Diagnostics of Hereditary Connective Tissue Disorders by Genetic Next-Generation Sequencing.

Aims: This quality analysis study was designed to review the indications, reports, and clinical consequences of 438 diagnostic next-generation sequencing (NGS) gene panel analyses for hereditary connective tissue disorders (HCTD). Methods: Molecular analyses were retrieved from laboratory databases and patient records, and compared to the clinical information in the requisition and classified according to the Human Phenotype Ontology. Results: In 123 of 438 NGS analyses, 156 sequence variants were reported in 33 of 54 genes analyzed. NGS analyses and, in some cases, postanalytic assessment resulted in identification of pathogenic variants in 40 (9%) of patients, and variants of uncertain significance were identified in 83 (19%) of cases analyzed. While cardiovascular abnormalities were the most common phenotype noted in the requisitions, no specific organ system could be identified in which the reported symptoms provided an actionable indication for the analysis. Certain health issues recorded in the patients' records were found to be frequently left out of requisitions. Conclusions: The interpretation of genetic sequence variants continues to be a significant challenge in HCTD. Although not associated with the highest diagnostic yield, cardiovascular disease and family history may be suitable indications for NGS due to the clinical consequences of the identification of a known or likely causative sequence variant for a vascular HCTD in patients and relatives.

Mutation screening of the TP53 gene by temporal temperature gel electrophoresis (TTGE).

A protocol for detection of mutations in the TP53 gene using temporal temperature gradient electrophoresis (TTGE) is described. TTGE is a mutation detection technique that separates DNA fragments differing by single base pairs according to their melting properties in a denaturing gel. It is based on constant denaturing conditions in the gel combined with a temperature gradient during the electrophoretic run. This method combines some of the advantages of the related techniques, denaturing gradient gel electrophoresis and constant denaturant gel electrophoresis, and eliminates some of the problems. The result is a rapid and sensitive screening technique which is robust and easily set up in smaller laboratory environments.

Deregulation of MYCN, LIN28B and LET7 in a molecular subtype of aggressive high-grade serous ovarian cancers.

Molecular subtypes of serous ovarian cancer have been recently described. Using data from independent datasets including over 900 primary tumour samples, we show that deregulation of the Let-7 pathway is specifically associated with the C5 molecular subtype of serous ovarian cancer. DNA copy number and gene expression of HMGA2, alleles of Let-7, LIN28, LIN28B, MYC, MYCN, DICER1, and RNASEN were measured using microarray and quantitative reverse transcriptase PCR. Immunohistochemistry was performed on 127 samples using tissue microarrays and anti-HMGA2 antibodies. Fluorescence in situ hybridisation of bacterial artificial chromosomes hybridized to 239 ovarian tumours was used to measure translocation at the LIN28B locus. Short interfering RNA knockdown in ovarian cell lines was used to test the functionality of associations observed. Four molecular subtypes (C1, C2, C4, C5) of high-grade serous ovarian cancers were robustly represented in each dataset and showed similar pattern of patient survival. We found highly specific activation of a pathway involving MYCN, LIN28B, Let-7 and HMGA2 in the C5 molecular subtype defined by MYCN amplification and over-expression, over-expression of MYCN targets including the Let-7 repressor LIN28B, loss of Let-7 expression and HMGA2 amplification and over-expression. DICER1, a known Let-7 target, and RNASEN were over-expressed in C5 tumours. We saw no evidence of translocation at the LIN28B locus in C5 tumours. The reported interaction between LIN28B and Let-7 was recapitulated by siRNA knockdown in ovarian cancer cell lines. Our results associate deregulation of MYCN and downstream targets, including Let-7 and oncofetal genes, with serous ovarian cancer. We define for the first time how elements of an oncogenic pathway, involving multiple genes that contribute to stem cell renewal, is specifically altered in a molecular subtype of serous ovarian cancer. By defining the drivers of a molecular subtype of serous ovarian cancers we provide a novel strategy for targeted therapeutic intervention.

miRNA-mRNA integrated analysis reveals roles for miRNAs in primary breast tumors.

INTRODUCTION: Few studies have performed expression profiling of both miRNA and mRNA from the same primary breast carcinomas. In this study we present and analyze data derived from expression profiling of 799 miRNAs in 101 primary human breast tumors, along with genome-wide mRNA profiles and extensive clinical information. METHODS: We investigate the relationship between these molecular components, in terms of their correlation with each other and with clinical characteristics. We use a systems biology approach to examine the correlative relationship between miRNA and mRNAs using statistical enrichment methods. RESULTS: We identify statistical significant differential expression of miRNAs between molecular intrinsic subtypes, and between samples with different levels of proliferation. Specifically, we point to miRNAs significantly associated with TP53 and ER status. We also show that several cellular processes, such as proliferation, cell adhesion and immune response, are strongly associated with certain miRNAs. We validate the role of miRNAs in regulating proliferation using high-throughput lysate-microarrays on cell lines and point to potential drivers of this process. CONCLUSION: This study provides a comprehensive dataset as well as methods and system-level results that jointly form a basis for further work on understanding the role of miRNA in primary breast cancer.

Merging transcriptomics and metabolomics--advances in breast cancer profiling.

BACKGROUND: Combining gene expression microarrays and high resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS) of the same tissue samples enables comparison of the transcriptional and metabolic profiles of breast cancer. The aim of this study was to explore the potential of combining these two different types of information. METHODS: Breast cancer tissue from 46 patients was analyzed by HR MAS MRS followed by gene expression microarrays. Two strategies were used to combine the gene expression and metabolic data; first using multivariate analyses to identify different groups based on gene expression and metabolic data; second correlating levels of specific metabolites to transcripts to suggest new hypotheses of connections between metabolite levels and the underlying biological processes. A parallel study was designed to address experimental issues of combining microarrays and HR MAS MRS. RESULTS: In the first strategy, using the microarray data and previously reported molecular classification methods, the majority of samples were classified as luminal A. Three subgroups of luminal A tumors were identified based on hierarchical clustering of the HR MAS MR spectra. The samples in one of the subgroups, designated A2, showed significantly lower glucose and higher alanine levels than the other luminal A samples, suggesting a higher glycolytic activity in these tumors. This group was also enriched for genes annotated with Gene Ontology (GO) terms related to cell cycle and DNA repair. In the second strategy, the correlations between concentrations of myo-inositol, glycine, taurine, glycerophosphocholine, phosphocholine, choline and creatine and all transcripts in the filtered microarray data were investigated. GO-terms related to the extracellular matrix were enriched among the genes that correlated the most to myo-inositol and taurine, while cell cycle related GO-terms were enriched for the genes that correlated the most to choline. Additionally, a subset of transcripts was identified to have slightly altered expression after HR MAS MRS and was therefore removed from all other analyses. CONCLUSIONS: Combining transcriptional and metabolic data from the same breast carcinoma sample is feasible and may contribute to a more refined subclassification of breast cancers as well as reveal relations between metabolic and transcriptional levels. See Commentary: http://www.biomedcentral.com/1741-7015/8/73.

Molecular diversity in ductal carcinoma in situ (DCIS) and early invasive breast cancer.

Ductal carcinoma in situ (DCIS) is a non-invasive form of breast cancer where cells restricted to the ducts exhibit an atypical phenotype. Some DCIS lesions are believed to rapidly transit to invasive ductal carcinomas (IDCs), while others remain unchanged. Existing classification systems for DCIS fail to identify those lesions that transit to IDC. We studied gene expression patterns of 31 pure DCIS, 36 pure invasive cancers and 42 cases of mixed diagnosis (invasive cancer with an in situ component) using Agilent Whole Human Genome Oligo Microarrays 44k. Six normal breast tissue samples were also included as controls. qRT-PCR was used for validation. All DCIS and invasive samples could be classified into the "intrinsic" molecular subtypes defined for invasive breast cancer. Hierarchical clustering establishes that samples group by intrinsic subtype, and not by diagnosis. We observed heterogeneity in the transcriptomes among DCIS of high histological grade and identified a distinct subgroup containing seven of the 31 DCIS samples with gene expression characteristics more similar to advanced tumours. A set of genes independent of grade, ER-status and HER2-status was identified by logistic regression that univariately classified a sample as belonging to this distinct DCIS subgroup. qRT-PCR of single markers clearly separated this DCIS subgroup from the other DCIS, and contains samples from several histopathological and intrinsic molecular subtypes. The genes that differentiate between these two types of DCIS suggest several processes related to the re-organisation of the microenvironment. This raises interesting possibilities for identification of DCIS lesions both with and without invasive characteristics, which potentially could be used in clinical assessment of a woman's risk of progression, and lead to improved management that would avoid the current over- and under-treatment of patients.

Molecular profiling and characterization of luminal-like and basal-like in vivo breast cancer xenograft models.

The number of relevant and well-characterized cell lines and xenograft models for studying human breast cancer are few, and may represent a limitation for this field of research. With the aim of developing new breast cancer model systems for in vivo studies of hormone dependent and independent tumor growth, progression and invasion, and for in vivo experimental therapy studies, we collected primary mammary tumor specimens from patients, and implanted them in immunodeficient mice. Primary tumor tissue from 29 patients with breast cancer was implanted subcutaneously with matrigel in SCID mice, in the presence of continuous release of estradiol. The tumors were transferred into new animals when reaching a diameter of 15mm and engrafted tumors were harvested for morphological and molecular characterization from passage six. Further, gene expression profiling was performed using Agilent Human Whole Genome Oligo Microarrays, as well as DNA copy number analysis using Agilent Human Genome CGH 244K Microarrays. Of the 30 primary tumors implanted into mice (including two implants from the same patient), two gave rise to viable tumors beyond passage ten. One showed high expression levels of estrogen receptor-alpha protein (ER) while the other was negative. Histopathological evaluation of xenograft tumors was carried out at passage 10-12; both xenografts maintained the morphological characteristics of the original tumors (classified as invasive grade III ductal carcinomas). The genomic profile of the ER-positive xenograft tumor resembled the profile of the primary tumor, while the profile obtained from the ER-negative parental tumor was different from the xenograft. However, the ER-negative parental tumor and xenograft clustered on the same branch using unsupervised hierarchical clustering analysis on RNA microarray expression data of "intrinsic genes". A significant variation was observed in the expression of extracellular matrix (ECM)-related genes, which were found downregulated in the engrafted tumors compared to the primary tumor. By IHC and qRT-PCR we found that the downregulation of stroma-related genes was compensated by the overexpression of such molecules by the mouse host tissue. The two established breast cancer xenograft models showed different histopathological characteristics and profound diversity in gene expression patterns that in part can be associated to their ER status and here described as basal-like and luminal-like phenotype, respectively. These two new breast cancer xenografts represent useful preclinical tools for developing and testing of new therapies and improving our knowledge on breast cancer biology.

p53-Repressed miRNAs are involved with E2F in a feed-forward loop promoting proliferation.

Normal cell growth is governed by a complicated biological system, featuring multiple levels of control, often deregulated in cancers. The role of microRNAs (miRNAs) in the control of gene expression is now increasingly appreciated, yet their involvement in controlling cell proliferation is still not well understood. Here we investigated the mammalian cell proliferation control network consisting of transcriptional regulators, E2F and p53, their targets and a family of 15 miRNAs. Indicative of their significance, expression of these miRNAs is downregulated in senescent cells and in breast cancers harboring wild-type p53. These miRNAs are repressed by p53 in an E2F1-mediated manner. Furthermore, we show that these miRNAs silence antiproliferative genes, which themselves are E2F1 targets. Thus, miRNAs and transcriptional regulators appear to cooperate in the framework of a multi-gene transcriptional and post-transcriptional feed-forward loop. Finally, we show that, similarly to p53 inactivation, overexpression of representative miRNAs promotes proliferation and delays senescence, manifesting the detrimental phenotypic consequence of perturbations in this circuit. Taken together, these findings position miRNAs as novel key players in the mammalian cellular proliferation network.

Radiation-induced effects on gene expression: an in vivo study on breast cancer.

BACKGROUND AND PURPOSE: Breast cancer is diagnosed worldwide in approximately one million women annually and radiation therapy is an integral part of treatment. The purpose of this study was to investigate the molecular basis underlying response to radiotherapy in breast cancer tissue. MATERIAL AND METHODS: Tumour biopsies were sampled before radiation and after 10 treatments (of 2 Gray (Gy) each) from 19 patients with breast cancer receiving radiation therapy. Gene expression microarray analyses were performed to identify in vivo radiation-responsive genes in tumours from patients diagnosed with breast cancer. The mutation status of the TP53 gene was determined by using direct sequencing. RESULTS AND CONCLUSION: Several genes involved in cell cycle regulation and DNA repair were found to be significantly induced by radiation treatment. Mutations were found in the TP53 gene in 39% of the tumours and the gene expression profiles observed seemed to be influenced by the TP53 mutation status.

Distinct molecular mechanisms underlying clinically relevant subtypes of breast cancer: gene expression analyses across three different platforms.

BACKGROUND: Gene expression profiling has been used to define molecular phenotypes of complex diseases such as breast cancer. The luminal A and basal-like subtypes have been repeatedly identified and validated as the two main subtypes out of a total of five molecular subtypes of breast cancer. These two are associated with distinctly different gene expression patterns and more importantly, a significant difference in clinical outcome. To further validate and more thoroughly characterize these two subtypes at the molecular level in tumors at an early stage, we report a gene expression profiling study using three different DNA microarray platforms. RESULTS: Expression data from 20 tumor biopsies of early stage breast carcinomas were generated on three different DNA microarray platforms; Applied Biosystems Human Genome Survey Microarrays, Stanford cDNA Microarrays and Agilent's Whole Human Genome Oligo Microarrays, and the resulting gene expression patterns were analyzed. Both unsupervised and supervised analyses identified the different clinically relevant subtypes of breast tumours, and the results were consistent across all three platforms. Gene classification and biological pathway analyses of the genes differentially expressed between the two main subtypes revealed different molecular mechanisms descriptive of the two expression-based subtypes: Signature genes of the luminal A subtype were over-represented by genes involved in fatty acid metabolism and steroid hormone-mediated signaling pathways, in particular estrogen receptor signaling, while signature genes of the basal-like subtype were over-represented by genes involved in cell proliferation and differentiation, p21-mediated pathway, and G1-S checkpoint of cell cycle-signaling pathways. A minimal set of 54 genes that best discriminated the two subtypes was identified using the combined data sets generated from the three different array platforms. These predictor genes were further verified by TaqMan Gene Expression assays. CONCLUSION: We have identified and validated the two main previously defined clinically relevant subtypes, luminal A and basal-like, in a small set of early stage breast carcinomas. Signature genes characterizing these two subtypes revealed that distinct molecular mechanisms might have been pre-programmed at an early stage in different subtypes of the disease. Our results provide further evidence that these breast tumor subtypes represent biologically distinct disease entities and may require different therapeutic strategies. Finally, validated by multiple gene expression platforms, including quantitative PCR, the set of 54 predictor genes identified in this study may define potential prognostic molecular markers for breast cancer.

Evaluation of MetriGenix custom 4D arrays applied for detection of breast cancer subtypes.

BACKGROUND: Previously, a total of five breast cancer subtypes have been identified based on variation in gene expression patterns. These expression profiles were also shown to be associated with different prognostic value. In this study tumour samples from 27 breast cancer patients, previously subtyped by expression analysis using DNA microarrays, and four controls from normal breast tissue were included. A new MetriGenix 4D array proposed for diagnostic use was evaluated. METHODS: We applied MetriGenix custom 4D arrays for the detection of previously defined molecular subtypes of breast cancer. MetriGenix 4D arrays have special features including probe immobilization in microchannels with chemiluminescence detection that enable shorter hybridization time. RESULTS: The MetriGenix 4D array platform was evaluated with respect to both the accuracy in classifying the samples as well as the performance of the system itself. In a cross validation analysis using "Nearest Shrunken Centroid classifier" and the PAM software, 77% of the samples were classified correctly according to earlier classification results. CONCLUSION: The system shows potential for fast screening; however, improvements are needed.

Mutation screening of the TP53 gene by temporal temperature gradient gel electrophoresis.

A protocol for detection of mutations in the TP53 gene using temporal temperature gradient gel electrophoresis (TTGE) is described. TTGE is a mutation detection technique that separates DNA fragments differing by single base pairs according to their melting properties in a denaturing gel. It is based on constant denaturing conditions in the gel combined with a temperature gradient during the electrophoretic run. This method combines some of the advantages of the related techniques denaturing gradient gel electrophoresis (DGGE) and constant denaturant gel electrophoresis (CDGE) and eliminates some of the problems. The result is a rapid and sensitive screening technique that is robust and easily set up in smaller laboratory environments.

TP53 gene mutations predict the response to neoadjuvant treatment with 5-fluorouracil and mitomycin in locally advanced breast cancer.

PURPOSE: Recent studies have found an association between certain TP53 mutations and resistance to anthracycline-based primary medical therapy in breast cancer. The purpose of this study was to investigate whether TP53 mutational status also might influence the response to a non-anthracycline-containing regimen in primary breast cancer. EXPERIMENTAL DESIGN: Thirty-five patients with locally advanced breast cancer were investigated for TP53 mutations before receiving combination chemotherapy with 5-fluorouracil (1000 mg/m(2) on days 1 and 2) and mitomycin (6 mg/m(2) on day 2), administered every 3 weeks for 2-10 cycles in the neoadjuvant setting. RESULTS: Mutations in the TP53 gene, in particular those affecting loop domains L2 or L3 of the p53 protein, were associated with lack of response to chemotherapy (i.e., increase in the diameter product of tumor lesion by >/=25%; P = 0.177 for all mutations and P = 0.006 for those affecting L2/L3 domains, respectively). No statistically significant correlation between TP53 LOH and response to therapy was seen. CONCLUSION: This study revealed a significant association between lack of response to 5-fluorouracil and mitomycin and mutations affecting the L2/L3 domains of the p53 protein. Together with our previous finding that such mutations predict resistance to weekly doxorubicin, our data suggest that mutations affecting this particular domain of the p53 protein may cause resistance to several different cytotoxic compounds applied in breast cancer treatment.

Repeated observation of breast tumor subtypes in independent gene expression data sets.

Characteristic patterns of gene expression measured by DNA microarrays have been used to classify tumors into clinically relevant subgroups. In this study, we have refined the previously defined subtypes of breast tumors that could be distinguished by their distinct patterns of gene expression. A total of 115 malignant breast tumors were analyzed by hierarchical clustering based on patterns of expression of 534 "intrinsic" genes and shown to subdivide into one basal-like, one ERBB2-overexpressing, two luminal-like, and one normal breast tissue-like subgroup. The genes used for classification were selected based on their similar expression levels between pairs of consecutive samples taken from the same tumor separated by 15 weeks of neoadjuvant treatment. Similar cluster analyses of two published, independent data sets representing different patient cohorts from different laboratories, uncovered some of the same breast cancer subtypes. In the one data set that included information on time to development of distant metastasis, subtypes were associated with significant differences in this clinical feature. By including a group of tumors from BRCA1 carriers in the analysis, we found that this genotype predisposes to the basal tumor subtype. Our results strongly support the idea that many of these breast tumor subtypes represent biologically distinct disease entities.