in breast cancer tissues based on DNA methylation. Further analysis of simulated cancer tissues and real cell lines revealed that this approach exhibits satisfactory performance and relative stability in estimating the composition and proportions of cellular subpopulations. The application of this strategy to breast cancer individuals of the Cancer Genome Atlas's identified different cellular subpopulations with distinct molecular phenotypes. Moreover, the current and potential future applications of this deconvolution strategy to clinical breast cancer research are discussed, and emphasis was placed on the DNA methylation-based recognition of intra-tumour heterogeneity. The wide use of these methods for estimating heterogeneity to further clinical cohorts will improve our understanding of neoplastic progression and the design of therapeutic interventions for treating breast cancer and other malignancies.

Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies

A comprehensive characterization of tumor genetic heterogeneity is critical for understanding how cancers evolve and escape treatment. Although many algorithms have been developed for capturing tumor heterogeneity, they are designed for analyzing either a single type of genomic aberration or individual biopsies. Here we present THEMIS (Tumor Heterogeneity Extensible Modeling via an Integrative System), which allows for the joint analysis of different types of genomic aberrations from multiple biopsies taken from the same patient, using a dynamic graphical model. Simulation experiments demonstrate higher accuracy of THEMIS over its ancestor, TITAN. The heterogeneity analysis results from THEMIS are validated with single cell DNA sequencing from a clinical tumor biopsy. When THEMIS is used to analyze tumor heterogeneity among multiple biopsies from the same patient, it helps to reveal the mutation accumulation history, track cancer progression, and identify the mutations related to treatment resistance. We implement our model via an extensible modeling platform, which makes our approach open, reproducible, and easy for others to extend.

Characterizing the heterogeneity of tumor tissues from spatially resolved molecular measures

Background

Tumor heterogeneity can manifest itself by sub-populations of cells having distinct phenotypic profiles expressed as diverse molecular, morphological and spatial distributions. This inherent heterogeneity poses challenges in terms of diagnosis, prognosis and efficient treatment. Consequently, tools and techniques are being developed to properly characterize and quantify tumor heterogeneity. Multiplexed immunofluorescence (MxIF) is one such technology that offers molecular insight into both inter-individual and intratumor heterogeneity. It enables the quantification of both the concentration and spatial distribution of 60+ proteins across a tissue section. Upon bioimage processing, protein expression data can be generated for each cell from a tissue field of view.

Results

The Multi-Omics Heterogeneity Analysis (MOHA) tool was developed to compute tissue heterogeneity metrics from MxIF spatially resolved tissue imaging data. This technique computes the molecular state of each cell in a sample based on a pathway or gene set. Spatial states are then computed based on the spatial arrangements of the cells as distinguished by their respective molecular states. MOHA computes tissue heterogeneity metrics from the distributions of these molecular and spatially defined states. A colorectal cancer cohort of approximately 700 subjects with MxIF data is presented to demonstrate the MOHA methodology. Within this dataset, statistically significant correlations were found between the intratumor AKT pathway state diversity and cancer stage and histological tumor grade. Furthermore, intratumor spatial diversity metrics were found to correlate with cancer recurrence.

Conclusions

MOHA provides a simple and robust approach to characterize molecular and spatial heterogeneity of tissues. Research projects that generate spatially resolved tissue imaging data can take full advantage of this useful technique. The MOHA algorithm is implemented as a freely available R script (see supplementary information).

Sweepstake evolution revealed by population-genetic analysis of copy-number alterations in single genomes of breast cancer

Single-cell sequencing is a promising technology that can address cancer cell evolution by identifying genetic alterations in individual cells. In a recent study, genome-wide DNA copy numbers of single cells were accurately quantified by single-cell sequencing in breast cancers. Phylogenetic-tree analysis revealed genetically distinct populations, each consisting of homogeneous cells. Bioinformatics methods based on population genetics should be further developed to quantitatively analyse the single-cell sequencing data. We developed a bioinformatics framework that was combined with molecular-evolution theories to analyse copy-number losses. This analysis revealed that most deletions in the breast cancers at the single-cell level were generated by simple stochastic processes. A non-standard type of coalescent theory, the multiple-merger coalescent model, aided by approximate Bayesian computation fit well with the data, allowing us to estimate the population-genetic parameters in addition to false-positive and false-negative rates. The estimated parameters suggest that the cancer cells underwent sweepstake evolution, where only one or very few parental cells produced a descendent cell population. We conclude that breast cancer cells successively substitute in a tumour mass, and the high reproduction of only a portion of cancer cells may confer high adaptability to this cancer.

A Linear-Time Algorithm for the Copy Number Transformation Problem

Problems of genome rearrangement are central in both evolution and cancer. Most evolutionary scenarios have been studied under the assumption that the genome contains a single copy of each gene. In contrast, tumor genomes undergo deletions and duplications, and thus, the number of copies of genes varies. The number of copies of each segment along a chromosome is called its copy number profile (CNP). Understanding CNP changes can assist in predicting disease progression and treatment. To date, questions related to distances between CNPs gained little scientific attention. Here we focus on the following fundamental problem, introduced by Schwarz et al.: given two CNPs, u and v, compute the minimum number of operations transforming u into v, where the edit operations are segmental deletions and amplifications. We establish the computational complexity of this problem, showing that it is solvable in linear time and constant space.

Challenges and unanswered questions for the next decade of circulating tumour cell research in lung cancer

Since blood borne circulating tumour cells (CTCs) initially shed from the primary tumour can seed and initiate metastasis at distant sites a better understanding of the biology of CTCs and their dissemination could provide valuable information that could guide therapeutic intervention and real time monitoring of disease progression. Although CTC enumeration has provided a reliable prognostic readout for a number of cancers, including lung cancer, the precise clinical utility of CTCs remains to be established. The rarity of CTCs together with the vanishingly small amounts of nucleic acids present in a single cell as well as cell to cell heterogeneity has stimulated the development of a wide range of powerful cellular and molecular methodologies applied to CTCs. These technical developments are now enabling researchers to focus on understanding the biology of CTCs and their clinical utility as a predictive and pharmacodynamics markers. This review summarises recent advances in the field of CTC research with focus on technical and biological challenges as well the progress made towards clinical utility of characterisation of CTCs with emphasis on studies in lung cancer.

Quantification of within-sample genetic heterogeneity from SNP-array data

Intra-tumour genetic heterogeneity (ITH) fosters drug resistance and is a critical hurdle to clinical treatment. ITH can be well-measured using multi-region sampling but this is costly and challenging to implement. There is therefore a need for tools to estimate ITH in individual samples, using standard genomic data such as SNP-arrays, that could be implemented routinely. We designed two novel scores S and R, respectively based on the Shannon diversity index and Ripley's L statistic of spatial homogeneity, to quantify ITH in single SNP-array samples. We created in-silico and in-vitro mixtures of tumour clones, in which diversity was known for benchmarking purposes. We found significant but highly-variable associations of our scores with diversity in-silico (p < 0.001) and moderate associations in-vitro (p = 0.015 and p = 0.085). Our scores were also correlated to previous ITH estimates from sequencing data but heterogeneity in the fraction of tumour cells present across samples hampered accurate quantification. The prognostic potential of both scores was moderate but significantly predictive of survival in several tumour types (corrected p = 0.03). Our work thus shows how individual SNP-arrays reveal intra-sample clonal diversity with moderate accuracy.

Complexity and algorithms for copy-number evolution problems

BACKGROUND

Cancer is an evolutionary process characterized by the accumulation of somatic mutations in a population of cells that form a tumor. One frequent type of mutations is copy number aberrations, which alter the number of copies of genomic regions. The number of copies of each position along a chromosome constitutes the chromosome's copy-number profile. Understanding how such profiles evolve in cancer can assist in both diagnosis and prognosis.

RESULTS

We model the evolution of a tumor by segmental deletions and amplifications, and gauge distance from profile [Formula: see text] to [Formula: see text] by the minimum number of events needed to transform [Formula: see text] into [Formula: see text]. Given two profiles, our first problem aims to find a parental profile that minimizes the sum of distances to its children. Given k profiles, the second, more general problem, seeks a phylogenetic tree, whose k leaves are labeled by the k given profiles and whose internal vertices are labeled by ancestral profiles such that the sum of edge distances is minimum.

CONCLUSIONS

For the former problem we give a pseudo-polynomial dynamic programming algorithm that is linear in the profile length, and an integer linear program formulation. For the latter problem we show it is NP-hard and give an integer linear program formulation that scales to practical problem instance sizes. We assess the efficiency and quality of our algorithms on simulated instances.

AVAILABILITY

https://github.com/raphael-group/CNT-ILP.

Solving the master equation for Indels

BACKGROUND

Despite the long-anticipated possibility of putting sequence alignment on the same footing as statistical phylogenetics, theorists have struggled to develop time-dependent evolutionary models for indels that are as tractable as the analogous models for substitution events.

MAIN TEXT

This paper discusses progress in the area of insertion-deletion models, in view of recent work by Ezawa (BMC Bioinformatics 17:304, 2016); (BMC Bioinformatics 17:397, 2016); (BMC Bioinformatics 17:457, 2016) on the calculation of time-dependent gap length distributions in pairwise alignments, and current approaches for extending these approaches from ancestor-descendant pairs to phylogenetic trees.

CONCLUSIONS

While approximations that use finite-state machines (Pair HMMs and transducers) currently represent the most practical approach to problems such as sequence alignment and phylogeny, more rigorous approaches that work directly with the matrix exponential of the underlying continuous-time Markov chain also show promise, especially in view of recent advances.

Phylogenetic analysis of metastatic progression in breast cancer using somatic mutations and copy number aberrations

Several studies using genome-wide molecular techniques have reported various degrees of genetic heterogeneity between primary tumours and their distant metastases. However, it has been difficult to discern patterns of dissemination owing to the limited number of patients and available metastases. Here, we use phylogenetic techniques on data generated using whole-exome sequencing and copy number profiling of primary and multiple-matched metastatic tumours from ten autopsied patients to infer the evolutionary history of breast cancer progression. We observed two modes of disease progression. In some patients, all distant metastases cluster on a branch separate from their primary lesion. Clonal frequency analyses of somatic mutations show that the metastases have a monoclonal origin and descend from a common 'metastatic precursor'. Alternatively, multiple metastatic lesions are seeded from different clones present within the primary tumour. We further show that a metastasis can be horizontally cross-seeded. These findings provide insights into breast cancer dissemination.

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.

Advances in understanding tumour evolution through single-cell sequencing

The mutational heterogeneity observed within tumours poses additional challenges to the development of effective cancer treatments. A thorough understanding of a tumour's subclonal composition and its mutational history is essential to open up the design of treatments tailored to individual patients. Comparative studies on a large number of tumours permit the identification of mutational patterns which may refine forecasts of cancer progression, response to treatment and metastatic potential. The composition of tumours is shaped by evolutionary processes. Recent advances in next-generation sequencing offer the possibility to analyse the evolutionary history and accompanying heterogeneity of tumours at an unprecedented resolution, by sequencing single cells. New computational challenges arise when moving from bulk to single-cell sequencing data, leading to the development of novel modelling frameworks. In this review, we present the state of the art methods for understanding the phylogeny encoded in bulk or single-cell sequencing data, and highlight future directions for developing more comprehensive and informative pictures of tumour evolution. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

A population genetics perspective on the determinants of intra-tumor heterogeneity

Cancer results from the acquisition of somatic alterations in a microevolutionary process that typically occurs over many years, much of which is occult. Understanding the evolutionary dynamics that are operative at different stages of progression in individual tumors might inform the earlier detection, diagnosis, and treatment of cancer. Although these processes cannot be directly observed, the resultant spatiotemporal patterns of genetic variation amongst tumor cells encode their evolutionary histories. Such intra-tumor heterogeneity is pervasive not only at the genomic level, but also at the transcriptomic, phenotypic, and cellular levels. Given the implications for precision medicine, the accurate quantification of heterogeneity within and between tumors has become a major focus of current research. In this review, we provide a population genetics perspective on the determinants of intra-tumor heterogeneity and approaches to quantify genetic diversity. We summarize evidence for different modes of evolution based on recent cancer genome sequencing studies and discuss emerging evolutionary strategies to therapeutically exploit tumor heterogeneity. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

Tumor evolution: Linear, branching, neutral or punctuated?

Intratumor heterogeneity has been widely reported in human cancers, but our knowledge of how this genetic diversity emerges over time remains limited. A central challenge in studying tumor evolution is the difficulty in collecting longitudinal samples from cancer patients. Consequently, most studies have inferred tumor evolution from single time-point samples, providing very indirect information. These data have led to several competing models of tumor evolution: linear, branching, neutral and punctuated. Each model makes different assumptions regarding the timing of mutations and selection of clones, and therefore has different implications for the diagnosis and therapeutic treatment of cancer patients. Furthermore, emerging evidence suggests that models may change during tumor progression or operate concurrently for different classes of mutations. Finally, we discuss data that supports the theory that most human tumors evolve from a single cell in the normal tissue. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

Biologically Relevant Heterogeneity: Metrics and Practical Insights

Heterogeneity is a fundamental property of biological systems at all scales that must be addressed in a wide range of biomedical applications, including basic biomedical research, drug discovery, diagnostics, and the implementation of precision medicine. There are a number of published approaches to characterizing heterogeneity in cells in vitro and in tissue sections. However, there are no generally accepted approaches for the detection and quantitation of heterogeneity that can be applied in a relatively high-throughput workflow. This review and perspective emphasizes the experimental methods that capture multiplexed cell-level data, as well as the need for standard metrics of the spatial, temporal, and population components of heterogeneity. A recommendation is made for the adoption of a set of three heterogeneity indices that can be implemented in any high-throughput workflow to optimize the decision-making process. In addition, a pairwise mutual information method is suggested as an approach to characterizing the spatial features of heterogeneity, especially in tissue-based imaging. Furthermore, metrics for temporal heterogeneity are in the early stages of development. Example studies indicate that the analysis of functional phenotypic heterogeneity can be exploited to guide decisions in the interpretation of biomedical experiments, drug discovery, diagnostics, and the design of optimal therapeutic strategies for individual patients.

Measuring cancer evolution from the genome

The temporal dynamics of cancer evolution remain elusive, because it is impractical to longitudinally observe cancers unperturbed by treatment. Consequently, our knowledge of how cancers grow largely derives from inferences made from a single point in time - the endpoint in the cancer's evolution, when it is removed from the body and studied in the laboratory. Fortuitously however, the cancer genome, by virtue of ongoing mutations that uniquely mark clonal lineages within the tumour, provides a rich, yet surreptitious, record of cancer development. In this review, we describe how a cancer's genome can be analysed to reveal the temporal history of mutation and selection, and discuss why both selective and neutral evolution feature prominently in carcinogenesis. We argue that selection in cancer can only be properly studied once we have some understanding of what the absence of selection looks like. We review the data describing punctuated evolution in cancer, and reason that punctuated phenotype evolution is consistent with both gradual and punctuated genome evolution. We conclude that, to map and predict evolutionary trajectories during carcinogenesis, it is critical to better understand the relationship between genotype change and phenotype change. Copyright © 2016 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Modeling cancer rearrangement landscapes

Cancer genome sequences contain footprints of somatic mutational processes, whose analysis in large tumor sequencing datasets has revealed novel mutational signatures, correlative features of variant topography, and complex events. Many of these analytic results have yet to reconciled with decades of mechanistic genome integrity research performed in controlled model systems. However, a new generation of genome-integrity experiments combining computational modeling, data analytics, and high-throughput sequencing are emerging to link mechanisms to patterns. Conversely, analytic studies evaluating quantitative footprints of specific genome integrity hypotheses will be critical in fitting naturally occurring mutational patterns to the predictions of a particular mechanistic model. Such quantitative and mechanistic studies will form the foundation of an emerging systems biology of genome integrity.

Comparing nodal versus bony metastatic spread using tumour phylogenies

The role of lymph node metastases in distant prostate cancer dissemination and lethality is ill defined. Patients with metastases restricted to lymph nodes have a better prognosis than those with distant metastatic spread, suggesting the possibility of distinct aetiologies. To explore this, we traced patterns of cancer dissemination using tumour phylogenies inferred from genome-wide copy-number profiling of 48 samples across 3 patients with lymph node metastatic disease and 3 patients with osseous metastatic disease. Our results show that metastatic cells in regional lymph nodes originate from evolutionary advanced extraprostatic tumour cells rather than less advanced central tumour cell populations. In contrast, osseous metastases do not exhibit such a constrained developmental lineage, arising from either intra or extraprostatic tumour cell populations, at early and late stages in the evolution of the primary. Collectively, this comparison suggests that lymph node metastases may not be an intermediate developmental step for distant osseous metastases, but rather represent a distinct metastatic lineage.

Intra-Tumor Genetic Heterogeneity in Wilms Tumor: Clonal Evolution and Clinical Implications

The evolution of pediatric solid tumors is poorly understood. There is conflicting evidence of intra-tumor genetic homogeneity vs. heterogeneity (ITGH) in a small number of studies in pediatric solid tumors. A number of copy number aberrations (CNA) are proposed as prognostic biomarkers to stratify patients, for example 1q+ in Wilms tumor (WT); current clinical trials use only one sample per tumor to profile this genetic biomarker. We multisampled 20 WT cases and assessed genome-wide allele-specific CNA and loss of heterozygosity, and inferred tumor evolution, using Illumina CytoSNP12v2.1 arrays, a custom analysis pipeline, and the MEDICC algorithm. We found remarkable diversity of ITGH and evolutionary trajectories in WT. 1q+ is heterogeneous in the majority of tumors with this change, with variable evolutionary timing. We estimate that at least three samples per tumor are needed to detect >95% of cases with 1q+. In contrast, somatic 11p15 LOH is uniformly an early event in WT development. We find evidence of two separate tumor origins in unilateral disease with divergent histology, and in bilateral WT. We also show subclonal changes related to differential response to chemotherapy. Rational trial design to include biomarkers in risk stratification requires tumor multisampling and reliable delineation of ITGH and tumor evolution.

OncoNEM: inferring tumor evolution from single-cell sequencing data

Single-cell sequencing promises a high-resolution view of genetic heterogeneity and clonal evolution in cancer. However, methods to infer tumor evolution from single-cell sequencing data lag behind methods developed for bulk-sequencing data. Here, we present OncoNEM, a probabilistic method for inferring intra-tumor evolutionary lineage trees from somatic single nucleotide variants of single cells. OncoNEM identifies homogeneous cellular subpopulations and infers their genotypes as well as a tree describing their evolutionary relationships. In simulation studies, we assess OncoNEM's robustness and benchmark its performance against competing methods. Finally, we show its applicability in case studies of muscle-invasive bladder cancer and essential thrombocythemia.

A metric and workflow for quality control in the analysis of heterogeneity in phenotypic profiles and screens

Heterogeneity is well recognized as a common property of cellular systems that impacts biomedical research and the development of therapeutics and diagnostics. Several studies have shown that analysis of heterogeneity: gives insight into mechanisms of action of perturbagens; can be used to predict optimal combination therapies; and can be applied to tumors where heterogeneity is believed to be associated with adaptation and resistance. Cytometry methods including high content screening (HCS), high throughput microscopy, flow cytometry, mass spec imaging and digital pathology capture cell level data for populations of cells. However it is often assumed that the population response is normally distributed and therefore that the average adequately describes the results. A deeper understanding of the results of the measurements and more effective comparison of perturbagen effects requires analysis that takes into account the distribution of the measurements, i.e. the heterogeneity. However, the reproducibility of heterogeneous data collected on different days, and in different plates/slides has not previously been evaluated. Here we show that conventional assay quality metrics alone are not adequate for quality control of the heterogeneity in the data. To address this need, we demonstrate the use of the Kolmogorov-Smirnov statistic as a metric for monitoring the reproducibility of heterogeneity in an SAR screen, describe a workflow for quality control in heterogeneity analysis. One major challenge in high throughput biology is the evaluation and interpretation of heterogeneity in thousands of samples, such as compounds in a cell-based screen. In this study we also demonstrate that three heterogeneity indices previously reported, capture the shapes of the distributions and provide a means to filter and browse big data sets of cellular distributions in order to compare and identify distributions of interest. These metrics and methods are presented as a workflow for analysis of heterogeneity in large scale biology projects.

Inferring the Origin of Metastases from Cancer Phylogenies

Determining the evolutionary history of metastases is a key problem in cancer biology. Several recent studies have presented inferences regarding the origin of metastases based on phylogenies of cancer lineages. Many of these studies have concluded that the observed monophyly of metastatic subclones favored metastasis-to-metastasis spread ("a metastatic cascade" rather than parallel metastases from the primary tumor). In this article, we argue that identifying a monophyletic clade of metastatic subclones does not provide sufficient evidence to unequivocally establish a history of metastatic cascades. In the absence of a complete phylogeny of the subclones within the primary tumor, a scenario of parallel metastatic events from the primary tumor is an equally plausible interpretation. Future phylogenetic studies on the origin of metastases should obtain a complete phylogeny of subclones within the primary tumor. This complete phylogeny may be obtainable by ultra-deep sequencing and phasing of large sections or by targeted sequencing of many small, spatially heterogeneous sections, followed by phylogenetic reconstruction using well-established molecular evolutionary models. In addition to resolving the evolutionary history of metastases, a complete phylogeny of subclones within the primary tumor facilitates the identification of driver mutations by application of phylogeny-based tests of natural selection.

Subclonal Genomic Architectures of Primary and Metastatic Colorectal Cancer Based on Intratumoral Genetic Heterogeneity

PURPOSE

The intratumoral heterogeneity (ITH) and the evolution of genomic architectures associated with the development of distant metastases are not well understood in colorectal cancers.

EXPERIMENTAL DESIGN

We performed multiregion biopsies of primary and liver metastatic regions from five colorectal cancers with whole-exome sequencing and copy number profiling.

RESULTS

In addition to a substantial level of genetic ITH, multiregion genetic profiling identifies the subclonal mutational architecture, leading to the region-based or spatial categorization of somatic mutations and the inference of intratumoral evolutionary history of cancers. The universal mutations (those observed in all the regional biopsies) are enriched in known cancer genes such as APC and TP53 with distinct mutational spectra compared with biopsy- or region-specific mutations, suggesting that major operative mutational mechanisms and their selective pressures are not constant across the metastatic progression. The phylogenies inferred from genomic data show branching evolutionary patterns where some primary biopsies are often segregated with metastastic lesions. Our analyses also revealed that copy number changes such as the chromosomal gains of c-MYC and chromothripsis can be region specific and the potential source of genetic ITH.

CONCLUSIONS

Our data show that the genetic ITH is prevalent in colorectal cancer serving as a potential driving force to generate metastasis-initiating clones and also as a means to infer the intratumoral evolutionary history of cancers. The paucity of recurrent metastasis-clonal events suggests that colorectal cancer distant metastases may not follow a uniform course of genomic evolution, which should be considered in the genetic diagnosis and the selection of therapeutic targets for the advanced colorectal cancer.

Turning the headlights on novel cancer biomarkers: Inspection of mechanics underlying intratumor heterogeneity

Although the existence of intratumoral heterogeneity (ITH) in the expression of common biomarkers has been described by pathologists since the late 1890s, we have only recently begun to fathom the staggering extent and near ubiquity of this phenomenon. From the tumor's perspective, ITH provides a stabilizing diversity that allows for the evolution of aggressive cancer phenotypes. As the weight of the evidence correlating ITH to poor prognosis burgeons, it has become increasingly important to determine the mechanisms by which a tumor acquires ITH, find clinically-adaptable means to quantify ITH and design strategies to deal with the numerous profound clinical ramifications that ITH forces upon us. Elucidation of the drivers of ITH could enable development of novel biomarkers whose interrogation might permit quantitative evaluation of the ITH inherent in a tumor in order to predict the poor prognosis risk associated with that tumor. This review proposes centrosome amplification (CA), aided and abetted by centrosome clustering mechanisms, as a critical driver of chromosomal instability (CIN) that makes a key contribution to ITH generation. Herein we also evaluate how a tumor's inherent mitotic propensity, which reflects the cell cycling kinetics within the tumor's proliferative cells, functions as the indispensable engine underpinning CIN, and determines the rate of CIN. We thus expound how the forces of centrosome amplification and mitotic propensity collaborate to sculpt the genetic landscape of a tumor and spawn extensive subclonal diversity. As such, centrosome amplification and mitotic propensity profiles could serve as clinically facile and powerful prognostic biomarkers that would enable more accurate risk segmentation of patients and design of individualized therapies.

Uncovering the genomic heterogeneity of multifocal breast cancer

Multifocal breast cancer (MFBC), defined as multiple synchronous unilateral lesions of invasive breast cancer, is relatively frequent and has been associated with more aggressive features than unifocal cancer. Here, we aimed to investigate the genomic heterogeneity between MFBC lesions sharing similar histopathological parameters. Characterization of different lesions from 36 patients with ductal MFBC involved the identification of non‐silent coding mutations in 360 protein‐coding genes (171 tumour and 36 matched normal samples). We selected only patients with lesions presenting the same grade, ER, and HER2 status. Mutations were classified as ‘oncogenic’ in the case of recurrent substitutions reported in COSMIC or truncating mutations affecting tumour suppressor genes. All mutations identified in a given patient were further interrogated in all samples from that patient through deep resequencing using an orthogonal platform. Whole‐genome rearrangement screen was further conducted in 8/36 patients. Twenty‐four patients (67%) had substitutions/indels shared by all their lesions, of which 11 carried the same mutations in all lesions, and 13 had lesions with both common and private mutations. Three‐quarters of those 24 patients shared oncogenic variants. The remaining 12 patients (33%) did not share any substitution/indels, with inter‐lesion heterogeneity observed for oncogenic mutation(s) in genes such as PIK3CA, TP53, GATA3, and PTEN. Genomically heterogeneous lesions tended to be further apart in the mammary gland than homogeneous lesions. Genome‐wide analyses of a limited number of patients identified a common somatic background in all studied MFBCs, including those with no mutation in common between the lesions. To conclude, as the number of molecular targeted therapies increases and trials driven by genomic screening are ongoing, our findings highlight the presence of genomic inter‐lesion heterogeneity in one‐third, despite similar pathological features. This implies that deeper molecular characterization of all MFBC lesions is warranted for the adequate management of those cancers. © 2015 The Authors. Pathological Society of Great Britain and Ireland.

BitPhylogeny: a probabilistic framework for reconstructing intra-tumor phylogenies

Cancer has long been understood as a somatic evolutionary process, but many details of tumor progression remain elusive. Here, we present BitPhylogenyBitPhylogeny, a probabilistic framework to reconstruct intra-tumor evolutionary pathways. Using a full Bayesian approach, we jointly estimate the number and composition of clones in the sample as well as the most likely tree connecting them. We validate our approach in the controlled setting of a simulation study and compare it against several competing methods. In two case studies, we demonstrate how BitPhylogeny BitPhylogeny reconstructs tumor phylogenies from methylation patterns in colon cancer and from single-cell exomes in myeloproliferative neoplasm.

A Big Bang model of human colorectal tumor growth

What happens in the early, still undetectable human malignancy is unknown because direct observations are impractical. Here we present and validate a “Big Bang” model, whereby tumors grow predominantly as a single expansion producing numerous intermixed sub-clones that are not subject to stringent selection, and where both public (clonal) and most detectable private (subclonal) alterations arise early during growth. Genomic profiling of 349 individual glands from 15 colorectal tumors revealed the absence of selective sweeps, uniformly high intra-tumor heterogeneity (ITH), and sub-clone mixing in distant regions, as postulated by our model. We also verified the prediction that most detectable ITH originates from early private alterations, and not from later clonal expansions, thus exposing the profile of the primordial tumor. Moreover, some tumors appear born-to-be-bad, with sub-clone mixing indicative of early malignant potential. This new model provides a quantitative framework to interpret tumor growth dynamics and the origins of ITH with significant clinical implications.

Open access to large scale datasets is needed to translate knowledge of cancer heterogeneity into better patient outcomes

In this guest editorial, Andrew Beck discusses the importance of open access to big data for translating knowledge of cancer heterogeneity into better outcomes for cancer patients.

Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic analysis

BACKGROUND

The major clinical challenge in the treatment of high-grade serous ovarian cancer (HGSOC) is the development of progressive resistance to platinum-based chemotherapy. The objective of this study was to determine whether intra-tumour genetic heterogeneity resulting from clonal evolution and the emergence of subclonal tumour populations in HGSOC was associated with the development of resistant disease.

METHODS AND FINDINGS

Evolutionary inference and phylogenetic quantification of heterogeneity was performed using the MEDICC algorithm on high-resolution whole genome copy number profiles and selected genome-wide sequencing of 135 spatially and temporally separated samples from 14 patients with HGSOC who received platinum-based chemotherapy. Samples were obtained from the clinical CTCR-OV03/04 studies, and patients were enrolled between 20 July 2007 and 22 October 2009. Median follow-up of the cohort was 31 mo (interquartile range 22-46 mo), censored after 26 October 2013. Outcome measures were overall survival (OS) and progression-free survival (PFS). There were marked differences in the degree of clonal expansion (CE) between patients (median 0.74, interquartile range 0.66-1.15), and dichotimization by median CE showed worse survival in CE-high cases (PFS 12.7 versus 10.1 mo, p = 0.009; OS 42.6 versus 23.5 mo, p = 0.003). Bootstrap analysis with resampling showed that the 95% confidence intervals for the hazard ratios for PFS and OS in the CE-high group were greater than 1.0. These data support a relationship between heterogeneity and survival but do not precisely determine its effect size. Relapsed tissue was available for two patients in the CE-high group, and phylogenetic analysis showed that the prevalent clonal population at clinical recurrence arose from early divergence events. A subclonal population marked by a NF1 deletion showed a progressive increase in tumour allele fraction during chemotherapy.

CONCLUSIONS

This study demonstrates that quantitative measures of intra-tumour heterogeneity may have predictive value for survival after chemotherapy treatment in HGSOC. Subclonal tumour populations are present in pre-treatment biopsies in HGSOC and can undergo expansion during chemotherapy, causing clinical relapse.