Keiran Raine

Somatic CALR Mutations in Myeloproliferative Neoplasms with Nonmutated JAK2

BACKGROUNDnSomatic mutations in the Janus kinase 2 gene (JAK2) occur in many myeloproliferative neoplasms, but the molecular pathogenesis of myeloproliferative neoplasms with nonmutated JAK2 is obscure, and the diagnosis of these neoplasms remains a challenge.nnnMETHODSnWe performed exome sequencing of samples obtained from 151 patients with myeloproliferative neoplasms. The mutation status of the gene encoding calreticulin (CALR) was assessed in an additional 1345 hematologic cancers, 1517 other cancers, and 550 controls. We established phylogenetic trees using hematopoietic colonies. We assessed calreticulin subcellular localization using immunofluorescence and flow cytometry.nnnRESULTSnExome sequencing identified 1498 mutations in 151 patients, with medians of 6.5, 6.5, and 13.0 mutations per patient in samples of polycythemia vera, essential thrombocythemia, and myelofibrosis, respectively. Somatic CALR mutations were found in 70 to 84% of samples of myeloproliferative neoplasms with nonmutated JAK2, in 8% of myelodysplasia samples, in occasional samples of other myeloid cancers, and in none of the other cancers. A total of 148 CALR mutations were identified with 19 distinct variants. Mutations were located in exon 9 and generated a +1 base-pair frameshift, which would result in a mutant protein with a novel C-terminal. Mutant calreticulin was observed in the endoplasmic reticulum without increased cell-surface or Golgi accumulation. Patients with myeloproliferative neoplasms carrying CALR mutations presented with higher platelet counts and lower hemoglobin levels than patients with mutated JAK2. Mutation of CALR was detected in hematopoietic stem and progenitor cells. Clonal analyses showed CALR mutations in the earliest phylogenetic node, a finding consistent with its role as an initiating mutation in some patients.nnnCONCLUSIONSnSomatic mutations in the endoplasmic reticulum chaperone CALR were found in a majority of patients with myeloproliferative neoplasms with nonmutated JAK2. (Funded by the Kay Kendall Leukaemia Fund and others.).

Somatic SF3B1 mutation in myelodysplasia with ring sideroblasts.

BACKGROUNDnMyelodysplastic syndromes are a diverse and common group of chronic hematologic cancers. The identification of new genetic lesions could facilitate new diagnostic and therapeutic strategies.nnnMETHODSnWe used massively parallel sequencing technology to identify somatically acquired point mutations across all protein-coding exons in the genome in 9 patients with low-grade myelodysplasia. Targeted resequencing of the gene encoding RNA splicing factor 3B, subunit 1 (SF3B1), was also performed in a cohort of 2087 patients with myeloid or other cancers.nnnRESULTSnWe identified 64 point mutations in the 9 patients. Recurrent somatically acquired mutations were identified in SF3B1. Follow-up revealed SF3B1 mutations in 72 of 354 patients (20%) with myelodysplastic syndromes, with particularly high frequency among patients whose disease was characterized by ring sideroblasts (53 of 82 [65%]). The gene was also mutated in 1 to 5% of patients with a variety of other tumor types. The observed mutations were less deleterious than was expected on the basis of chance, suggesting that the mutated protein retains structural integrity with altered function. SF3B1 mutations were associated with down-regulation of key gene networks, including core mitochondrial pathways. Clinically, patients with SF3B1 mutations had fewer cytopenias and longer event-free survival than patients without SF3B1 mutations.nnnCONCLUSIONSnMutations in SF3B1 implicate abnormalities of messenger RNA splicing in the pathogenesis of myelodysplastic syndromes. (Funded by the Wellcome Trust and others.).

Universal Patterns of Selection in Cancer and Somatic Tissues

Summary Cancer develops as a result of somatic mutation and clonal selection, but quantitative measures of selection in cancer evolution are lacking. We adapted methods from molecular evolution and applied them to 7,664 tumors across 29 cancer types. Unlike species evolution, positive selection outweighs negative selection during cancer development. On average, <1 coding base substitution/tumor is lost through negative selection, with purifying selection almost absent outside homozygous loss of essential genes. This allows exome-wide enumeration of all driver coding mutations, including outside known cancer genes. On average, tumors carry ∼4 coding substitutions under positive selection, ranging from <1/tumor in thyroid and testicular cancers to >10/tumor in endometrial and colorectal cancers. Half of driver substitutions occur in yet-to-be-discovered cancer genes. With increasing mutation burden, numbers of driver mutations increase, but not linearly. We systematically catalog cancer genes and show that genes vary extensively in what proportion of mutations are drivers versus passengers.

Mutational signatures of ionizing radiation in second malignancies

Ionizing radiation is a potent carcinogen, inducing cancer through DNA damage. The signatures of mutations arising in human tissues following in vivo exposure to ionizing radiation have not been documented. Here, we searched for signatures of ionizing radiation in 12 radiation-associated second malignancies of different tumour types. Two signatures of somatic mutation characterize ionizing radiation exposure irrespective of tumour type. Compared with 319 radiation-naive tumours, radiation-associated tumours carry a median extra 201 deletions genome-wide, sized 1–100 base pairs often with microhomology at the junction. Unlike deletions of radiation-naive tumours, these show no variation in density across the genome or correlation with sequence context, replication timing or chromatin structure. Furthermore, we observe a significant increase in balanced inversions in radiation-associated tumours. Both small deletions and inversions generate driver mutations. Thus, ionizing radiation generates distinctive mutational signatures that explain its carcinogenic potential.

cgpCaVEManWrapper: Simple Execution of CaVEMan in Order to Detect Somatic Single Nucleotide Variants in NGS Data.

CaVEMan is an expectation maximization–based somatic substitution‐detection algorithm that is written in C. The algorithm analyzes sequence data from a test sample, such as a tumor relative to a reference normal sample from the same patient and the reference genome. It performs a comparative analysis of the tumor and normal sample to derive a probabilistic estimate for putative somatic substitutions. When combined with a set of validated post‐hoc filters, CaVEMan generates a set of somatic substitution calls with high recall and positive predictive value. Here we provide instructions for using a wrapper script called cgpCaVEManWrapper, which runs the CaVEMan algorithm and additional downstream post‐hoc filters. We describe both a simple one‐shot run of cgpCaVEManWrapper and a more in‐depth implementation suited to large‐scale compute farms.

cgpPindel: Identifying Somatically Acquired Insertion and Deletion Events from Paired End Sequencing

cgpPindel is a modified version of Pindel that is optimized for detecting somatic insertions and deletions (indels) in cancer genomes and other samples compared to a reference control. Post‐hoc filters remove false positive calls, resulting in a high‐quality dataset for downstream analysis. This unit provides concise instructions for both a simple ‘one‐shot’ execution of cgpPindel and a more detailed approach suitable for large‐scale compute farms.

ascatNgs: Identifying Somatically Acquired Copy‐Number Alterations from Whole‐Genome Sequencing Data

We have developed ascatNgs to aid researchers in carrying out Allele‐Specific Copy number Analysis of Tumours (ASCAT). ASCAT is capable of detecting DNA copy number changes affecting a tumor genome when comparing to a matched normal sample. Additionally, the algorithm estimates the amount of tumor DNA in the sample, known as Aberrant Cell Fraction (ACF). ASCAT itself is an R‐package which requires the generation of many file types. Here, we present a suite of tools to help handle this for the user. Our code is available on our GitHub site (https://github.com/cancerit). This unit describes both ‘one‐shot’ execution and approaches more suitable for large‐scale compute farms.

Analysis of the genomic landscape of multiple myeloma highlights novel prognostic markers and disease subgroups

In multiple myeloma, next generation sequencing (NGS) has expanded our knowledge of genomic lesions, and highlighted a dynamic and heterogeneous composition of the tumor. Here, we used NGS to characterize the genomic landscape of 418 multiple myeloma cases at diagnosis and correlate this with prognosis and classification. Translocations and copy number changes (CNAs) had a preponderant contribution over gene mutations in defining the genotype and prognosis of each case. Known and novel independent prognostic markers were identified in our cohort of proteasome inhibitor and IMiD-treated patients with long follow-up, including events with context-specific prognostic value, such as deletions of the PRDM1 gene. Taking advantage of the comprehensive genomic annotation of each case, we used innovative statistical approaches to identify potential novel myeloma subgroups. We observed clusters of patients stratified based on the overall number of mutations and number/type of CNAs, with distinct effects on survival, suggesting that extended genotype of multiple myeloma at diagnosis may lead to improved disease classification and prognostication.Leukemia accepted article preview online, 06 December 2017. doi:10.1038/leu.2017.344.

Framework For Quality Assessment Of Whole Genome, Cancer Sequences

Working with cancer whole genomes sequenced over a period of many years in different sequencing centres requires a validated framework to compare the quality of these sequences. The Pan-Cancer Analysis of Whole Genomes (PCAWG) of the International Cancer Genome Consortium (ICGC), a project a cohort of over 2800 donors provided us with the challenge of assessing the quality of the genome sequences. A non-redundant set of five quality control (QC) measurements were assembled and used to establish a star rating system. These QC measures reflect known differences in sequencing protocol and provide a guide to downstream analyses of these whole genome sequences. The resulting QC measures also allowed for exclusion samples of poor quality, providing researchers within PCAWG, and when the data is released for other researchers, a good idea of the sequencing quality. For a researcher wishing to apply the QC measures for their data we provide a Docker Container of the software used to calculate them. We believe that this is an effective framework of quality measures for whole genome, cancer sequences, which will be a useful addition to analytical pipelines, as it has to the PCAWG project.

Abstract 93: COSMIC: The catalogue of somatic mutations in cancer receives full genome variant annotations

Proceedings: AACR 101st Annual Meeting 2010‐‐ Apr 17‐21, 2010; Washington, DCnnCOSMIC, the Catalogue Of Somatic Mutations In Cancer (http://www.sanger.ac.uk/cosmic), is one of the most comprehensive web systems describing the impact of somatic mutations in human cancer. The information is sourced directly from the published scientific literature and the Cancer Genome Projects high throughput sequencing laboratories at the Wellcome Trust Sanger Institute UK (CGP), allowing a high data quality to be maintained. The v44 release (Nov. 2009) contained the curation of 8336 papers covering 71 known cancer genes by point mutation and 30 by gene fusion. The mutation data for all these genes is being maintained up-to-date and new genes are regularly added to this list. Additionally, 4871 genes and 2764 samples have been scrutinized by the CGP laboratories. As cancer genetics moves toward the analysis of whole genomes, COSMIC is being updated to handle and display these data and increase the functionality of the website.nnWith the rapid development of sequencing technologies, the quantity of data that can be put into COSMIC is increasing fast. We have extended our curation process to include large exome-wide candidate gene screens, recently including the first such screen from Sjoblom et al (2006), and the first TCGA screen (2008) with other large scale screening datasets being curated. In addition, whole-genome screens are becoming available with many non-coding variations. From the CGP laboratories, we already have two low-coverage genome rearrangement screens in COSMIC and this will rapidly expand, beginning with the imminent release of rearrangement scans in a set of 24 breast tumours. High-coverage genome analyses are also beginning to become available; with full genome coverage, the majority of somatic mutations in a tumour can be identified and COSMIC is being prepared to receive and display this data.nnWith the inclusion of these new data, the COSMIC website has evolved to allow more easy access to required data. New specialization filters are in place to provide methods to search for subsets of COSMIC data in the usual user-friendly web pages. More extensive investigations can be performed using the new COSMIC Biomart, an industry-standard data mining system allowing access through web pages or programmatic interfaces.nnAs the inclusion of new genomic data accelerates and the systems through which it can be accessed evolve, COSMIC is well placed to remain a central resource in human cancer genetics.nnNote: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend.nnCitation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 93.