A renewed model of pancreatic cancer evolution based on genomic rearrangement patterns.

Pancreatic cancer, a highly aggressive tumour type with uniformly poor prognosis, exemplifies the classically held view of stepwise cancer development. The current model of tumorigenesis, based on analyses of precursor lesions, termed pancreatic intraepithelial neoplasm (PanINs) lesions, makes two predictions: first, that pancreatic cancer develops through a particular sequence of genetic alterations (KRAS, followed by CDKN2A, then TP53 and SMAD4); and second, that the evolutionary trajectory of pancreatic cancer progression is gradual because each alteration is acquired independently. A shortcoming of this model is that clonally expanded precursor lesions do not always belong to the tumour lineage, indicating that the evolutionary trajectory of the tumour lineage and precursor lesions can be divergent. This prevailing model of tumorigenesis has contributed to the clinical notion that pancreatic cancer evolves slowly and presents at a late stage. However, the propensity for this disease to rapidly metastasize and the inability to improve patient outcomes, despite efforts aimed at early detection, suggest that pancreatic cancer progression is not gradual. Here, using newly developed informatics tools, we tracked changes in DNA copy number and their associated rearrangements in tumour-enriched genomes and found that pancreatic cancer tumorigenesis is neither gradual nor follows the accepted mutation order. Two-thirds of tumours harbour complex rearrangement patterns associated with mitotic errors, consistent with punctuated equilibrium as the principal evolutionary trajectory. In a subset of cases, the consequence of such errors is the simultaneous, rather than sequential, knockout of canonical preneoplastic genetic drivers that are likely to set-off invasive cancer growth. These findings challenge the current progression model of pancreatic cancer and provide insights into the mutational processes that give rise to these aggressive tumours.

Identification of pre-leukaemic haematopoietic stem cells in acute leukaemia.

In acute myeloid leukaemia (AML), the cell of origin, nature and biological consequences of initiating lesions, and order of subsequent mutations remain poorly understood, as AML is typically diagnosed without observation of a pre-leukaemic phase. Here, highly purified haematopoietic stem cells (HSCs), progenitor and mature cell fractions from the blood of AML patients were found to contain recurrent DNMT3A mutations (DNMT3A(mut)) at high allele frequency, but without coincident NPM1 mutations (NPM1c) present in AML blasts. DNMT3A(mut)-bearing HSCs showed a multilineage repopulation advantage over non-mutated HSCs in xenografts, establishing their identity as pre-leukaemic HSCs. Pre-leukaemic HSCs were found in remission samples, indicating that they survive chemotherapy. Therefore DNMT3A(mut) arises early in AML evolution, probably in HSCs, leading to a clonally expanded pool of pre-leukaemic HSCs from which AML evolves. Our findings provide a paradigm for the detection and treatment of pre-leukaemic clones before the acquisition of additional genetic lesions engenders greater therapeutic resistance.

Clinical genomics information management software linking cancer genome sequence and clinical decisions.

Using sequencing information to guide clinical decision-making requires coordination of a diverse set of people and activities. In clinical genomics, the process typically includes sample acquisition, template preparation, genome data generation, analysis to identify and confirm variant alleles, interpretation of clinical significance, and reporting to clinicians. We describe a software application developed within a clinical genomics study, to support this entire process. The software application tracks patients, samples, genomic results, decisions and reports across the cohort, monitors progress and sends reminders, and works alongside an electronic data capture system for the trial's clinical and genomic data. It incorporates systems to read, store, analyze and consolidate sequencing results from multiple technologies, and provides a curated knowledge base of tumor mutation frequency (from the COSMIC database) annotated with clinical significance and drug sensitivity to generate reports for clinicians. By supporting the entire process, the application provides deep support for clinical decision making, enabling the generation of relevant guidance in reports for verification by an expert panel prior to forwarding to the treating physician.

Feasibility of real time next generation sequencing of cancer genes linked to drug response: results from a clinical trial.

The successes of targeted drugs with companion predictive biomarkers and the technological advances in gene sequencing have generated enthusiasm for evaluating personalized cancer medicine strategies using genomic profiling. We assessed the feasibility of incorporating real-time analysis of somatic mutations within exons of 19 genes into patient management. Blood, tumor biopsy and archived tumor samples were collected from 50 patients recruited from four cancer centers. Samples were analyzed using three technologies: targeted exon sequencing using Pacific Biosciences PacBio RS, multiplex somatic mutation genotyping using Sequenom MassARRAY and Sanger sequencing. An expert panel reviewed results prior to reporting to clinicians. A clinical laboratory verified actionable mutations. Fifty patients were recruited. Nineteen actionable mutations were identified in 16 (32%) patients. Across technologies, results were in agreement in 100% of biopsy specimens and 95% of archival specimens. Profiling results from paired archival/biopsy specimens were concordant in 30/34 (88%) patients. We demonstrated that the use of next generation sequencing for real-time genomic profiling in advanced cancer patients is feasible. Additionally, actionable mutations identified in this study were relatively stable between archival and biopsy samples, implying that cancer mutations that are good predictors of drug response may remain constant across clinical stages.

A cancer cell-line titration series for evaluating somatic classification.

BACKGROUND: Accurate detection of somatic single nucleotide variants and small insertions and deletions from DNA sequencing experiments of tumour-normal pairs is a challenging task. Tumour samples are often contaminated with normal cells confounding the available evidence for the somatic variants. Furthermore, tumours are heterogeneous so sub-clonal variants are observed at reduced allele frequencies. We present here a cell-line titration series dataset that can be used to evaluate somatic variant calling pipelines with the goal of reliably calling true somatic mutations at low allele frequencies. RESULTS: Cell-line DNA was mixed with matched normal DNA at 8 different ratios to generate samples with known tumour cellularities, and exome sequenced on Illumina HiSeq to depths of >300×. The data was processed with several different variant calling pipelines and verification experiments were performed to assay >1500 somatic variant candidates using Ion Torrent PGM as an orthogonal technology. By examining the variants called at varying cellularities and depths of coverage, we show that the best performing pipelines are able to maintain a high level of precision at any cellularity. In addition, we estimate the number of true somatic variants undetected as cellularity and coverage decrease. CONCLUSIONS: Our cell-line titration series dataset, along with the associated verification results, was effective for this evaluation and will serve as a valuable dataset for future somatic calling algorithm development. The data is available for further analysis at the European Genome-phenome Archive under accession number EGAS00001001016. Data access requires registration through the International Cancer Genome Consortium's Data Access Compliance Office (ICGC DACO).

Use of Sequenom sample ID Plus® SNP genotyping in identification of FFPE tumor samples.

Short tandem repeat (STR) analysis, such as the AmpFlSTR® Identifiler® Plus kit, is a standard, PCR-based human genotyping method used in the field of forensics. Misidentification of cell line and tissue DNA can be costly if not detected early; therefore it is necessary to have quality control measures such as STR profiling in place. A major issue in large-scale research studies involving archival formalin-fixed paraffin embedded (FFPE) tissues is that varying levels of DNA degradation can result in failure to correctly identify samples using STR genotyping. PCR amplification of STRs of several hundred base pairs is not always possible when DNA is degraded. The Sample ID Plus® panel from Sequenom allows for human DNA identification and authentication using SNP genotyping. In comparison to lengthy STR amplicons, this multiplexing PCR assay requires amplification of only 76-139 base pairs, and utilizes 47 SNPs to discriminate between individual samples. In this study, we evaluated both STR and SNP genotyping methods of sample identification, with a focus on paired FFPE tumor/normal DNA samples intended for next-generation sequencing (NGS). The ability to successfully validate the identity of FFPE samples can enable cost savings by reducing rework.

Development of a broad-based ADME panel for use in pharmacogenomic studies.

AIM: To optimally address the interindividual variability observed in pharmacokinetic drug response, we have created a custom genotyping panel that interrogates most of the key genetic variations present in a set of 181 prioritized genes responsible for the absorption, distribution, metabolism and excretion (ADME) of many therapeutic agents. This consensus list of genes and variants was based on the ADME core and extended gene lists compiled by a group of pharmaceutical companies as having relevance. Although these pharmacokinetic genes and pathways are well known, tools that can interrogate a large number of these genes simultaneously within a single experiment are not currently available. METHODS: Using novel design strategies, we have developed an optimized and validated ADME genotyping panel, encompassing approximately 3000 variants, that has broad applicability to any study or clinical trial that would benefit from the evaluation of an extensive list of ADME genes. RESULTS & CONCLUSION: Over the course of three design iterations, overall assay conversion rates were improved from 83 to 97% resulting in a panel that fills in many of the gaps in coverage present on currently available commercial genotyping assays. The utility of the assay has been demonstrated by the screening of more than 1000 samples resulting in the discovery of novel pharmacogenomic associations. The assay, and the underlying methods, will continue to be a valuable tool for use in future pharmacogenomic studies.

CKM and LILRB5 are associated with serum levels of creatine kinase.

BACKGROUND: Statins (HMG-CoA reductase inhibitors) are the most prescribed class of lipid-lowering drugs for the treatment and prevention of cardiovascular disease. Creatine kinase (CK) is a commonly used biomarker to assist in the diagnosis of statin-induced myotoxicity but the normal range of CK concentrations is wide, which limits its use as a diagnostic biomarker. METHODS AND RESULTS: We conducted a genome-wide association study of serum CK levels in 3412 statin users. Patients were recruited in Quebec, Canada, and genotyped on Illumina Human610-Quad and an iSelect panel enriched for lipid homeostasis, hypertension, and drug metabolism genes. We found a strong association signal between serum levels of CK and the muscle CK (CKM) gene (rs11559024: P=3.69×10(-16); R(2)=0.02) and with the leukocyte immunoglobulin-like receptor subfamily B member 5 (LILRB5) gene (rs2361797: P=1.96×10(-10); R(2)=0.01). Genetic variants in those 2 genes were independently associated with CK levels in statin users. Results were successfully replicated in 5330 participants from the Montreal Heart Institute Biobank in statin users for CKM (rs11559024: P=4.32×10(-16); R(2)=0.02) and LILRB5 (rs12975366 P=4.45×10(-10); R(2)=0.01) and statin nonusers (P=4.08×10(-7), R(2)=0.01; P=3.17×10(-9), R(2)=0.02, respectively). CONCLUSIONS: This is the first genome-wide study to report on the underlying genetic determinants of CK variation in a population of statin users. We found statistically significant association for variants in the CKM and LILRB5 genes.

Variable clonal repopulation dynamics influence chemotherapy response in colorectal cancer.

Intratumoral heterogeneity arises through the evolution of genetically diverse subclones during tumor progression. However, it remains unknown whether cells within single genetic clones are functionally equivalent. By combining DNA copy number alteration (CNA) profiling, sequencing, and lentiviral lineage tracking, we followed the repopulation dynamics of 150 single lentivirus-marked lineages from 10 human colorectal cancers through serial xenograft passages in mice. CNA and mutational analysis distinguished individual clones and showed that clones remained stable upon serial transplantation. Despite this stability, the proliferation, persistence, and chemotherapy tolerance of lentivirally marked lineages were variable within each clone. Chemotherapy promoted the dominance of previously minor or dormant lineages. Thus, apart from genetic diversity, tumor cells display inherent functional variability in tumor propagation potential, which contributes to both cancer growth and therapy tolerance.

Cancer genomics: technology, discovery, and translation.

In recent years, the increasing awareness that somatic mutations and other genetic aberrations drive human malignancies has led us within reach of personalized cancer medicine (PCM). The implementation of PCM is based on the following premises: genetic aberrations exist in human malignancies; a subset of these aberrations drive oncogenesis and tumor biology; these aberrations are actionable (defined as having the potential to affect management recommendations based on diagnostic, prognostic, and/or predictive implications); and there are highly specific anticancer agents available that effectively modulate these targets. This article highlights the technology underlying cancer genomics and examines the early results of genome sequencing and the challenges met in the discovery of new genetic aberrations. Finally, drawing from experiences gained in a feasibility study of somatic mutation genotyping and targeted exome sequencing led by Princess Margaret Hospital-University Health Network and the Ontario Institute for Cancer Research, the processes, challenges, and issues involved in the translation of cancer genomics to the clinic are discussed.

Pharmacogenomic prediction of anthracycline-induced cardiotoxicity in children.

PURPOSE: Anthracycline-induced cardiotoxicity (ACT) is a serious adverse drug reaction limiting anthracycline use and causing substantial morbidity and mortality. Our aim was to identify genetic variants associated with ACT in patients treated for childhood cancer. PATIENTS AND METHODS: We carried out a study of 2,977 single-nucleotide polymorphisms (SNPs) in 220 key drug biotransformation genes in a discovery cohort of 156 anthracycline-treated children from British Columbia, with replication in a second cohort of 188 children from across Canada and further replication of the top SNP in a third cohort of 96 patients from Amsterdam, the Netherlands. RESULTS: We identified a highly significant association of a synonymous coding variant rs7853758 (L461L) within the SLC28A3 gene with ACT (odds ratio, 0.35; P = 1.8 × 10(-5) for all cohorts combined). Additional associations (P < .01) with risk and protective variants in other genes including SLC28A1 and several adenosine triphosphate-binding cassette transporters (ABCB1, ABCB4, and ABCC1) were present. We further explored combining multiple variants into a single-prediction model together with clinical risk factors and classification of patients into three risk groups. In the high-risk group, 75% of patients were accurately predicted to develop ACT, with 36% developing this within the first year alone, whereas in the low-risk group, 96% of patients were accurately predicted not to develop ACT. CONCLUSION: We have identified multiple genetic variants in SLC28A3 and other genes associated with ACT. Combined with clinical risk factors, genetic risk profiling might be used to identify high-risk patients who can then be provided with safer treatment options.

Damming the genomic data flood using a comprehensive analysis and storage data structure.

Data generation, driven by rapid advances in genomic technologies, is fast outpacing our analysis capabilities. Faced with this flood of data, more hardware and software resources are added to accommodate data sets whose structure has not specifically been designed for analysis. This leads to unnecessarily lengthy processing times and excessive data handling and storage costs. Current efforts to address this have centered on developing new indexing schemas and analysis algorithms, whereas the root of the problem lies in the format of the data itself. We have developed a new data structure for storing and analyzing genotype and phenotype data. By leveraging data normalization techniques, database management system capabilities and the use of a novel multi-table, multidimensional database structure we have eliminated the following: (i) unnecessarily large data set size due to high levels of redundancy, (ii) sequential access to these data sets and (iii) common bottlenecks in analysis times. The resulting novel data structure horizontally divides the data to circumvent traditional problems associated with the use of databases for very large genomic data sets. The resulting data set required 86% less disk space and performed analytical calculations 6248 times faster compared to a standard approach without any loss of information. Database URL: http://castor.pharmacogenomics.ca.

Genetic variants in TPMT and COMT are associated with hearing loss in children receiving cisplatin chemotherapy.

Cisplatin is a widely used and effective chemotherapeutic agent, although its use is restricted by the high incidence of irreversible ototoxicity associated with it. In children, cisplatin ototoxicity is a serious and pervasive problem, affecting more than 60% of those receiving cisplatin and compromising language and cognitive development. Candidate gene studies have previously reported associations of cisplatin ototoxicity with genetic variants in the genes encoding glutathione S-transferases and megalin. We report association analyses for 220 drug-metabolism genes in genetic susceptibility to cisplatin-induced hearing loss in children. We genotyped 1,949 SNPs in these candidate genes in an initial cohort of 54 children treated in pediatric oncology units, with replication in a second cohort of 112 children recruited through a national surveillance network for adverse drug reactions in Canada. We identified genetic variants in TPMT (rs12201199, P value = 0.00022, OR = 17.0, 95% CI 2.3-125.9) and COMT (rs9332377, P value = 0.00018, OR = 5.5, 95% CI 1.9-15.9) associated with cisplatin-induced hearing loss in children.