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Advancing a clinically relevant perspective of the clonal nature of cancer.

ABSTRACT: Cancers frequently arise as a result of an acquired genomic instability and the subsequent clonal evolution of neoplastic cells with variable patterns of genetic aberrations. Thus, the presence and behaviors of distinct clonal populations in each patient's tumor may underlie multiple clinical phenotypes in cancers. We applied DNA content-based flow sorting to identify and isolate the nuclei of clonal populations from tumor biopsies, which was coupled with array CGH and targeted resequencing. The results produced high-definition genomic profiles of clonal populations from 40 pancreatic adenocarcinomas and a set of prostate adenocarcinomas, including serial biopsies from a patient who progressed to androgen-independent metastatic disease. The genomes of clonal populations were found to have patient-specific aberrations of clinical relevance. Furthermore, we identified genomic aberrations specific to therapeutically responsive and resistant clones arising during the evolution of androgen-independent metastatic prostate adenocarcinoma. We also distinguished divergent clonal populations within single biopsies and mapped aberrations in multiple aneuploid populations arising in primary and metastatic pancreatic adenocarcinoma. We propose that our high-definition analyses of the genomes of distinct clonal populations of cancer cells in patients in vivo can help guide diagnoses and tailor approaches to personalized treatment.

SUBMITTER: Ruiz C

PROVIDER: S-EPMC3141975 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature

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Advancing a clinically relevant perspective of the clonal nature of cancer.

Ruiz Christian C Lenkiewicz Elizabeth E Evers Lisa L Holley Tara T Robeson Alex A Kiefer Jeffrey J Demeure Michael J MJ Hollingsworth Michael A MA Shen Michael M Prunkard Donna D Rabinovitch Peter S PS Zellweger Tobias T Mousses Spyro S Trent Jeffrey M JM Carpten John D JD Bubendorf Lukas L Von Hoff Daniel D Barrett Michael T MT

Proceedings of the National Academy of Sciences of the United States of America 20110705 29

Cancers frequently arise as a result of an acquired genomic instability and the subsequent clonal evolution of neoplastic cells with variable patterns of genetic aberrations. Thus, the presence and behaviors of distinct clonal populations in each patient's tumor may underlie multiple clinical phenotypes in cancers. We applied DNA content-based flow sorting to identify and isolate the nuclei of clonal populations from tumor biopsies, which was coupled with array CGH and targeted resequencing. The ...[more]

PMID: 21730190

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Project description:BackgroundPediatric cancers typically have a distinct genomic landscape when compared to adult cancers and frequently carry somatic gene fusion events that alter gene expression and drive tumorigenesis. Sensitive and specific detection of gene fusions through the analysis of next-generation-based RNA sequencing (RNA-Seq) data is computationally challenging and may be confounded by low tumor cellularity or underlying genomic complexity. Furthermore, numerous computational tools are available to identify fusions from supporting RNA-Seq reads, yet each algorithm demonstrates unique variability in sensitivity and precision, and no clearly superior approach currently exists. To overcome these challenges, we have developed an ensemble fusion calling approach to increase the accuracy of identifying fusions.ResultsOur Ensemble Fusion (EnFusion) approach utilizes seven fusion calling algorithms: Arriba, CICERO, FusionMap, FusionCatcher, JAFFA, MapSplice, and STAR-Fusion, which are packaged as a fully automated pipeline using Docker and Amazon Web Services (AWS) serverless technology. This method uses paired end RNA-Seq sequence reads as input, and the output from each algorithm is examined to identify fusions detected by a consensus of at least three algorithms. These consensus fusion results are filtered by comparison to an internal database to remove likely artifactual fusions occurring at high frequencies in our internal cohort, while a "known fusion list" prevents failure to report known pathogenic events. We have employed the EnFusion pipeline on RNA-Seq data from 229 patients with pediatric cancer or blood disorders studied under an IRB-approved protocol. The samples consist of 138 central nervous system tumors, 73 solid tumors, and 18 hematologic malignancies or disorders. The combination of an ensemble fusion-calling pipeline and a knowledge-based filtering strategy identified 67 clinically relevant fusions among our cohort (diagnostic yield of 29.3%), including RBPMS-MET, BCAN-NTRK1, and TRIM22-BRAF fusions. Following clinical confirmation and reporting in the patient's medical record, both known and novel fusions provided medically meaningful information.ConclusionsThe EnFusion pipeline offers a streamlined approach to discover fusions in cancer, at higher levels of sensitivity and accuracy than single algorithm methods. Furthermore, this method accurately identifies driver fusions in pediatric cancer, providing clinical impact by contributing evidence to diagnosis and, when appropriate, indicating targeted therapies.

Project description:Current prognostic gene expression profiles for breast cancer mainly reflect proliferation status and are most useful in ER-positive cancers. Triple-negative breast cancers (TNBCs) are clinically heterogeneous, and prognostic markers and biology-based therapies are needed to better treat this disease. We assembled Affymetrix gene expression data for 579 TNBCs and performed unsupervised analysis to define metagenes that distinguish molecular subsets within TNBC. We used n=394 cases for discovery and n=185 cases for validation. Sixteen metagenes emerged that identified basal-like, apocrine and claudin-low molecular subtypes, or reflected various non-neoplastic cell populations including immune cells, blood, adipocytes, stroma, angiogenesis, and inflammation within the cancer. The expressions of these metagenes were correlated with survival and multivariate analysis was performed including routine clinical and pathological variables. 73% of TNBCs displayed basal-like molecular subtype that correlated with high histological grade and younger age. Survival of basal-like TNBC was not different from non-basal-like TNBC. High expression of immune cell metagenes was associated with good and high expression of inflammation and angiogenesis-related metagenes were associated with poor prognosis. A ratio of high B-cell and low IL-8 metagenes identified 32% of TNBC with good prognosis (HR 0.37, 95% CI 0.22-0.61; P<0.001) and was the only significant predictor in multivariate analysis including routine clincopathological variables. We describe a ratio of high B-cell presence and low IL-8 activity as a powerful new prognostic marker for TNBC. Inhibition of the IL-8 pathway also represents an attractive novel therapeutic target for this disease. Analysis of primary breast cancer biopsies from patients before treatment. No replicates. No control or reference samples are included. The set of 579 TNBCs includes: (1) 67 new GEO Samples (GSM782523-GSM782589), (2) 489 re-analyzed GEO Samples (see 'Relation' links below), and (3) 23 re-analyzed ArrayExpress Samples. Cohorts: HH = University of Hamburg FRA = University of Frankfurt, adjuvant chemotherapy FRA-2 = University of Frankfurt, neoadjuvant chemotherapy FRA-3 = University of Frankfurt, no adjuvant chemotherapy Data processing of the 579 TNBC Samples: MAS5 values were taken from GEO if available. For samples with no MAS5 values, CEL files were downloaded from GEO and the affy package from Bioconductor was used to generate MAS5 values. Next, MAS5 values corresponding only to the 22283 probesets from the U133A array were compiled. Subsequently, normalization of MAS5 data was performed using the command line version of the program CLUSTER 3.0 (Michael Eisen; updated by Michiel de Hoon; http://bonsai.hgc.jp/~mdehoon/software/cluster/command.txt). The following three steps were performed in the following order: 1. log2 transformation of MAS5 values 2. median centering of arrays 3. magnitude normalization of arrays These three steps correspond to the following commands: cluster.com filename -l cluster.com filename -ca m cluster.com filename -na The resulting dataset, which is linked below as a supplementary file, was used for the subsequent analyses.

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Advancing a clinically relevant perspective of the clonal nature of cancer.

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Advancing a clinically relevant perspective of the clonal nature of cancer.

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