Inmaculada Spiteri

MicroRNA expression profiling of human breast cancer identifies new markers of tumor subtype

BackgroundMicroRNAs (miRNAs), a class of short non-coding RNAs found in many plants and animals, often act post-transcriptionally to inhibit gene expression.ResultsHere we report the analysis of miRNA expression in 93 primary human breast tumors, using a bead-based flow cytometric miRNA expression profiling method. Of 309 human miRNAs assayed, we identify 133 miRNAs expressed in human breast and breast tumors. We used mRNA expression profiling to classify the breast tumors as luminal A, luminal B, basal-like, HER2+ and normal-like. A number of miRNAs are differentially expressed between these molecular tumor subtypes and individual miRNAs are associated with clinicopathological factors. Furthermore, we find that miRNAs could classify basal versus luminal tumor subtypes in an independent data set. In some cases, changes in miRNA expression correlate with genomic loss or gain; in others, changes in miRNA expression are likely due to changes in primary transcription and or miRNA biogenesis. Finally, the expression of DICER1 and AGO2 is correlated with tumor subtype and may explain some of the changes in miRNA expression observed.ConclusionThis study represents the first integrated analysis of miRNA expression, mRNA expression and genomic changes in human breast cancer and may serve as a basis for functional studies of the role of miRNAs in the etiology of breast cancer. Furthermore, we demonstrate that bead-based flow cytometric miRNA expression profiling might be a suitable platform to classify breast cancer into prognostic molecular subtypes.

Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics

Glioblastoma (GB) is the most common and aggressive primary brain malignancy, with poor prognosis and a lack of effective therapeutic options. Accumulating evidence suggests that intratumor heterogeneity likely is the key to understanding treatment failure. However, the extent of intratumor heterogeneity as a result of tumor evolution is still poorly understood. To address this, we developed a unique surgical multisampling scheme to collect spatially distinct tumor fragments from 11 GB patients. We present an integrated genomic analysis that uncovers extensive intratumor heterogeneity, with most patients displaying different GB subtypes within the same tumor. Moreover, we reconstructed the phylogeny of the fragments for each patient, identifying copy number alterations in EGFR and CDKN2A/B/p14ARF as early events, and aberrations in PDGFRA and PTEN as later events during cancer progression. We also characterized the clonal organization of each tumor fragment at the single-molecule level, detecting multiple coexisting cell lineages. Our results reveal the genome-wide architecture of intratumor variability in GB across multiple spatial scales and patient-specific patterns of cancer evolution, with consequences for treatment design.

The pitfalls of platform comparison: DNA copy number array technologies assessed

BackgroundThe accurate and high resolution mapping of DNA copy number aberrations has become an important tool by which to gain insight into the mechanisms of tumourigenesis. There are various commercially available platforms for such studies, but there remains no general consensus as to the optimal platform. There have been several previous platform comparison studies, but they have either described older technologies, used less-complex samples, or have not addressed the issue of the inherent biases in such comparisons. Here we describe a systematic comparison of data from four leading microarray technologies (the Affymetrix Genome-wide SNP 5.0 array, Agilent High-Density CGH Human 244A array, Illumina HumanCNV370-Duo DNA Analysis BeadChip, and the Nimblegen 385 K oligonucleotide array). We compare samples derived from primary breast tumours and their corresponding matched normals, well-established cancer cell lines, and HapMap individuals. By careful consideration and avoidance of potential sources of bias, we aim to provide a fair assessment of platform performance.ResultsBy performing a theoretical assessment of the reproducibility, noise, and sensitivity of each platform, notable differences were revealed. Nimblegen exhibited between-replicate array variances an order of magnitude greater than the other three platforms, with Agilent slightly outperforming the others, and a comparison of self-self hybridizations revealed similar patterns. An assessment of the single probe power revealed that Agilent exhibits the highest sensitivity. Additionally, we performed an in-depth visual assessment of the ability of each platform to detect aberrations of varying sizes. As expected, all platforms were able to identify large aberrations in a robust manner. However, some focal amplifications and deletions were only detected in a subset of the platforms.ConclusionAlthough there are substantial differences in the design, density, and number of replicate probes, the comparison indicates a generally high level of concordance between platforms, despite differences in the reproducibility, noise, and sensitivity. In general, Agilent tended to be the best aCGH platform and Affymetrix, the superior SNP-CGH platform, but for specific decisions the results described herein provide a guide for platform selection and study design, and the dataset a resource for more tailored comparisons.

Characterisation of microRNA expression in post-natal mouse mammary gland development

BackgroundThe differential expression pattern of microRNAs (miRNAs) during mammary gland development might provide insights into their role in regulating the homeostasis of the mammary epithelium. Our aim was to analyse these regulatory functions by deriving a comprehensive tissue-specific combined miRNA and mRNA expression profile of post-natal mouse mammary gland development.We measured the expression of 318 individual murine miRNAs by bead-based flow-cytometric profiling of whole mouse mammary glands throughout a 16-point developmental time course, including juvenile, puberty, mature virgin, gestation, lactation, and involution stages. In parallel whole-genome mRNA expression data were obtained.ResultsOne third (n = 102) of all murine miRNAs analysed were detected during mammary gland development. MicroRNAs were represented in seven temporally co-expressed clusters, which were enriched for both miRNAs belonging to the same family and breast cancer-associated miRNAs. Global miRNA and mRNA expression was significantly reduced during lactation and the early stages of involution after weaning. For most detected miRNA families we did not observe systematic changes in the expression of predicted targets. For miRNA families whose targets did show changes, we observed inverse patterns of miRNA and target expression. The data sets are made publicly available and the combined expression profiles represent an important community resource for mammary gland biology research.ConclusionMicroRNAs were expressed in likely co-regulated clusters during mammary gland development. Breast cancer-associated miRNAs were significantly enriched in these clusters. The mechanism and functional consequences of this miRNA co-regulation provide new avenues for research into mammary gland biology and generate candidates for functional validation.

Saliva samples are a viable alternative to blood samples as a source of DNA for high throughput genotyping

BackgroundThe increasing trend for incorporation of biological sample collection within clinical trials requires sample collection procedures which are convenient and acceptable for both patients and clinicians. This study investigated the feasibility of using saliva-extracted DNA in comparison to blood-derived DNA, across two genotyping platforms: Applied Biosystems TaqmanTM and Illumina BeadchipTM genome-wide arrays.MethodPatients were recruited from the Pharmacogenetics of Breast Cancer Chemotherapy (PGSNPS) study. Paired blood and saliva samples were collected from 79 study participants. The Oragene DNA Self-Collection kit (DNAgenotek®) was used to collect and extract DNA from saliva. DNA from EDTA blood samples (median volume 8 ml) was extracted by Gen-Probe, Livingstone, UK. DNA yields, standard measures of DNA quality, genotype call rates and genotype concordance between paired, duplicated samples were assessed.ResultsTotal DNA yields were lower from saliva (mean 24 μg, range 0.2–52 μg) than from blood (mean 210 μg, range 58–577 μg) and a 2-fold difference remained after adjusting for the volume of biological material collected. Protein contamination and DNA fragmentation measures were greater in saliva DNA. 78/79 saliva samples yielded sufficient DNA for use on Illumina Beadchip arrays and using Taqman assays. Four samples were randomly selected for genotyping in duplicate on the Illumina Beadchip arrays. All samples were genotyped using Taqman assays. DNA quality, as assessed by genotype call rates and genotype concordance between matched pairs of DNA was high (>97%) for each measure in both blood and saliva-derived DNA.ConclusionWe conclude that DNA from saliva and blood samples is comparable when genotyping using either Taqman assays or genome-wide chip arrays. Saliva sampling has the potential to increase participant recruitment within clinical trials, as well as reducing the resources and organisation required for multicentre sample collection.

Patient-derived organoids model treatment response of metastatic gastrointestinal cancers

Cancer organoids to model therapy response Cancer organoids are miniature, three-dimensional cell culture models that can be made from primary patient tumors and studied in the laboratory. Vlachogiannis et al. asked whether such “tumor-in-a-dish” approaches can be used to predict drug responses in the clinic. They generated a live organoid biobank from patients with metastatic gastrointestinal cancer who had previously been enrolled in phase I or II clinical trials. This allowed the authors to compare organoid drug responses with how the patient actually responded in the clinic. Encouragingly, the organoids had similar molecular profiles to those of the patient tumor, reinforcing their value as a platform for drug screening and development. Science, this issue p. 920 Organoids can recapitulate patient responses in the clinic, with potential for drug screening and personalized medicine. Patient-derived organoids (PDOs) have recently emerged as robust preclinical models; however, their potential to predict clinical outcomes in patients has remained unclear. We report on a living biobank of PDOs from metastatic, heavily pretreated colorectal and gastroesophageal cancer patients recruited in phase 1/2 clinical trials. Phenotypic and genotypic profiling of PDOs showed a high degree of similarity to the original patient tumors. Molecular profiling of tumor organoids was matched to drug-screening results, suggesting that PDOs could complement existing approaches in defining cancer vulnerabilities and improving treatment responses. We compared responses to anticancer agents ex vivo in organoids and PDO-based orthotopic mouse tumor xenograft models with the responses of the patients in clinical trials. Our data suggest that PDOs can recapitulate patient responses in the clinic and could be implemented in personalized medicine programs.

Single-Molecule Genomic Data Delineate Patient-Specific Tumor Profiles and Cancer Stem Cell Organization

Substantial evidence supports the concept that cancers are organized in a cellular hierarchy with cancer stem cells (CSC) at the apex. To date, the primary evidence for CSCs derives from transplantation assays, which have known limitations. In particular, they are unable to report on the fate of cells within the original human tumor. Because of the difficulty in measuring tumor characteristics in patients, cellular organization and other aspects of cancer dynamics have not been quantified directly, although they likely play a fundamental role in tumor progression and therapy response. As such, new approaches to study CSCs in patient-derived tumor specimens are needed. In this study, we exploited ultradeep single-molecule genomic data derived from multiple microdissected colorectal cancer glands per tumor, along with a novel quantitative approach to measure tumor characteristics, define patient-specific tumor profiles, and infer tumor ancestral trees. We show that each cancer is unique in terms of its cellular organization, molecular heterogeneity, time from malignant transformation, and rate of mutation and apoptosis. Importantly, we estimate CSC fractions between 0.5% and 4%, indicative of a hierarchical organization responsible for long-lived CSC lineages, with variable rates of symmetric cell division. We also observed extensive molecular heterogeneity, both between and within individual cancer glands, suggesting a complex hierarchy of mitotic clones. Our framework enables the measurement of clinically relevant patient-specific characteristics in vivo, providing insight into the cellular organization and dynamics of tumor growth, with implications for personalized patient care.

Current Challenges in Glioblastoma: Intratumour Heterogeneity, Residual Disease, and Models to Predict Disease Recurrence

Glioblastoma (GB) is the most common primary malignant brain tumor, and despite the availability of chemotherapy and radiotherapy to combat the disease, overall survival remains low with a high incidence of tumor recurrence. Technological advances are continually improving our understanding of the disease, and in particular, our knowledge of clonal evolution, intratumor heterogeneity, and possible reservoirs of residual disease. These may inform how we approach clinical treatment and recurrence in GB. Mathematical modeling (including neural networks) and strategies such as multiple sampling during tumor resection and genetic analysis of circulating cancer cells, may be of great future benefit to help predict the nature of residual disease and resistance to standard and molecular therapies in GB.

Extent of differential allelic expression of candidate breast cancer genes is similar in blood and breast

IntroductionNormal gene expression variation is thought to play a central role in inter-individual variation and susceptibility to disease. Regulatory polymorphisms in cis-acting elements result in the unequal expression of alleles. Differential allelic expression (DAE) in heterozygote individuals could be used to develop a new approach to discover regulatory breast cancer susceptibility loci. As access to large numbers of fresh breast tissue to perform such studies is difficult, a suitable surrogate test tissue must be identified for future studies.MethodsWe measured differential allelic expression of 12 candidate genes possibly related to breast cancer susceptibility (BRCA1, BRCA2, C1qA, CCND3, EMSY, GPX1, GPX4, MLH3, MTHFR, NBS1, TP53 and TRXR2) in breast tissue (n = 40) and fresh blood (n = 170) of healthy individuals and EBV-transformed lymphoblastoid cells (n = 19). Differential allelic expression ratios were determined by Taqman assay. Ratio distributions were compared using t-test and Wilcoxon rank sum test, for mean ratios and variances respectively.ResultsWe show that differential allelic expression is common among these 12 candidate genes and is comparable between breast and blood (fresh and transformed lymphoblasts) in a significant proportion of them. We found that eight out of nine genes with DAE in breast and fresh blood were comparable, as were 10 out of 11 genes between breast and transformed lymphoblasts.ConclusionsOur findings support the use of differential allelic expression in blood as a surrogate for breast tissue in future studies on predisposition to breast cancer.

PMC42, a breast progenitor cancer cell line, has normal-like mRNA and microRNA transcriptomes

IntroductionThe use of cultured cell lines as model systems for normal tissue is limited by the molecular alterations accompanying the immortalisation process, including changes in the mRNA and microRNA (miRNA) repertoire. Therefore, identification of cell lines with normal-like expression profiles is of paramount importance in studies of normal gene regulation.MethodsThe mRNA and miRNA expression profiles of several breast cell lines of cancerous or normal origin were measured using printed slide arrays, Luminex bead arrays, and real-time reverse transcription-polymerase chain reaction.ResultsWe demonstrate that the mRNA expression profiles of two breast cell lines are similar to that of normal breast tissue: HB4a, immortalised normal breast epithelium, and PMC42, a breast cancer cell line that retains progenitor pluripotency allowing in-culture differentiation to both secretory and myoepithelial fates. In contrast, only PMC42 exhibits a normal-like miRNA expression profile. We identified a group of miRNAs that are highly expressed in normal breast tissue and PMC42 but are lost in all other cancerous and normal-origin breast cell lines and observed a similar loss in immortalised lymphoblastoid cell lines compared with healthy uncultured B cells. Moreover, like tumour suppressor genes, these miRNAs are lost in a variety of tumours. We show that the mechanism leading to the loss of these miRNAs in breast cancer cell lines has genomic, transcriptional, and post-transcriptional components.ConclusionWe propose that, despite its neoplastic origin, PMC42 is an excellent molecular model for normal breast epithelium, providing a unique tool to study breast differentiation and the function of key miRNAs that are typically lost in cancer.