Integrative Inference of Subclonal Tumour Evolution from Single-Cell and Bulk Sequencing Data

Understanding the clonal architecture and evolutionary history of a tumour poses one of the key challenges to overcome treatment failure due to resistant cell populations. Previously, studies on subclonal tumour evolution have been primarily based on bulk sequencing and in some recent cases on single-cell sequencing data. Either data type alone has shortcomings with regard to this task, but methods integrating both data types have been lacking. Here, we present B-SCITE, the first computational approach that infers tumour phylogenies from combined single-cell and bulk sequencing data. Using a comprehensive set of simulated data, we show that B-SCITE systematically outperforms existing methods with respect to tree reconstruction accuracy and subclone identification. B-SCITE provides high-fidelity reconstructions even with a modest number of single cells and in cases where bulk allele frequencies are affected by copy number changes. On real tumour data, B-SCITE generated mutation histories show high concordance with expert generated trees

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

Evaluation of simulation methods for tumor subclonal reconstruction

Most neoplastic tumors originate from a single cell, and their evolution can be genetically traced through lineages characterized by common alterations such as small somatic mutations (SSMs), copy number alterations (CNAs), structural variants (SVs), and aneuploidies. Due to the complexity of these alterations in most tumors and the errors introduced by sequencing protocols and calling algorithms, tumor subclonal reconstruction algorithms are necessary to recapitulate the DNA sequence composition and tumor evolution in silico. With a growing number of these algorithms available, there is a pressing need for consistent and comprehensive benchmarking, which relies on realistic tumor sequencing generated by simulation tools. Here, we examine the current simulation methods, identifying their strengths and weaknesses, and provide recommendations for their improvement. Our review also explores potential new directions for research in this area. This work aims to serve as a resource for understanding and enhancing tumor genomic simulations, contributing to the advancement of the field

Charting genomic heterogeneity in tumours : from bulk to single cell

Tumours do not consist of a single homogeneous population but are complex heterogeneous systems that contain billions of ever-evolving cells with no two tumours being the same. Tumour heterogeneity is present at three levels, 1) inter-patient heterogeneity; 2) intra-patient heterogeneity; and 3) intra-tumour heterogeneity (ITH). Understanding all levels of heterogeneity is crucial for patient prognosis and treatment choice. To this end, we aimed to improve our understanding of all three levels of tumour heterogeneity. In paper I we investigated the prevalence, type, length, and genomic distribution of 853.218 somatic copy number alterations (SCNAs) across 20.249 tumours belonging to 32 cancer types. Based on the 1) number of SCNAs; 2) percentage of the genome altered; and 3) average SCNA size, we found high levels of inter-patient heterogeneity, both between and within cancer types. We found that specific chromosomes were preferentially lost or gained depending on cancer type. Lastly, we detected co-alterations of key oncogenes and TSGs. Taken together, we provided a comprehensive analysis on SCNAs across many cancer types as a valuable resource for the community. In paper II we sought to elucidate intra-patient heterogeneity in non-small cell lung cancer (NSCLC) and their matched brain metastasis (BM). We performed shallow wholegenome sequencing (WGS) on 51 primary NSCLC and matched BM, whole exome sequencing on 40 of the pairs, multi-region sequencing of 15 BMs, and shallow WGS on an additional cohort of 115 BMs. We showed that there is significant intra-patient heterogeneity at the SCNA level, with BM samples showing, on average, more SCNAs compared to their matched NSCLC. In contrast, multi-region sequencing of 15 BMs did not show significant ITH at the level of SCNAs. Finally, we identified putative metastatic driver SCNAs and singlenucleotide variants in key tumour suppressor genes (TSGs) and oncogenes. In paper III we aimed to assess the level of ITH in early localized prostate cancer. We performed organ-wide, multi-region, single-cell DNA sequencing on two prostate midsections. We found transient chromosomal instability (CIN) both in tumour and normal prostate tissue, evidenced by a large number of cells with unique chromosomal (arm) losses and or gains. Furthermore, we found three distinct groups of cells within the prostate: 1) diploid cells; 2) pseudo-diploid cells; and 3) monster cells. We observed an enrichment of diploid cells in normal regions and pseudo-diploid cells in tumour-rich regions, while monster cells were equally distributed over the entire prostate, again suggesting that there were elevated CIN levels across the prostate. Lastly, we detected highly localized subclones that were exclusive to tumour-rich regions and harboured deletions in TSGs that are known to be frequently deleted in prostate cancer. Taken together, with this thesis, I have contributed to advance the understanding of inter-patient, intra-patient, and intra-tumour heterogeneity

Methods and practice of detecting selection in human cancers

Cancer development and progression is an evolutionary process, understanding these evolutionary dynamics is important for treatment and diagnosis as how a cancer evolves determines its future prognosis. This thesis focuses on elucidating selective evolutionary pressures in cancers and somatic tissues using population genetics models and cancer genomics data. First a model for the expected diversity in the absence of selection was developed. This neutral model of evolution predicts that under neutrality the frequency of subclonal mutations is expected to follow a power law distribution. Surprisingly more than 30% of cancer across multiple cohorts fitted this model. The next part of the thesis develops models to explore the effects of selection given these should be observable as deviations from the neutral prediction. For this I developed two approaches. The first approach investigated selection at the level of individual samples and showed that a characteristic pattern of clusters of mutations is observed in deep sequencing experiments. Using a mathematical model, information encoded within these clusters can be used to measure the relative fitness of subclones and the time they emerge during tumour evolution. With this I observed strikingly high fitness advantages for subclones of above 20%. The second approach enables measuring recurrent patterns of selection in cohorts of sequenced cancers using dN/dS, the ratio of non-synonymous to synonymous mutations, a method originally developed for molecular species evolution. This approach demonstrates how selection coefficients can be extracted by combining measurements of dN/dS with the size of mutational lineages. With this approach selection coefficients were again observed to be strikingly high. Finally I looked at population dynamics in normal colonic tissue given that many mutations accumulate in physiologically normal tissue. I found that the current view of stem cell dynamics was unable to explain sequencing data from individual colonic crypts. Some new models were proposed that introduce a longer time scale evolution that suppresses the accumulation of mutations which appear consistent with the data

Insights into the metastatic cascade through research autopsies

Metastasis is a complex process and the leading cause of cancer-related death globally. Recent studies have demonstrated that genomic sequencing data from paired primary and metastatic tumours can be used to trace the evolutionary origins of cells responsible for metastasis. This approach has yielded new insights into the genomic alterations that engender metastatic potential, and the mechanisms by which cancer spreads. Given that the reliability of these approaches is contingent upon how representative the samples are of primary and metastatic tumour heterogeneity, we review insights from studies that have reconstructed the evolution of metastasis within the context of their cohorts and designs. We discuss the role of research autopsies in achieving the comprehensive sampling necessary to advance the current understanding of metastasis

PhyliCS: a Python library to explore scCNA data and quantify spatial tumor heterogeneity

Background: Tumors are composed by a number of cancer cell subpopulations (subclones), characterized by a distinguishable set of mutations. This phenomenon, known as intra-tumor heterogeneity (ITH), may be studied using Copy Number Aberrations (CNAs). Nowadays ITH can be assessed at the highest possible resolution using single-cell DNA (scDNA) sequencing technology. Additionally, single-cell CNA (scCNA) profiles from multiple samples of the same tumor can in principle be exploited to study the spatial distribution of subclones within a tumor mass. However, since the technology required to generate large scDNA sequencing datasets is relatively recent, dedicated analytical approaches are still lacking. Results: We present PhyliCS, the first tool which exploits scCNA data from multiple samples from the same tumor to estimate whether the different clones of a tumor are well mixed or spatially separated. Starting from the CNA data produced with third party instruments, it computes a score, the Spatial Heterogeneity score, aimed at distinguishing spatially intermixed cell populations from spatially segregated ones. Additionally, it provides functionalities to facilitate scDNA analysis, such as feature selection and dimensionality reduction methods, visualization tools and a flexible clustering module. Conclusions: PhyliCS represents a valuable instrument to explore the extent of spatial heterogeneity in multi-regional tumour sampling, exploiting the potential of scCNA data

Refphase: Multi-sample phasing reveals haplotype-specific copy number heterogeneity

Most computational methods that infer somatic copy number alterations (SCNAs) from bulk sequencing of DNA analyse tumour samples individually. However, the sequencing of multiple tumour samples from a patient’s disease is an increasingly common practice. We introduce Refphase, an algorithm that leverages this multi-sampling approach to infer haplotype-specific copy numbers through multi-sample phasing. We demonstrate Refphase’s ability to infer haplotype-specific SCNAs and characterise their intra-tumour heterogeneity, to uncover previously undetected allelic imbalance in low purity samples, and to identify parallel evolution in the context of whole genome doubling in a pan-cancer cohort of 336 samples from 99 tumours

Genetic and non-genetic clonal diversity in cancer evolution

The observation and analysis of intra-tumour heterogeneity (ITH), particularly in genomic studies, has advanced our understanding of the evolutionary forces that shape cancer growth and development. However, only a subset of the variation observed in a single tumour will have an impact on cancer evolution, highlighting the need to distinguish between functional and non-functional ITH. Emerging studies highlight a role for the cancer epigenome, transcriptome and immune microenvironment in functional ITH. Here, we consider the importance of both genetic and non-genetic ITH and their role in tumour evolution, and present the rationale for a broad research focus beyond the cancer genome. Systems-biology analytical approaches will be necessary to outline the scale and importance of functional ITH. By allowing a deeper understanding of tumour evolution this will, in time, encourage development of novel therapies and improve outcomes for patients