Edward J. Fox

Detection of ultra-rare mutations by next-generation sequencing

Next-generation DNA sequencing promises to revolutionize clinical medicine and basic research. However, while this technology has the capacity to generate hundreds of billions of nucleotides of DNA sequence in a single experiment, the error rate of ∼1% results in hundreds of millions of sequencing mistakes. These scattered errors can be tolerated in some applications but become extremely problematic when “deep sequencing” genetically heterogeneous mixtures, such as tumors or mixed microbial populations. To overcome limitations in sequencing accuracy, we have developed a method termed Duplex Sequencing. This approach greatly reduces errors by independently tagging and sequencing each of the two strands of a DNA duplex. As the two strands are complementary, true mutations are found at the same position in both strands. In contrast, PCR or sequencing errors result in mutations in only one strand and can thus be discounted as technical error. We determine that Duplex Sequencing has a theoretical background error rate of less than one artifactual mutation per billion nucleotides sequenced. In addition, we establish that detection of mutations present in only one of the two strands of duplex DNA can be used to identify sites of DNA damage. We apply the method to directly assess the frequency and pattern of random mutations in mitochondrial DNA from human cells.

Mutational Heterogeneity in Human Cancers: Origin and Consequences

Cancer recapitulates Darwinian evolution. Mutations acquired during life that provide cells with a growth or survival advantage will preferentially multiply to form a tumor. As a result of The Cancer Genome Atlas Project, we have gathered detailed information on the nucleotide sequence changes in a number of human cancers. The sources of mutations in cancer are diverse, and the complexity of those found to be clonally present in tumors has increasingly made it difficult to identify key rate-limiting genes for tumor growth that could serve as potential targets for directed therapies. The impact of DNA sequencing on future cancer research and personalized therapy is likely to be profound and merits critical evaluation.

CENP-F expression is associated with poor prognosis and chromosomal instability in patients with primary breast cancer

DNA microarrays have the potential to classify tumors according to their transcriptome. Tissue microarrays (TMAs) facilitate the validation of biomarkers by offering a high‐throughput approach to sample analysis. We reanalyzed a high profile breast cancer DNA microarray dataset containing 96 tumor samples using a powerful statistical approach, between group analyses. Among the genes we identified was centromere protein‐F (CENP‐F), a gene associated with poor prognosis. In a published follow‐up breast cancer DNA microarray study, comprising 295 tumour samples, we found that CENP‐F upregulation was significantly associated with worse overall survival (p < 0.001) and reduced metastasis‐free survival (p < 0.001). To validate and expand upon these findings, we used 2 independent breast cancer patient cohorts represented on TMAs. CENP‐F protein expression was evaluated by immunohistochemistry in 91 primary breast cancer samples from cohort I and 289 samples from cohort II. CENP‐F correlated with markers of aggressive tumor behavior including ER negativity and high tumor grade. In cohort I, CENP‐F was significantly associated with markers of CIN including cyclin E, increased telomerase activity, c‐Myc amplification and aneuploidy. In cohort II, CENP‐F correlated with VEGFR2, phosphorylated Ets‐2 and Ki67, and in multivariate analysis, was an independent predictor of worse breast cancer‐specific survival (p = 0.036) and overall survival (p = 0.040). In conclusion, we identified CENP‐F as a biomarker associated with poor outcome in breast cancer and showed several novel associations of biological significance.

Interaction of soft condensed materials with living cells: Phenotype/transcriptome correlations for the hydrophobic effect

The assessment of biomaterial compatibility relies heavily on the analysis of macroscopic cellular responses to material interaction. However, new technologies have become available that permit a more profound understanding of the molecular basis of cell–biomaterial interaction. Here, both conventional phenotypic and contemporary transcriptomic (DNA microarray-based) analysis techniques were combined to examine the interaction of cells with a homologous series of copolymer films that subtly vary in terms of surface hydrophobicity. More specifically, we used differing combinations of N-isopropylacrylamide, which is presently used as an adaptive cell culture substrate, and the more hydrophobic, yet structurally similar, monomer N-tert-butylacrylamide. We show here that even discrete modifications with respect to the physiochemistry of soft amorphous materials can lead to significant impacts on the phenotype of interacting cells. Furthermore, we have elucidated putative links between phenotypic responses to cell–biomaterial interaction and global gene expression profile alterations. This case study indicates that high-throughput analysis of gene expression not only can greatly refine our knowledge of cell–biomaterial interaction, but also can yield novel biomarkers for potential use in biocompatibility assessment.

Combination of SELDI-TOF-MS and Data Mining Provides Early-stage Response Prediction for Rectal Tumors Undergoing Multimodal Neoadjuvant Therapy

Objective:We investigated whether proteomic analysis of the low molecular weight region of the serum proteome could predict histologic response of locally advanced rectal cancer to neoadjuvant radiochemotherapy (RCT). Summary Background Data:Proteomic analysis of serum is emerging as a powerful new modality in cancer, in terms of both screening and monitoring response to treatment. No study has yet assessed its ability to predict and monitor the response of rectal cancer to RCT. Methods:Sequential serum samples from 20 patients undergoing RCT were prospectively collected. Time points sampled were as follows: pretreatment, 24/48 hours, 1 week, 2 weeks, 3 weeks, 5 weeks (last day of RCT), and presurgery. Response to treatment was measured using a 5-point tumor regression grade (TRG) based on the degree of residual tumor to fibrosis. All serum samples were analyzed in duplicate using surface-enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF-MS). Support vector machine (SVM) analysis of spectra was used to generate a predictive algorithm for each time point based on proteins that were maximally differentially expressed between good and poor responders. This algorithm was then tested using leave-one-out cross validation. Results:In total, 230 spectra were generated representing all available time points from 9 good responders (TRG 1+2) and 11 poor responders (TRG 3–5). SVM analysis indicated that changes within the serum proteome at the 24/48 hours time point into treatment provided optimal classification accuracy. In more detail, a cohort of 14 protein peaks were identified that collectively differentiated between good and poor responders, with 87.5% sensitivity and 80% specificity. Conclusions:Serum proteomic analysis may represent an early response predictor in multimodal treatment regimens of rectal cancer. These data suggest that this novel, minimally invasive modality may be a useful adjunct in the multimodal management of rectal cancer, and in the design of future clinical trials.

Oxidative stress is not a major contributor to somatic mitochondrial DNA mutations.

The accumulation of somatic mitochondrial DNA (mtDNA) mutations is implicated in aging and common diseases of the elderly, including cancer and neurodegenerative disease. However, the mechanisms that influence the frequency of somatic mtDNA mutations are poorly understood. To develop a simple invertebrate model system to address this matter, we used the Random Mutation Capture (RMC) assay to characterize the age-dependent frequency and distribution of mtDNA mutations in the fruit fly Drosophila melanogaster. Because oxidative stress is a major suspect in the age-dependent accumulation of somatic mtDNA mutations, we also used the RMC assay to explore the influence of oxidative stress on the somatic mtDNA mutation frequency. We found that many of the features associated with mtDNA mutations in vertebrates are conserved in Drosophila, including a comparable somatic mtDNA mutation frequency (∼10−5), an increased frequency of mtDNA mutations with age, and a prevalence of transition mutations. Only a small fraction of the mtDNA mutations detected in young or old animals were G∶C to T∶A transversions, a signature of oxidative damage, and loss-of-function mutations in the mitochondrial superoxide dismutase, Sod2, had no detectable influence on the somatic mtDNA mutation frequency. Moreover, a loss-of-function mutation in Ogg1, which encodes a DNA repair enzyme that removes oxidatively damaged deoxyguanosine residues (8-hydroxy-2′-deoxyguanosine), did not significantly influence the somatic mtDNA mutation frequency of Sod2 mutants. Together, these findings indicate that oxidative stress is not a major cause of somatic mtDNA mutations. Our data instead suggests that somatic mtDNA mutations arise primarily from errors that occur during mtDNA replication. Further studies using Drosophila should aid in the identification of factors that influence the frequency of somatic mtDNA mutations.

Cancer Genome Sequencing—An Interim Analysis

With the publishing of the first complete whole genome of a human cancer and its paired normal, we have passed a key milestone in the cancer genome sequencing strategy. The generation of such data will, thanks to technical advances, soon become commonplace. As a significant number of proof-of-concept studies have been published, it is important to analyze now the likely implications of these data and how this information might frame cancer research in the near future. The diversity of genes mutated within individual tumor types, the most striking feature of all studies reported to date, challenges gene-centric models of tumorigenesis. Although cancer genome sequencing will revolutionize certain aspects of personalized care, the value of these studies in facilitating the development of new therapies, their primary goal, seems less promising. Most significantly, however, the cancer genome sequencing strategy, as currently applied, fails to characterize the most relevant genomic features of cancer-the mutational heterogeneity within individual tumors.

Lethal Mutagenesis: targeting the mutator phenotype in cancer

The evolution of cancer and RNA viruses share many similarities. Both exploit high levels of genotypic diversity to enable extensive phenotypic plasticity and thereby facilitate rapid adaptation. In order to accumulate large numbers of mutations, we have proposed that cancers express a mutator phenotype. Similar to cancer cells, many viral populations, by replicating their genomes with low fidelity, carry a substantial mutational load. As high levels of mutation are potentially deleterious, the viral mutation frequency is thresholded at a level below which viral populations equilibrate in a traditional mutation-selection balance, and above which the population is no longer viable, i.e., the population undergoes an error catastrophe. Because their mutation frequencies are fine-tuned just below this error threshold, viral populations are susceptible to further increases in mutational load and, recently this phenomenon has been exploited therapeutically by a concept that has been termed lethal mutagenesis. Here we review the application of lethal mutagenesis to the treatment of HIV and discuss how lethal mutagenesis may represent a novel therapeutic approach for the treatment of solid cancers.

Sequencing small genomic targets with high efficiency and extreme accuracy

The detection of minority variants in mixed samples requires methods for enrichment and accurate sequencing of small genomic intervals. We describe an efficient approach based on sequential rounds of hybridization with biotinylated oligonucleotides that enables more than 1-million-fold enrichment of genomic regions of interest. In conjunction with error-correcting double-stranded molecular tags, our approach enables the quantification of mutations in individual DNA molecules.

Do mutator mutations fuel tumorigenesis

The mutator phenotype hypothesis proposes that the mutation rate of normal cells is insufficient to account for the large number of mutations found in human cancers. Consequently, human tumors exhibit an elevated mutation rate that increases the likelihood of a tumor acquiring advantageous mutations. The hypothesis predicts that tumors are composed of cells harboring hundreds of thousands of mutations, as opposed to a small number of specific driver mutations, and that malignant cells within a tumor therefore constitute a highly heterogeneous population. As a result, drugs targeting specific mutated driver genes or even pathways of mutated driver genes will have only limited anticancer potential. In addition, because the tumor is composed of such a diverse cell population, tumor cells harboring drug-resistant mutations will exist prior to the administration of any chemotherapeutic agent. We present recent evidence in support of the mutator phenotype hypothesis, major arguments against this concept, and discuss the clinical consequences of tumor evolution fueled by an elevated mutation rate. We also consider the therapeutic possibility of altering the rate of mutation accumulation. Most significantly, we contend that there is a need to fundamentally reconsider current approaches to personalized cancer therapy. We propose that targeting cellular pathways that alter the rate of mutation accumulation in tumors will ultimately prove more effective than attempting to identify and target mutant driver genes or driver pathways.