Immunohistochemistry in the evaluation of neovascularization in tumor xenografts

Angiogenesis, or neovascularization, is known to play an important role in the neoplastic progression leading to metastasis. CD31 or Factor VIII-related antigen (F VIII RAg) immunohistochemistry is widely used in experimental studies for quantifying tumor neovascularization in immunocompromised animal models implanted with transformed human cell lines. Quantification, however, can be affected by variations in the methodology used to measure vascularization including antibody selection, antigen retrieval (AR) pretreatment, and evaluation techniques. To examine this further, we investigated the microvessel density (MVD) and the intensity of microvascular staining among five different human tumor xenografts and a mouse syngeneic tumor using anti-CD31 and F VIII RAg immunohistochemical staining. Different AR methods also were evaluated. Maximal retrieval of CD31 was achieved using 0.5 M Tris (pH 10) buffer, while maximum retrieval of F VIII RAg was achieved using 0.05% pepsin treatment of tissue sections. For each optimized retrieval condition, anti-CD31 highlighted small vessels better than F VIII RAg. Furthermore, the MVD of CD31 was significantly greater than that of F VIII RAg decorated vessels (p<0.001). The choice of antibody and AR method has a significant affect on immunohistochemical findings when studying angiogenesis. One also must use caution when comparing studies in the literature that use different techniques and reagents.     

Cytofluorometric analyses of human T cell CD2/CD4 inter-molecular interactions

Incubation of human T lymphocytes with saturating concentrations of combinations of certain anti-CD2 and -CD4 mAb results in reciprocal down-regulation of the cell surface density expression of the respective CD molecules. Such reciprocal down-regulation occurs at 0 degrees C in the presence of sodium azide and appears selective for CD2 and CD4 molecules because mAb identifying various other CD T cell surface molecules (anti-Leu2a, -OK-CLL, -W6/32, -beta 2-microglobulin, -4B4) do not modulate CD2 or CD4 R density, and because anti-CD2 mAb (anti-OKT11 and -D66 clone-1) do not alter CD8 R density (anti-OKT8, -Leu2a) and vice versa. Down-regulation of CD2 by mAb specific to CD4 is epitope-specific but does not vary on the basis of the antibody isotype used. The anti-CD4 mAb, Leu3a, was the strongest CD2 down-regulator examined followed by OKT4F. mAb specific to other CD4 epitopes (B, C, D, and E) caused only slight down-regulation of CD2 expression whereas anti-OKT4 and -OKT4A mAb had no significant regulatory effect. Also, mAb specific to the 9.6 (anti-OKT11) and D66 (anti-D66 clone 1) epitopes of the CD2 molecule down-regulated CD4 density detectable with Leu3a, OKT4, and OKT4A anti-CD4 mAb. Down-regulation of CD2 by anti-CD4 mAb also occurred with the transformed T cell line, KE-37, which demonstrates that such effects can occur without mononuclear phagocytic accessory cells. From these data it can be concluded that important T cell immunoregulatory signals may be transmitted intramembranally between CD2 and CD4 glycoproteins.     

Comparing and linking machine learning and semi-mechanistic models for the predictability of endemic measles dynamics

Measles is one the best-documented and most-mechanistically-studied non-linear infectious disease dynamical systems. However, systematic investigation into the comparative performance of traditional mechanistic models and machine learning approaches in forecasting the transmission dynamics of this pathogen are still rare. Here, we compare one of the most widely used semi-mechanistic models for measles (TSIR) with a commonly used machine learning approach (LASSO), comparing performance and limits in predicting short to long term outbreak trajectories and seasonality for both regular and less regular measles outbreaks in England and Wales (E&W) and the United States. First, our results indicate that the proposed LASSO model can efficiently use data from multiple major cities and achieve similar short-to-medium term forecasting performance to semi-mechanistic models for E&W epidemics. Second, interestingly, the LASSO model also captures annual to biennial bifurcation of measles epidemics in E&W caused by susceptible response to the late 1940s baby boom. LASSO may also outperform TSIR for predicting less-regular dynamics such as those observed in major cities in US between 1932–45. Although both approaches capture short-term forecasts, accuracy suffers for both methods as we attempt longer-term predictions in highly irregular, post-vaccination outbreaks in E&W. Finally, we illustrate that the LASSO model can both qualitatively and quantitatively reconstruct mechanistic assumptions, notably susceptible dynamics, in the TSIR model. Our results characterize the limits of predictability of infectious disease dynamics for strongly immunizing pathogens with both mechanistic and machine learning models, and identify connections between these two approaches.     

SWI/SNF gene variants and glioma risk and outcome

Background: The human SWItch/Sucrose Non-Fermentable (SWI/SNF) chromatin remodeling complex plays essential roles in a variety of cellular processes and has been implicated in human cancer. However, the role of germline genetic variants in this complex in relation to cancer risk is not well studied. Methods: We assessed the association of 16 variants in the catalytic subunits (SMARCA2 and SMARCA4) of the SWI/SNF complex with the risk of glioma subtypes (lower grade astrocytoma, oligodendroglioma and glioblastoma [GBM]) and with mortality from high-grade tumors (GBM) in a multicenter US case–control study that included 561 cases and 574 controls. Associations were estimated with odds ratios (OR, for risk) or hazards ratios (HR, for mortality) with 95% confidence intervals (CI). False discovery rate (FDR-q) was used to control for multiple testing in risk associations. Results: None of the investigated SNPs was associated with overall glioma risk. However, analyses according to histological subtypes revealed a statistically significant increased risk of oligodendroglioma in association with SMARCA2 rs2296212 (OR = 4.05, 95%CI = 1.11–14.80, P = 0.030, q = 0.08) and rs4741651 (OR = 4.68, 95%CI = 1.43–15.30, P = 0.011, q = 0.08) and SMARCA4 rs11672232 (OR = 1.90, 95%CI = 1.01–3.58, P = 0.048, q = 0.08) and rs12232780 (OR = 2.14, 95%CI = 1.06–4.33, P = 0.035, q = 0.08). No significant risk associations were observed for GBM or lower grade astrocytoma. Suggestive associations with GBM mortality were not validated in the Cancer Genome Atlas. Conclusion: Our findings suggest that genetic variants in SMARCA2 and SMARCA4 influence the risk of oligodendroglioma. Further research is warranted on the SWI/SNF complex genes and epigenetic mechanisms more generally in the development of glioma in adults.     

Controlled destabilization of a liposomal drug delivery system enhances mitoxantrone antitumor activity.

Programmable fusogenic vesicles (PFVs) are lipid-based drug-delivery systems that exhibit time-dependent destabilization. The rate at which this destabilization occurs is determined by the exchange rate of a bilayer-stabilizing component, polyethylene glycol-phosphatidylethanolamine (PEG-PE) from the vesicle surface. This exchange rate is controlled, in turn, by the acyl chain composition of the PEG-PE. We describe in vitro and in vivo studies using PFVs as delivery vehicles for the anticancer drug mitoxantrone. We demonstrate that the PEG-PE acyl composition determined the rate at which PFVs are eliminated from plasma after intravenous administration, and the rate of mitoxantrone leakage from PFV. The nature of the PEG-PE component also determined the antitumor efficacy of mitoxantrone-loaded PFV in murine and human in murine and human xenograft tumor models. Increased circulation time and improved activity were obtained for PFV containing PEG-PE with an 18-carbon acyl chain length, as a result of slower vesicle destabilization.     

The ubiquitous nature of the progesterone receptor binding factor-1 (RBF-1) in avian tissues

The avian oviduct receptor binding factor-1 (RBF-1) is a 10 kDa nuclear matrix protein that was originally identified through its ability to effect high affinity interaction of activated progesterone receptor (PR) with chromatin. In the present study, the RBF-1 is shown to not be restricted to reproductive tissues (e.g., oviduct) but present in all avian tissues examined by Western blot analysis with a monoclonal antibody prepared against purified RBF-1. The heart and pancreas had the highest and lowest RBF-1 levels, respectively; the concentration ranging by ～ 50-fold in these tissues. The 10 kDa size of the RBF-1 detected in all tissues suggests no significant tissue-specific differences in the protein. This was consistent with the finding that purified hepatic and oviductal RBF-1 have identical amino-terminal sequence. Using a recently isolated cDNA to RBF-1, the levels of RBF-1 mRNA were found to correlate well with the ubiquitous presence of the protein as well as tissue-specific differences in concentration. The presence of RBF-1 in non-progesterone responsive tissues suggests the possibility that RBF-1 may not be specifically involved in PR-DNA interactions but may play a more diverse role, possibly involving other steroid receptors such as the glucocorticoid receptor. © 1994 Wiley-Liss, Inc.