Quantitative RNA assessment and long-term stability in the FFPE tumor samples using Digital Spatial Profiler.

Background: Long-term storage of tissue slides has been reported to induce reduced biomarker (e.g. proteins and messenger RNA) detection. This study aimed to evaluate the impact of long-term storage time (0, 16, 24 and 36 weeks) and treatment conditions (non-paraffin and paraffin dipping) at 4°C on RNA quality in formalin-fixed and paraffin-embedded (FFPE) tissue sections. Materials and methods: NanoString GeoMx Digital Spatial Profiling (DSP), a novel platform that allows spatial profiling, was used to profile RNA in human bladder cancer FFPE tissue sections. Results: We observed excellent consistency of quantitative DSP RNA counts of all targets between two different treatment conditions (R > 0.97, Pearson correlation) at each time point and among all four different storage time points (R > 0.96, Pearson correlation) within each treatment condition. No significant difference was observed in the percentage of target genes with sufficient signal across two different treatment conditions at any time point (0 week, P = 0.96; 16 weeks, P = 0.76; 24 weeks, P = 0.96; 36 weeks, P = 0.76, Kolmogorov-Smirnov test) and across all four different storage time points (P > 0.05, Kolmogorov-Smirnov test) in either treatment condition. Conclusion: Although both treatment conditions provided similar results in terms of count reproducibility and signal preservation, we recommend paraffin dipping to generate reproducible RNA results and optimize sample storage. Technology behind the NanoString GeoMx DSP platform shows a robust and reproducible RNA signal from multiple targets in the FFPE tissue sections stored at 4°C for at least up to 36 weeks.

Pharmacogenomics of insulin-like growth factor-I generation during GH treatment in children with GH deficiency or Turner syndrome.

Individual responses to growth hormone (GH) treatment are variable. Short-term generation of insulin-like growth factor-I (IGF-I) is recognized as a potential marker of sensitivity to GH treatment. This prospective, phase IV study used an integrated genomic analysis to identify markers associated with 1-month change in IGF-I (ΔIGF-I) following initiation of recombinant human (r-h)GH therapy in treatment-naïve children with GH deficiency (GHD) (n=166) or Turner syndrome (TS) (n=147). In both GHD and TS, polymorphisms in the cell-cycle regulator CDK4 were associated with 1-month ΔIGF-I (P<0.05). Baseline gene expression was also correlated with 1-month ΔIGF-I in both GHD and TS (r=0.3; P<0.01). In patients with low IGF-I responses, carriage of specific CDK4 alleles was associated with MAPK and glucocorticoid receptor signaling in GHD, and with p53 and Wnt signaling pathways in TS. Understanding the relationship between genomic markers and early changes in IGF-I may allow development of strategies to rapidly individualize r-hGH dose.

A pharmacogenomic approach to the treatment of children with GH deficiency or Turner syndrome.

OBJECTIVE: Individual sensitivity to recombinant human GH (r-hGH) is variable. Identification of genetic factors contributing to this variability has potential use for individualization of treatment. The objective of this study was to identify genetic markers and gene expression profiles associated with growth response on r-hGH therapy in treatment-naïve, prepubertal children with GH deficiency (GHD) or Turner syndrome (TS). DESIGN: A prospective, multicenter, international, open-label pharmacogenomic study. METHODS: The associations of genotypes in 103 growth- and metabolism-related genes and baseline gene expression profiles with growth response to r-hGH (cm/year) over the first year were evaluated. Genotype associations were assessed with growth response as a continuous variable and as a categorical variable divided into quartiles. RESULTS: Eleven genes in GHD and ten in TS, with two overlapping between conditions, were significantly associated with growth response either as a continuous variable (seven in GHD, two in TS) or as a categorical variable (four more in GHD, eight more in TS). For example, in GHD, GRB10 was associated with high response (≥ Q3; P=0.0012), while SOS2 was associated with low response (≤ Q1; P=0.006), while in TS, LHX4 was associated with high response (P=0.0003) and PTPN1 with low response (P=0.0037). Differences in expression were identified for one of the growth response-associated genes in GHD (AKT1) and for two in TS (KRAS and MYOD1). CONCLUSIONS: Carriage of specific growth-related genetic markers is associated with growth response in GHD and TS. These findings indicate that pharmacogenomics could have a role in individualized management of childhood growth disorders.

Functional genetic polymorphisms and female reproductive disorders: part II--endometriosis.

BACKGROUND: Endometriosis has a strong genetic component, and numerous genetic studies have been reported. METHODS: We have systematically reviewed these studies and included 114 in our final selection. RESULTS: We found no consistent evidence linking endometriosis with specific polymorphisms in genes encoding inflammatory mediators, proteins involved in sex steroid metabolism, vascular function and tissue remodelling. Although a number of polymorphisms have been associated with endometriosis in selected populations, the associations have not been independently confirmed, either because only single studies were carried out on these markers/genes or because other studies reported no association. The most solid evidence linking specific polymorphisms to endometriosis came from studies investigating glutathione-S-transferase, a phase II detoxification enzyme. Carriage of the GSTT1 null deletion variant showed consistent association with endometriosis with a 29% increased risk; however, it cannot be excluded that this result was due to publication bias, and this association should be independently confirmed in large-scale, well-designed case-control studies. CONCLUSIONS: The evidence of an association between genetic polymorphisms and endometriosis is weak. Carriage of the GSTT1 null deletion may moderately increase the risk of this disease. We suggest that the methodology of association studies should be improved in order to identify and validate associations in endometriosis.

Functional genetic polymorphisms and female reproductive disorders: Part I: Polycystic ovary syndrome and ovarian response.

BACKGROUND: The identification of polymorphisms associated with a disease can help to elucidate its pathogenesis, and this knowledge can be used to improve prognosis for women with a particular disorder, such as polycystic ovary syndrome (PCOS). Since an altered response to ovarian stimulation is also a characteristic of the disease, further knowledge about its aetiology could help in defining the parameters that determine the response of an individual to ovarian stimulation. METHODS: PubMed and EMBASE databases were systematically searched for gene association studies published until the end of August 2007, using search criteria relevant to PCOS and ovarian response to stimulation. Data from additional papers identified through hand searches were also included; 139 publications were reviewed. RESULTS: Several genes involved in ovarian function and metabolism are associated with increased susceptibility to PCOS, but none is strong enough to correlate alone with susceptibility to the disease, or response to therapy. A single-nucleotide polymorphism in exon 10 of the FSH receptor (FSHR) gene, FSHR p.N680S, was consistently identified as having a significant association with ovarian response to FSH. CONCLUSIONS: No consistent association between gene polymorphism and PCOS could be identified. The FSHR gene may play a significant role in the success of ovarian stimulation, and can be used as a marker to predict differences in FSHR function and ovarian response to FSH. Genotyping the FSHR p.N680S polymorphism may provide a means of identifying a population of poor responders before in vitro fertilization procedures are initiated.

New advances in pharmacogenomics.

In the years to come, pharmacogenomics will make an impact on the development of new drugs. Numerous publications have shown that gene polymorphism can influence drug toxicity and/or efficacy. In order to improve current applications of pharmacogenomics, some technical limitations regarding marker generation, genotyping and biostatistical analysis are being overcome.