Deterministic Evolutionary Trajectories Influence Primary Tumor Growth: TRACERx Renal

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Summary

The evolutionary features of clear-cell renal cell carcinoma (ccRCC) have not been systematically studied to date. We analyzed 1,206 primary tumor regions from 101 patients recruited into the multi-center prospective study, TRACERx Renal. We observe up to 30 driver events per tumor and show that subclonal diversification is associated with known prognostic parameters. By resolving the patterns of driver event ordering, co-occurrence, and mutual exclusivity at clone level, we show the deterministic nature of clonal evolution. ccRCC can be grouped into seven evolutionary subtypes, ranging from tumors characterized by early fixation of multiple mutational and copy number drivers and rapid metastases to highly branched tumors with >10 subclonal drivers and extensive parallel evolution associated with attenuated progression. We identify genetic diversity and chromosomal complexity as determinants of patient outcome. Our insights reconcile the variable clinical behavior of ccRCC and suggest evolutionary potential as a biomarker for both intervention and surveillance.

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Highlights

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ccRCC evolutionary subtypes correlate with clinical phenotypes
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Genetic diversity and chromosome complexity contribute to patient outcomes
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Early fixation of multiple driver events leads to rapid growth and metastases
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Subclonal diversification is linked with slower growth and attenuated metastases

Abstract

A multi-center prospective study on 101 patients with clear-cell renal cell carcinoma resolves the evolutionary features and subtypes underpinning the diverse clinical phenotypes of the disease and suggests these features as potential biomarkers for guiding intervention and surveillance.

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Most cited references 69

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Fast and accurate short read alignment with Burrows–Wheeler transform

Heng Li, Richard Durbin (2009)

Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk

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BEDTools: a flexible suite of utilities for comparing genomic features

Aaron Quinlan, Ira Hall (2010)

Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.

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ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

Kai Wang, Mingyao Li, Hakon Hakonarson (2010)

High-throughput sequencing platforms are generating massive amounts of genetic variation data for diverse genomes, but it remains a challenge to pinpoint a small subset of functionally important variants. To fill these unmet needs, we developed the ANNOVAR tool to annotate single nucleotide variants (SNVs) and insertions/deletions, such as examining their functional consequence on genes, inferring cytogenetic bands, reporting functional importance scores, finding variants in conserved regions, or identifying variants reported in the 1000 Genomes Project and dbSNP. ANNOVAR can utilize annotation databases from the UCSC Genome Browser or any annotation data set conforming to Generic Feature Format version 3 (GFF3). We also illustrate a ‘variants reduction’ protocol on 4.7 million SNVs and indels from a human genome, including two causal mutations for Miller syndrome, a rare recessive disease. Through a stepwise procedure, we excluded variants that are unlikely to be causal, and identified 20 candidate genes including the causal gene. Using a desktop computer, ANNOVAR requires ∼4 min to perform gene-based annotation and ∼15 min to perform variants reduction on 4.7 million variants, making it practical to handle hundreds of human genomes in a day. ANNOVAR is freely available at http://www.openbioinformatics.org/annovar/ .

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Author and article information

Contributors

James Larkin

Charles Swanton

Journal

Journal ID (nlm-ta): Cell

Journal ID (iso-abbrev): Cell

Title: Cell

Publisher: Cell Press

ISSN (Print): 0092-8674

ISSN (Electronic): 1097-4172

Publication date PMC-release: 19 April 2018

Publication date (Print): 19 April 2018

Volume: 173

Issue: 3

Pages: 595-610.e11

Affiliations

[1 ]Translational Cancer Therapeutics Laboratory, the Francis Crick Institute, London NW1 1AT, UK

[2 ]Renal and Skin Units, the Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK

[3 ]Department of Bioinformatics and Biostatistics, the Francis Crick Institute, London NW1 1AT, UK

[4 ]Urology Centre, Guy’s and St. Thomas’ NHS Foundation Trust, London SE1 9RT, UK

[5 ]Department of Pathology, Cruces University Hospital, Biocruces Institute, University of the Basque Country, Barakaldo, Spain

[6 ]Department of Urology, the Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK

[7 ]Department of Pathology, the Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK

[8 ]Department of Pathology, Guy’s and St. Thomas’ NHS Foundation Trust, London SE1 7EH, UK

[9 ]Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK

[10 ]Department of Surgery, Addenbrooke’s Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK

[11 ]Department of Medical Oncology, Guy’s and St. Thomas’ NHS Foundation Trust, London SE1 9RT, UK

[12 ]Experimental Histopathology Laboratory, the Francis Crick Institute, London NW1 1AT, UK

[13 ]Department of Scientific Computing, the Francis Crick Institute, London NW1 1AT, UK

[14 ]Advanced Sequencing Facility, the Francis Crick Institute, London NW1 1AT, UK

[15 ]Department of Radiology, the Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK

[16 ]Berlin Institute for Medical Systems Biology, Max Delbrueck Center for Molecular Medicine, Berlin, Germany

[17 ]Bill Lyons Informatics Centre, UCL Cancer Institute, University College London, London WC1E 6DD, UK

[18 ]Bioinformatics and Computational Biology Laboratory, the Francis Crick Institute, London NW1 1AT, UK

[19 ]Cancer Research UK Lung Cancer Centre of Excellence London, University College London Cancer Institute, London WC1E 6DD, UK

[20 ]Department of Physics of Complex Systems, ELTE Eötvös Loránd University, Budapest, Hungary

[21 ]Department of Bio and Health Informatics, Technical University of Denmark, Kgs Lyngby 2800, Denmark

[22 ]Computational Health Informatics Program, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA

[23 ]Cancer Genomics Laboratory, the Francis Crick Institute, London NW1 1AT, UK

[24 ]Department of Human Genetics, University of Leuven, 3000 Leuven, Belgium

[25 ]Department of Medical Oncology, University College London Hospitals, London NW1 2BU, UK

Author notes

[∗ ]Corresponding author james.larkin@ 123456rmh.nhs.uk

[∗∗ ]Corresponding author charles.swanton@ 123456crick.ac.uk

[26]

These authors contributed equally

[27]

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Article

Publisher ID: S0092-8674(18)30375-1

DOI: 10.1016/j.cell.2018.03.043

PMC ID: 5938372

PubMed ID: 29656894

SO-VID: 6f4ebd2c-0501-420f-9a8a-18951b877e83

License:

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

History

Date received : 15 September 2017

Date revision received : 12 January 2018

Date accepted : 19 March 2018

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UCL: UN SDG 03 Good Health and Well-Being

Most cited references 69

Fast and accurate short read alignment with Burrows–Wheeler transform

BEDTools: a flexible suite of utilities for comparing genomic features

ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

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Cited by 256