Evidence for methane and ammonia in the coma of comet P/Halley

Methane and ammonia abundances in the coma of Halley are derived from Giotto IMS data using an Eulerian model of chemical and physical processes inside the contact surface to simulate Giotto HIS ion mass spectral data for mass-to-charge ratios (m/q) from 15 to 19. The ratio m/q = 19/18 as a function of distance from the nucleus is not reproduced by a model for a pure water coma. It is necessary to include the presence of NH_3 , and uniquely NH_3 , in coma gases in order to explain the data. A ratio of production rates Q(NH_3)/Q(H20) = 0.01-Q.02 results in model values approximating the Giotto data. Methane is identified as the most probable source of the distinct peak at m/q = 15. The observations are fit best with Q(CH_4)/Q(H_20) = 0.02. The chemical composition of the comet nucleus implied by these production rate ratios is unlike that of the outer planets. On the other hand, there are also significant differences from observations of gas phase interstellar material

Intratumoral heterogeneity of microRNA expression in rectal cancer

Introduction: An increasing number of studies have investigated microRNAs (miRNAs) as potential markers of diagnosis, treatment and prognosis. So far, agreement between studies has been minimal, which may in part be explained by intratumoral heterogeneity of miRNA expression. The aim of the present study was to assess the heterogeneity of a panel of selected miRNAs in rectal cancer, using two different technical approaches. Materials and Methods: The expression of the investigated miRNAs was analysed by real-time quantitative polymerase chain reaction (RT-qPCR) and in situ hybridization (ISH) in tumour specimens from 27 patients with T3-4 rectal cancer. From each tumour, tissue from three different luminal localisations was examined. Inter- and intra-patient variability was assessed by calculating intraclass correlation coefficients (ICCs). Correlations between RT-qPCR and ISH were evaluated using Spearman's correlation. Results: ICCsingle (one sample from each patient) was higher than 50% for miRNA-21 and miRNA-31. For miRNA-125b, miRNA-145, and miRNA-630, ICCsingle was lower than 50%. The ICCmean (mean of three samples from each patient) was higher than 50% for miRNA-21(RT-qPCR and ISH), miRNA-125b (RT-qPCR and ISH), miRNA-145 (ISH), miRNA-630 (RT-qPCR), and miRNA-31 (RT-qPCR). For miRNA-145 (RT-qPCR) and miRNA-630 (ISH), ICCmean was lower than 50%. Spearman correlation coefficients, comparing results obtained by RT-qPCR and ISH, respectively, ranged from 0.084 to 0.325 for the mean value from each patient, and from -0.085 to 0.515 in the section including the deepest part of the tumour. Conclusion: Intratumoral heterogeneity may influence the measurement of miRNA expression and consequently the number of samples needed for representative estimates. Our findings with two different methods suggest that one sample is sufficient for adequate assessment of miRNA-21 and miRNA-31, whereas more samples would improve the assessment of miRNA-125b, miRNA-145, and miRNA-630. Interestingly, we found a poor correlation between the expression estimates obtained by RT-qPCR and ISH, respectively

Monocyte chemoattractant protein-1/CCL2 produced by stromal cells promotes lung metastasis of 4T1 murine breast cancer cells

MCP-1/CCL2 plays an important role in the initiation and progression of cancer. Since tumor cells produce MCP-1, they are considered to be the main source of this chemokine. Here, we examined whether MCP-1 produced by non-tumor cells affects the growth and lung metastasis of 4T1 breast cancer cells by transplanting them into the mammary pad of WT or MCP-1−/− mice. Primary tumors at the injected site grew similarly in both mice; however, lung metastases were markedly reduced in MCP-1−/− mice, with significantly longer mouse survival. High levels of MCP-1 mRNA were detected in tumors growing in WT, but not MCP-1−/− mice. Serum MCP-1 levels were increased in tumor-bearing WT, but not MCP-1−/− mice. Transplantation of MCP-1−/− bone marrow cells into WT mice did not alter the incidence of lung metastasis, whereas transplantation of WT bone marrow cells into MCP-1−/− mice increased lung metastasis. The primary tumors of MCP-1−/− mice consistently developed necrosis earlier than those of WT mice and showed decreased infiltration by macrophages and reduced angiogenesis. Interestingly, 4T1 cells that metastasized to the lung constitutively expressed elevated levels of MCP-1, and intravenous injection of 4T1 cells producing a high level of MCP-1 resulted in increased tumor foci in the lung of WT and MCP-1−/− mice. Thus, stromal cell-derived MCP-1 in the primary tumors promotes lung metastasis of 4T1 cells, but tumor cell-derived MCP-1 can also contribute once tumor cells enter the circulation. A greater understanding of the source and role of this chemokine may lead to novel strategies for cancer treatment

Patterns of basal signaling heterogeneity can distinguish cellular populations with different drug sensitivities

Non small cell lung cancer H460 clones exhibit a high degree of heterogeneity in signaling states.Clones with similar patterns of basal signaling heterogeneity have similar paclitaxel sensitivities.Models of signaling heterogeneity among the clones can be used to classify sensitivity to paclitaxel for other cancer populations

From normal cell types to malignant phenotypes

The phenotypic diversity of breast cancer has been proposed to result from different target cell types undergoing oncogenic transformation and giving rise to cancer stem cells. Global gene expression profiling revealed distinct molecular phenotypes and some of these signatures were held to reflect the cell of origin, with the basal carcinomas arising from basal/progenitor cells. Recent work challenges this view by providing evidence that luminal precursor cells are involved in the pathogenesis of basal breast cancers and has made new links between normal cell populations and molecular tumor phenotypes

Tumor heterogeneity in neoplasms of breast, colon, and skin

<p>Abstract</p> <p>Background</p> <p>Different cell subpopulations in a single tumor may show diverse capacities for growth, differentiation, metastasis formation, and sensitivity to treatments. Thus, heterogeneity is an important feature of tumors. However, due to limitations in experimental and analytical techniques, tumor heterogeneity has rarely been studied in detail.</p> <p>Presentation of the hypothesis</p> <p>Different tumor types have different heterogeneity patterns, thus heterogeneity could be a characteristic feature of a particular tumor type.</p> <p>Testing the hypothesis</p> <p>We applied our previously published mathematical heterogeneity model to decipher tumor heterogeneity through the analysis of genetic copy number aberrations revealed by array CGH data for tumors of three different tissues: breast, colon, and skin. The model estimates the number of subpopulations present in each tumor. The analysis confirms that different tumor types have different heterogeneity patterns. Computationally derived genomic copy number profiles from each subpopulation have also been analyzed and discussed with reference to the multiple hypothetical relationships between subpopulations in origin-related samples.</p> <p>Implications of the hypothesis</p> <p>Our observations imply that tumor heterogeneity could be seen as an independent parameter for determining the characteristics of tumors. In the context of more comprehensive usage of array CGH or genome sequencing in a clinical setting our study provides a new way to realize the full potential of tumor genetic analysis.</p

HUNK phosphorylates EGFR to regulate breast cancer metastasis

Epidermal growth factor receptor (EGFR) is commonly over-expressed in metastatic breast cancer yet metastatic breast cancer is generally resistant to anti-EGFR therapies, and the mechanism for resistance to EGFR inhibitors in this setting is not fully understood. Hormonally up-regulated neu-associated kinase (HUNK) kinase is up-regulated in aggressive breast cancers and is thought to play a role in breast cancer metastasis. However, no studies have been conducted to examine a relationship between EGFR and HUNK in breast cancer metastasis. We performed a kinase substrate screen and identified that EGFR is phosphorylated by HUNK. Our studies show that HUNK phosphorylates EGFR at T654, enhancing receptor stability and downstream signaling. We found that increased phosphorylation of T654 EGFR correlates with increased epithelial to mesenchymal, migration and invasion, and metastasis. In addition, we found that HUNK expression correlates with overall survival and distant metastasis free survival. This study shows that HUNK directly phosphorylates EGFR at T654 to promote metastasis and is the first study to show that the phosphorylation of this site in EGFR regulates metastasis

Current measures of metabolic heterogeneity within cervical cancer do not predict disease outcome

<p>Abstract</p> <p>Background</p> <p>A previous study evaluated the intra-tumoral heterogeneity observed in the uptake of F-18 fluorodeoxyglucose (FDG) in pre-treatment positron emission tomography (PET) scans of cancers of the uterine cervix as an indicator of disease outcome. This was done via a novel statistic which ostensibly measured the spatial variations in intra-tumoral metabolic activity. In this work, we argue that statistic is intrinsically <it>non</it>-spatial, and that the apparent delineation between unsuccessfully- and successfully-treated patient groups via that statistic is spurious.</p> <p>Methods</p> <p>We first offer a straightforward mathematical demonstration of our argument. Next, we recapitulate an assiduous re-analysis of the originally published data which was derived from FDG-PET imagery. Finally, we present the results of a principal component analysis of FDG-PET images similar to those previously analyzed.</p> <p>Results</p> <p>We find that the previously published measure of intra-tumoral heterogeneity is intrinsically non-spatial, and actually is only a surrogate for tumor volume. We also find that an optimized linear combination of more canonical heterogeneity quantifiers does not predict disease outcome.</p> <p>Conclusions</p> <p>Current measures of intra-tumoral metabolic activity are not predictive of disease outcome as has been claimed previously. The implications of this finding are: clinical categorization of patients based upon these statistics is invalid; more sophisticated, and perhaps innately-geometric, quantifications of metabolic activity are required for predicting disease outcome.</p