The internet is an increasingly important source of information for anaesthetists. We sought to determine the extent and patterns of internet use among Australian anaesthetists, and to assess its effect on clinical decision-making. A postal survey of all Australian Fellows of the Australian and New Zealand College of Anaesthetists (n=2344) was performed. The response rate was 48% (n=1109) and 1066 responses were analysed. Ninety-seven per cent of respondents, much higher than the national average of 72%, had internet access at work or home. The majority used the internet at least once a month for patient care, and over 50% had made clinical decisions influenced by information found on the internet. In contrast, less than 20% had had any training in its use. In terms of access, rural Australia did not appear to be disadvantaged.
Background: PIK3CA is the single most commonly mutated gene in breast cancer, with highest incidence reported in ER positive and HER2 negative breast cancer. Substantial data now suggests that breast cancers show intra-tumoural genetic heterogeneity, with apparently clonal tumours composed of multiple populations of tumour cells that, in addition to the founder genetic events common to all cells, harbour private genetic alterations. Tumours with mutations that are sub-clonal may respond less well to therapies targeting these mutations than cancers with clonal mutations. To assess how frequently PIK3CA mutations are clonal founder mutations, or may be subclonal, we assessed the abundance on PIK3CA mutation using digital PCR. Methods: DNA was extracted from frozen sections of 119 primary breast cancers, following macrodissection to achieve tumour cell content of >70%. PIK3CA mutations c.1624G>A (E542K), c.1633G>A (E545K), c.3140A>T (H1047L) and c.3140A>G (H1047R) were assessed by droplet digital PCR on a BioRad QX100 system. Exon 9 mutation assays were optimised to not amplify the PIK3CA pseudogene. Mutational abundance was calculated from the Poisson distribution, expressed as the portion of PIK3CA DNA in the sample that was mutant, and compared between breast cancer subtypes. A mutational abundance of <20% was predefined to represent low abundance mutation, that may be subclonal. Results: PIK3CA mutations were detected with abundance ranging from 80.4% to 0.0063%, with 26 cancers with an abundance >20% and 19 cancers with low abundance <20% (5 cancers with abundance 1-20%, and 14 cancer with abundance <1%). There was highly correlation between repeat experiments r2 = 0.98, p<0.0001, with 100% concordance for low abundance mutations in repeat analysis. High abundance mutations were numerically more common in ER positive HER2 negative cancers (18/65, 28%) than HER2 positive or triple negative (TN) cancers (7/54, 14% p = 0.07 Fishers exact test). Conversely, low abundance mutations were less common in ER positive HER2 negative cancers (4/65, 6%) than in HER2 positive or TN cancers (10/54, 19% p = 0.047). In cancers with a detectable PIK3CA mutation, mutational abundance was higher in ER positive cancer than ER negative cancers (p = 0.023 Mann-Whitney U test), and higher in ER positive HER2 negative cancers compared to HER2 positive or TN cancers (p = 0.0024). In ER positive HER2 negative cancers 82% (18/22) mutations were of high abundance, and likely clonal, whereas in TN or HER2 positive cancers 39% (7/18, p = 0.009) were of high abundance. Conclusion: Our data suggests that hotspot PIK3CA mutations are frequently of low abundance in HER2 positive or TN breast cancer, and may be subclonal. However, we cannot exclude the possibility that these findings represent contamination. If confirmed on an independent data set, our data suggest that identification of mutational abundance may be an important component of PIK3CA mutation assessment and the potential targeting of these mutations with PI3 kinase inhibitors. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P2-08-01.
Background: Germline mutation screening of BRCA1 and BRCA2 genes is performed in suspected familial breast cancer cases, but a causative mutation is found in only 30% of patients. The development of additional methods to identify good candidates for BRCA1 and BRCA2 analysis would therefore increase the efficacy of diagnostic mutation screening. With this in mind, we developed a study to determine molecular signatures of BRCA1—or BRCA2—mutated breast cancers. Materials and Methods: Array-cgh (comparative genomic hybridization) and transcriptomic analysis were performed on a series of 103 familial breast cancers. The series included 7 breast cancers with a BRCA1 mutation and 5 breast cancers with a BRCA2 mutation. The remaining 91 cases were obtained from 73 families selected on the basis of at least 3 affected first-degree relatives or at least 2 affected first-degree relatives with breast cancer at an average age of 45 years. Array-cgh analyses were performed on a 4407 BAC-array (CIT-V8) manufactured by IntegraGen. Transcriptomic analyses were performed using an Affymetrix Human Genome U133 Plus 2.0 chip. Results: Using supervised clustering analyses we identified two transcriptomic signatures: one for BRCA1-mutated breast cancers consisting of 600 probe sets and another for BRCA2-mutated breast cancers also consisting of 600 probes sets. We also defined cgh-array signatures, based on the presence of specific genomic rearrangements, one for BRCA1-mutated breast cancers and one for BRCA2-mutated breast cancers. Conclusions: This study identified molecular signatures of breast cancers with BRCA1 or BRCA2 germline mutations. Genes present in these signatures could be exploited to find new markers for such breast cancers. We also identified specific genomic rearrangements in these breast cancers, which could be screened for in a diagnostic setting using fluorescence in situ hybridization, thus improving patient selection for BRCA1 and BRCA2 molecular genetic analysis.
Aims: HER2 gene amplification is observed in up to 15% of breast carcinomas. In a rare subset of breast cancers classified as HER2-positive by immunohistochemistry and in situ hybridisation, HER2 overexpression and gene amplification are restricted to a subset of >30% but not all cancer cells. Here we sought to characterise the repertoire of gene copy number aberrations and somatic mutations in the HER2-positive and HER2-negative components of cases with heterogeneous HER2 overexpression and gene amplification. Material and methods: Cases diagnosed as HER2 positive but with >30% but <100% of cells displaying HER2 overexpression were retrieved from the authors' institutions. HER2 heterogeneity status was re-assessed using immunohistochemistry and chromogenic and/ or fluorescence in situ hybridisation. For cases with confirmed HER2 gene amplification heterogeneity, HER2-positive and HER2-negative components were microdissected from tissue sections stained with the Herceptest antibody. DNA samples extracted from both components of each case were subjected to microarray-based comparative genomic hybridisation (aCGH), using a 32K BAC array platform with 50Kb resolution. The HER2-positive and HER2-negative components of cases with frozen material were also subjected to massively parallel targeted exome sequencing. Results: Twelve cases yielded sufficient DNA for aCGH analysis. Tumours were preferentially ER positive (83%) and of histological grade 3 (67%). The HER2-positive and HER2-negative components of all cases shared most of the copy number aberrations. A pairwise comparison of the genomic profiles of the two components from each case revealed that in ten of the twelve cases, copy number aberrations in addition to 17q12 amplification encompassing the HER2 gene locus were restricted to one of the two components. Exome sequencing of two cases suggested that the HER2-positive and HER2-negative components from each case harboured >30 somatic mutations in common, including identical TP53 somatic mutations in both components of each case. The HER2-negative component of one of the cases displayed a somatic mutation in NRG2, an ERBB receptor ligand, and the HER2-negative component of the other case harboured a mutation in PTTG1IP, a proto-oncogene with putative oestrogen receptor elements. Conclusions: Our results demonstrate that in HER2-positive breast cancers with heterogeneous HER2 gene amplification, the HER2-positive and HER2-negative components are clonally related. The distinct genomic profiles of HER2-positive and HER2-negative components, however, suggest that, at least in some of these cases, HER2 amplification may constitute a relatively late event in tumour evolution. Exome sequencing revealed mutations restricted to the HER2-negative components of HER2-positive tumours with heterogeneous HER2 overexpression/gene amplification, which may constitute potential drivers in the absence of HER2 overexpression/gene amplification. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr PD05-08.
Background Endocrine resistance is a major clinical issue in breast cancer. Multiple studies provide data relating molecular features of primary tumours on their response to endocrine treatment. However, there is a paucity of data on molecular profiles of breast tumours at time when endocrine resistance has already manifested. Aim Provide multilayer molecular profiling of a series of breast tumours at time of relapse or progression on endocrine treatment. Results High density microarray-comparative genomic hybridisation (aCGH) analysis was performed in 39 endocrine treated breast tumours at time of progression or relapse. 25 of these tumours were also profiled using whole genome mRNA and miRNA arrays (HT-12 mRNA array and DASL Sentrix Array Matrix micro-RNA array, Illumina). Overall the aCGH profiles of resistant tumours were similar to aCGH profiles of un-selected luminal breast tumours and cell lines published earlier. The most common gene copy gain areas (>10M of length) were located in 1q23.1-44, 5p13.1-33, 8p11.23-q24.3, 16p12.1-13.3, 17q21.33-24.1 and 20q13.2-13.33, while the most common losses were in 1p33-36.32, 8p12-23.3, 11q22.3-24.1, 16q12.2-24.3 and 17p11.2-13.3. Unsupervised hierarchical clustering by gene copy number profiles split of the resistant tumours into 3 groups. As expected, on average mRNAs and microRNAs were higher expressed in tumours carrying gains of their genes and were decreased in tumours carrying the losses. Copy number gains in this series were associated with the upregulation of multiple RNAs, including C8orf76, TATDN1, CYC1, ZBTB10, C16orf63, THUMPD1, TFB2M, miR-454, miR-301a and miR-296-3p/5p. Losses were associated with the downregulation of RNAs including ACADVL, BNIP3L, UBE4A, REXO2, ATP5L, PPP2CB, NBL1 and PCM1. Of the above genes UBE4A and ATP5L are closely co-localised in 11q23.3; miR454-miR301a are located closely in 17q22. Such associations suggest that changes in expression of these genes in breast cancers may be driven by alterations in the gene copy number. Analysis of the recurrent amplification regions on this dataset is underway. Conclusions aCGH profiles of endocrine resistant tumours at time of relapse or progression are similar to those found in un-selected and non-treated luminal tumours. The study provides the first dataset integrating gene copy number with mRNA and microRNA gene expressions in clinical specimens of endocrine resistant breast cancers. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P4-02-03.
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