Molecular subtyping of breast cancer: Opportunities for new therapeutic approaches

Evidence is accumulating that breast cancer is not one disease but many separate diseases. DNA microarray-based gene expression profiling has demonstrated subtypes with distinct phenotypic features and clinical responses. Prominent among the new subtypes is 'basal-like' breast cancer, one of the 'intrinsic' subtypes defined by negativity for the estrogen, progesterone, and HER2/neu receptors and positivity for cytokeratins-5/6. Focusing on basal-like breast cancer, we discuss how molecular technologies provide new chemotherapy targets, optimising treatment whilst sparing patients from un-necessary toxicity. Clinical trials are needed that incorporate long-term follow-up of patients with well-characterised tumour markers. Whilst the absence of an obvious dominant oncogene driving basallike breast cancer and the lack of specific therapeutic agents are serious stumbling blocks, this review will highlight several promising therapeutic candidates currently under evaluation. Thus, new molecular technologies should provide a fundamental foundation for better understanding breast and other cancers which may be exploited to save lives.

Development and Validation of a PulseNet Standardized Protocol for Subtyping Isolates of Cronobacter Species

Cronobacter (formerly known as Enterobacter sakazakii) is a genus comprising seven species regarded as opportunistic pathogens that can be found in a wide variety of environments and foods, including powdered infant formula (PIF). Cronobacter sakazakii, the major species of this genus, has been epidemiologically linked to cases of bacteremia, meningitis in neonates, and necrotizing enterocolitis, and contaminated PIF has been identified as an important source of infection. Robust and reproducible subtyping methods are required to aid in the detection and investigation, of foodborne outbreaks. In this study, a pulsed-field gel electrophoresis (PFGE) protocol was developed and validated for subtyping Cronobacter species. It was derived from an existing modified PulseNet protocol, wherein XbaI and SpeI were the primary and secondary restriction enzymes used, generating an average of 14.7 and 20.3 bands, respectively. The PFGE method developed was both reproducible and discriminatory for subtyping Cronobacter species.

Molecular subtyping of primary prostate cancer reveals specific and shared target genes of different ETS rearrangements

This work aimed to evaluate whether ETS transcription factors frequently involved in rearrangements in prostate carcinomas (PCa), namely ERG and ETV1, regulate specific or shared target genes. We performed differential expression analysis on nine normal prostate tissues and 50 PCa enriched for different ETS rearrangements using exon-level expression microarrays, followed by in vitro validation using cell line models. We found specific deregulation of 57 genes in ERG-positive PCa and 15 genes in ETV1-positive PCa, whereas deregulation of 27 genes was shared in both tumor subtypes. We further showed that the expression of seven tumor-associated ERG target genes (PLA1A, CACNA1D, ATP8A2, HLA-DMB, PDE3B, TDRD1, and TMBIM1) and two tumor-associated ETV1 target genes (FKBP10 and GLYATL2) was significantly affected by specific ETS silencing in VCaP and LNCaP cell line models, respectively, whereas the expression of three candidate ERG and ETV1 shared targets (GRPR, KCNH8, and TMEM45B) was significantly affected by silencing of either ETS. Interestingly, we demonstrate that the expression of TDRD1, the topmost overexpressed gene of our list of ERG-specific candidate targets, is inversely correlated with the methylation levels of a CpG island found at -66 bp of the transcription start site in PCa and that TDRD1 expression is regulated by direct binding of ERG to the CpG island in VCaP cells. We conclude that ETS transcription factors regulate specific and shared target genes and that TDRD1, FKBP10, and GRPR are promising therapeutic targets and can serve as diagnostic markers for molecular subtypes of PCa harboring specific fusion gene rearrangements.

Predicting conversion to dementia in a memory clinic: A standard clinical approach compared with an empirically defined clustering method (latent profile analysis) for mild cognitive impairment subtyping

Introduction
Mild cognitive impairment (MCI) has clinical value in its ability to predict later dementia. A better understanding of cognitive profiles can further help delineate who is most at risk of conversion to dementia. We aimed to (1) examine to what extent the usual MCI subtyping using core criteria corresponds to empirically defined clusters of patients (latent profile analysis [LPA] of continuous neuropsychological data) and (2) compare the two methods of subtyping memory clinic participants in their prediction of conversion to dementia.

Methods
Memory clinic participants (MCI, n = 139) and age-matched controls (n = 98) were recruited. Participants had a full cognitive assessment, and results were grouped (1) according to traditional MCI subtypes and (2) using LPA. MCI participants were followed over approximately 2 years after their initial assessment to monitor for conversion to dementia.

Results
Groups were well matched for age and education. Controls performed significantly better than MCI participants on all cognitive measures. With the traditional analysis, most MCI participants were in the amnestic multidomain subgroup (46.8%) and this group was most at risk of conversion to dementia (63%). From the LPA, a three-profile solution fit the data best. Profile 3 was the largest group (40.3%), the most cognitively impaired, and most at risk of conversion to dementia (68% of the group).

Discussion
LPA provides a useful adjunct in delineating MCI participants most at risk of conversion to dementia and adds confidence to standard categories of clinical inference.

Microarray-based classifiers and prognosis models identify subgroups with distinct clinical outcomes and high risk of AML transformation of myelodysplastic syndrome

The diagnosis of myelodysplastic syndrome (MDS) currently relies primarily on the morphologic assessment of the patient's bone marrow and peripheral blood cells. Moreover, prognostic scoring systems rely on observer-dependent assessments of blast percentage and dysplasia. Gene expression profiling could enhance current diagnostic and prognostic systems by providing a set of standardized, objective gene signatures. Within the Microarray Innovations in LEukemia study, a diagnostic classification model was investigated to distinguish the distinct subclasses of pediatric and adult leukemia, as well as MDS. Overall, the accuracy of the diagnostic classification model for subtyping leukemia was approximately 93%, but this was not reflected for the MDS samples giving only approximately 50% accuracy. Discordant samples of MDS were classified either into acute myeloid leukemia (AML) or

Allele resolution of HLA-A using oligonucleotide probes in a two-stage typing strategy

High-resolution polymerase chain reaction using sequence-specific oligonucleotide probes (PCR-SSOP) typing methods for HLA-A identification have been established. The four systems, which operate independently of each other, are intended for use as secondary typing systems following HLA-A identification with a medium-resolution PCR-SSOP technique. The systems, all using digoxigenin-labelled probes, are based on group specific amplifications for resolution of: i) HLA-A*29 & -A*33; ii) HLA-A*24 & -A*30; and iii) HLA-A*26, -A*25, -A*11, -A*34, -A*66 and -A*68 alleles, respectively. The fourth system, for the detection of HLA-A*02 alleles, is a modification of a previously reported PCR-SSOP subtyping system. The methods have been applied to individuals from the local bone marrow registry and HLA-A allele frequencies for the Northern Ireland population have been established.

Mathematics and reading difficulty subtypes: Minor phonological influences on mathematics for 5-7 year olds

Linguistic influences in mathematics have previously been explored throughsubtyping methodology and by taking advantage of the componential nature ofmathematics and variations in language requirements that exist across tasks. Thepresent longitudinal investigation aimed to examine the language requirements of mathematical tasks in young children aged 5-7 years. Initially, 256 children were screened for mathematics and reading difficulties using standardised measures. Those scoring at or below the 35th percentile on either dimension were classified as having difficulty. From this screening, 115 children were allocated to each of the MD (n=26), MDRD (n=32), reading difficulty (RD, n=22) and typically achieving (TA, n=35) subtypes. These children were tested at four time points, separated by six monthly intervals, on a battery of seven mathematical tasks. Growth curve analysis indicated that, in contrast to previous research on older children, young children with MD and MDRD had very similar patterns of development on all mathematical tasks. Overall, the subtype comparisons suggested that language played only a minor mediating role in most tasks, and this was secondary in importance to non-verbal skills. Correlational evidence suggested that children from the different subtypescould have been using different mixes of verbal and non-verbal strategies to solve the mathematical problems.

Stratified analysis reveals chemokine-like factor (CKLF) as a potential prognostic marker in the MSI-immune consensus molecular subtype CMS1 of colorectal cancer

The Colorectal Cancer (CRC) Subtyping Consortium (CRCSC) recently published four consensus molecular subtypes (CMS’s) representing the underlying biology in CRC. The Microsatellite Instable (MSI) immune group, CMS1, has a favorable prognosis in early stage disease, but paradoxically has the worst prognosis following relapse, suggesting the presence of factors enabling neoplastic cells to circumvent this immune response. To identify the genes influencing subsequent poor prognosis in CMS1, we analyzed this subtype, centered on risk of relapse.
In a cohort of early stage colon cancer (n=460), we examined, in silico, changes in gene expression within the CMS1 subtype and demonstrated for the first time the favorable prognostic value of chemokine-like factor (CKLF) gene expression in the adjuvant disease setting [HR=0.18, CI=0.04-0.89]. In addition, using transcription profiles originating from cell sorted CRC tumors, we delineated the source of CKLF transcription within the colorectal tumor microenvironment to the leukocyte component of these tumors. Further to this, we confirmed that CKLF gene expression is confined to distinct immune subsets in whole blood samples and primary cell lines, highlighting CKLF as a potential immune cell-derived factor promoting tumor immune-surveillance of nascent neoplastic cells, particularly in CMS1 tumors. Building on the recently reported CRCSC data, we provide compelling evidence that leukocyte-infiltrate derived CKLF expression is a candidate biomarker of favorable prognosis, specifically in MSI-immune stage II/III disease.