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Posts Tagged ‘Conditions and Diseases’


Disentangling molecular alterations from water-content changes in the aging human brain using quantitative MRI

Reporter: Dror Nir, PhD

 

Authors’ list: Shir Filo, Oshrat Shtangel, Noga Salamon, Adi Kol, Batsheva Weisinger, Sagiv Shifman & Aviv A. Mezer
Published in: Nature Communications volume 10, Article number: 3403 (2019)

Abstract

It is an open question whether aging-related changes throughout the brain are driven by a common factor or result from several distinct molecular mechanisms. Quantitative magnetic resonance imaging (qMRI) provides biophysical parametric measurements allowing for non-invasive mapping of the aging human brain. However, qMRI measurements change in response to both molecular composition and water content. Here, we present a tissue relaxivity approach that disentangles these two tissue components and decodes molecular information from the MRI signal. Our approach enables us to reveal the molecular composition of lipid samples and predict lipidomics measurements of the brain. It produces unique molecular signatures across the brain, which are correlated with specific gene-expression profiles. We uncover region-specific molecular changes associated with brain aging. These changes are independent from other MRI aging markers. Our approach opens the door to a quantitative characterization of the biological sources for aging, that until now was possible only post-mortem.

 

Introduction

The biology of the aging process is complex, and involves various physiological changes throughout cells and tissues1. One of the major changes is atrophy, which can be monitored by measuring macroscale brain volume reduction1,2. In some cases, atrophy can also be detected as localized microscale tissue loss reflected by increased water content3. This process is selective for specific brain regions and is thought to be correlated with cognitive decline in Alzheimer’s disease2,4,5. In addition to atrophy, there are molecular changes associated with the aging of both the normal and pathological brain5,6. Specifically, lipidome changes are observed with age, and are associated with several neurological diseases7,8,9,10,11.

It is an open question as to whether there are general principles that govern the aging process, or whether each system, tissue, or cell deteriorates with age for different reasons12,13. On one hand, the common-cause hypothesis proposes that different biological aging-related changes are the result of a single underlying factor14,15. This implies that various biomarkers of aging will be highly correlated16. On the other hand, the mosaic theory of aging suggests that there are several distinct aging mechanisms that have a heterogenous effect throughout the brain12,13. According to this latter view, combining different measurements of brain tissue is crucial in order to fully describe the state of the aging brain. To test these two competing hypotheses in the context of volumetric and molecular aging-related changes, it is essential to measure different biological aspects of brain tissue. Unfortunately, the molecular correlates of aging are not readily accessible by current in vivo imaging methods.

The main technique used for non-invasive mapping of the aging process in the human brain is magnetic resonance imaging (MRI)2,17,18,19. Advances in the field have led to the development of quantitative MRI (qMRI). This technique provides biophysical parametric measurements that are useful in the investigation and diagnosis of normal and abnormal aging20,21,22,23,24,25,26,27. qMRI parameters have been shown to be sensitive to the microenvironment of brain tissue and are therefore named in vivo histology28,29,30. Nevertheless, an important challenge in applying qMRI measurements is increasing their biological interpretability. It is common to assume that qMRI parameters are sensitive to the myelin fraction20,23,30,31,32,33, yet any brain tissue including myelin is a mixture of multiple lipids and proteins. Moreover, since water protons serve as the source of the MRI signal, the sensitivity of qMRI parameters to different molecular microenvironments may be confounded by their sensitivity to the water content of the tissue34,35. We hypothesized that the changes observed with aging in MRI measurements20,23,30,31,32,33,36 such as R1, R2, mean diffusivity (MD), and magnetization transfer saturation (MTsat)37, could be due to a combination of an increase in water content at the expense of tissue loss, and molecular alterations in the tissue.

Here, we present a qMRI analysis that separately addresses the contribution of changes in molecular composition and water content to brain aging. Disentangling these two factors goes beyond the widely accepted “myelin hypothesis” by increasing the biological specificity of qMRI measurements to the molecular composition of the brain. For this purpose, we generalize the concept of relaxivity, which is defined as the dependency of MR relaxation parameters on the concentration of a contrast agent38. Instead of a contrast agent, our approach exploits the qMRI measurement of the local non-water fraction39 to assess the relaxivity of the brain tissue itself. This approach allows us to decode the molecular composition from the MRI signal. In samples of known composition, our approach provides unique signatures for different brain lipids. In the live human brain, it produces unique molecular signatures for different brain regions. Moreover, these MRI signatures agree with post-mortem measurements of the brain lipid and macromolecular composition, as well as with specific gene-expression profiles. To further validate the sensitivity of the relaxivity signatures to molecular composition, we perform direct comparison of MRI and lipidomics on post-mortem brains. We exploit our approach for multidimensional characterization of aging-related changes that are associated with alterations in the molecular composition of the brain. Finally, we evaluate the spatial pattern of these changes throughout the brain, in order to compare the common-cause and the mosaic theories of aging in vivo.

 

Results

Different brain lipids have unique relaxivity signatures
The aging process in the brain is accompanied by changes in the chemophysical composition, as well as by regional alterations in water content. In order to examine the separate pattern of these changes, we developed a model system. This system was based on lipid samples comprising common brain lipids (phosphatidylcholine, sphingomyelin, phosphatidylserine, phosphatidylcholine-cholesterol, and phosphatidylinositol-phosphatidylcholine)7. Using the model system, we tested whether accounting for the effect of the water content on qMRI parameters provides sensitivity to fine molecular details such as the head groups that distinguish different membrane phospholipids. The non-water fraction of the lipid samples can be estimated by the qMRI measurement of lipid and macromolecular tissue volume (MTV, for full glossary of terms see Supplementary Table 1)39. By varying the concentration of the lipid samples, we could alter their MTV and then examine the effect of this manipulation on qMRI parameters. The parameters we estimated for the lipid samples were R1, R2, and MTsat. The potential ambiguity in the biological interpretation of qMRI parameters is demonstrated in Fig. 1a. On one hand, samples with similar lipid composition can present different R1 measurements (Fig. 1a, points 1 & 2). On the other hand, scanning samples with different lipid compositions may result in similar R1 measurements (Fig. 1a, points 2 & 3). This ambiguity stems from the confounding effect of the water content on the MR relaxation properties.

Screenshot 2019-08-01 at 14.36.20

We evaluated the dependency of different qMRI parameters on the non-water fraction estimated by MTV. This analysis revealed strong linear dependencies (median R2 = 0.74, Fig. 1a, b and Supplementary Fig. 1a, b). These linear MTV dependencies change as a function of the lipid composition, reflecting the inherent relaxivity of the different lipids. We could therefore use the MTV derivatives of qMRI parameters (dqMRIdMTV, i.e., the slope of the linear relationship between each qMRI parameter and MTV) as a measure that is sensitive to molecular composition. By accounting for the Multidimensional Dependency on MTV (“MDM”) of several qMRI parameters, a unique MRI relaxivity signature was revealed for each lipid (Fig. 1c). This implies that the water-related ambiguity demonstrated in the inset of Fig. 1a can be removed by measuring the MTV dependencies (Fig. 1c). Creating mixtures of several lipids provided supportive evidence for the generality of our framework. Figure 1d and Supplementary Fig. 1c show that the qMRI measurements of a mixture can be predicted by summing the MTV dependencies of pure lipids (for further details see Supplementary Note 1 and Supplementary Fig. 2). Furthermore, we used this biophysical model to predict the lipid composition of a mixture from its MDM measurements (Fig. 1e). This model provided a good estimation of the sphingomyelin (Spg) and phosphatidylserine (PS) content (R2 > 0.64) but failed to predict phosphatidylcholine (PtdCho) content (for further details see Supplementary Note 2). While lipids are considered to be a major source of the MRI signal in the brain 40,41,42,43,44,45, our approach can be applied to other compounds to reveal differences in the MRI signal between different proteins, sugars, and ions (Supplementary Fig. 1d). Hence, the relationships between qMRI parameters and MTV account for the effect of water on MRI measurements and could be of use in quantifying the biological and molecular contributions to the MRI signal of water protons.

The tissue relaxivity of the human brain is region-specific.
In order to target age-related changes in molecular composition, we applied the same approach for the human brain (Fig. 2a).

Screenshot 2019-08-01 at 14.41.35

We found that the linear dependency of qMRI parameters on MTV is not limited to in vitro samples and a similar relationship was also evident in the human brain (Fig. 2b and Supplementary Figs. 3–5). Importantly, different brain regions displayed a distinct dependency on MTV. Therefore, the relaxivity of brain tissue is region-specific. Figure 2b provides an example for the regional linear trends of R1 and MTsat in a single subject. Remarkably, while the thalamus and the pallidum presented relatively similar R1 dependencies on MTV, their MTsat dependencies were different (p < 0.001, two-sample t-test). Compared to these two brain regions, frontal white-matter demonstrated different dependencies on MTV (p < 0.001, two-sample t-test). A better separation between brain regions can therefore be achieved by combining the MTV dependencies of several qMRI parameters (MTsat, MD, R1 and R2). The MTV derivatives of qMRI parameters are consistent across subjects (Fig. 2c and Supplementary Fig. 6), with good agreement between hemispheres (Supplementary Fig. 5). Moreover, they provide a novel pattern of differentiation between brain regions, which is not captured by conventional qMRI methods (Supplementary Fig. 7). In our lipid sample experiments, the MDM approach revealed unique relaxivity signatures of different lipids (Fig. 1c). Therefore, we attribute the observed diversity in the MTV derivatives of qMRI parameters across brain regions to the intrinsic heterogeneity in the chemophysical microenvironment of these regions. The multidimensional dependency of various qMRI parameters on MTV can be represented by the space of MTV derivatives to reveal a unique chemophysical MDM signature for different brain regions (Fig. 2d, see explanatory scheme of the MDM method in Supplementary Fig. 8). Fig. 2 figure2 The MDM method provides region-specific signatures in the in vivo human brain. a Representative MTV, MTsat, and R1 maps. b Calculating the MDM signatures. The dependency of R1 (left) and MTsat (right) on MTV in three brain regions of a single subject. For each region, MTV values were pooled into bins (dots are the median of each bin; shaded area is the median absolute deviation), and a linear fit was calculated (colored lines). The slopes of the linear fit represent the MTV derivatives of R1 and MTsat and vary across brain regions. c The reliability of the MDM method across subjects. Variation in the MTV derivatives of R1 (left) and MTsat (right) in young subjects (N = 23). Different colors represent 14 brain regions (see legend). Edges of each box represent the 25th, and 75th percentiles, median is in black, and whiskers extends to extreme data points. Different brain regions show distinct MTV derivatives. d Unique MDM signatures for different brain regions (in different colors). Each axis is the MTV derivative (“MDM measurements”) of a different qMRI parameter (R1, MTsat, R2, and MD). The range of each axis is in the legend. Colored traces extend between the MDM measurements, shaded areas represent the variation across subjects (N = 23). An overlay of all MDM signatures is marked with dashed lines Full size image The in vivo MDM approach captures ex vivo molecular profiles To validate that the MDM signatures relate to the chemophysical composition of brain tissue, we compared them to a previous study that reported the phospholipid composition of the human brain7. First, we established the comparability between the in vivo MRI measurements and the reported post-mortem data. MTV measures the non-water fraction of the tissue, a quantity that is directly related to the total phospholipid content. Indeed, we found good agreement between the in vivo measurement of MTV and the total phospholipid content across brain regions (R2 = 0.95, Fig. 3a). Söderberg et al.7 identified a unique phospholipid composition for different brain regions along with diverse ratios of phospholipids to proteins and cholesterol. We compared this regional molecular variability to the regional variability in the MDM signatures. To capture the main axes of variation, we performed principal component analysis (PCA) on both the molecular composition of the different brain regions and on their MDM signatures. For each of these two analyses, the first principal component (PC) explained >45% of the variance. The regional projection on the first PC of ex vivo molecular composition was highly correlated (R2 = 0.84, Fig. 3b) with the regional projection on the first PC of in vivo MDM signatures. This confirms that brain regions with a similar molecular composition have similar MDM. Supplementary Fig. 9a provides the correlations of individual lipids with MDM. Importantly, neither MTV nor the first PC of standard qMRI parameters was as strongly correlated with the ex vivo molecular composition as the MDM (Supplementary Fig. 9b, c). We next used the MDM measurements as predictors for molecular properties of different brain regions. Following our content predictions for lipids samples (Fig. 1e), we constructed a weighted linear model for human data (for further details see Supplementary Note 3). To avoid over fitting, we reduced the number of fitted parameters by including only the MDM and the molecular features that accounted for most of the regional variability. The MTV derivatives of R1 and MTsat accounted for most of the variance in MDM. Thus, we used these parameters as inputs to the linear model, while adjusting their weights through cross validation. We tested the performance of this model in predicting the three molecular features that account for most of the variance in the ex vivo molecular composition. Remarkably, MRI-driven MDM measurements provided good predictions for the regional sphingomyelin composition (R2 = 0.56, p < 0.05 for the F-test, Fig. 3c) and the regional ratio of phospholipids to proteins (R2 = 0.56, p < 0.05 for the F-test, Fig. 3c).

Screenshot 2019-08-01 at 14.44.06
Last, we compared the cortical MDM signatures to a gene co-expression network based on a widespread survey of gene expression in the human brain46. Nineteen modules were derived from the gene network, each comprised of a group of genes that co-varies in space. Six out of the nineteen gene modules were significantly correlated with the first PC of MDM. Interestingly, the first PC of MDM across the cortex was correlated most strongly with the two gene modules associated with membranes and synapses (Fig. 4, for further details see Supplementary Note 4 and Supplementary Figs. 10 and 11).

Screenshot 2019-08-01 at 14.47.04

Post-mortem validation for the lipidomic sensitivity of MDM.
The aforementioned analyses demonstrate strong agreement between in vivo MDM measurements and ex vivo molecular composition based on a group-level comparison of two different datasets. Strikingly, we were able to replicate this result at the level of the single brain. To achieve this we performed MRI scans (R1, MTsat, R2, MD, and MTV mapping) followed by histology of two fresh post-mortem porcine brains (Fig. 5a, b). First, we validated the qMRI estimation of MTV using dehydration techniques. MTV values estimated using MRI were in agreement with the non-water fraction found histologically (adjusted R2 = 0.64, p < 0.001 for the F-test, Fig. 5c).

Screenshot 2019-08-01 at 14.50.12
Next, we estimated the lipid composition of different brain regions. Thin-layer chromatography (TLC) was employed to quantify seven neutral and polar lipids (Supplementary Table 2 and Supplementary Fig. 12a). In accordance with the analysis in Fig. 3, we performed PCA to capture the main axes of variation in lipidomics, standard qMRI parameters, and MDM. Figure 5d shows that MTV did not correlate with the molecular variability across the brain, estimated by the 1st PC of lipidomics. Likewise, the molecular variability did not agree with the 1st PC of standard qMRI parameters (Fig. 5e).

Last, we applied the MDM approach to the post-mortem porcine brain. Similar to the human brain, different porcine brain regions have unique MDM signatures (Fig. 5f, g and Supplementary Fig. 12b). Remarkably, we found that agreement between lipid composition and MRI measurements emerges at the level of the MDM signatures. The molecular variability across brain regions significantly correlated with the regional variability in the MDM signatures (adjusted R2 = 0.3, p < 0.01 for the F-test, Fig. 5h). Excluding from the linear regression five outlier brain regions where the histological lipidomics results were 1.5 standard deviations away from the center yielded an even stronger correlation between MDM signatures and lipid composition (adjusted R2 = 0.55, p < 0.001 for the F-test, Supplementary Fig. 12c). This post-mortem analysis validates that the MDM approach allows us to capture molecular information using MRI at the level of the individual brain.

Disentangling water and molecular aging-related changes.
After establishing the sensitivity of the MDM signatures to the molecular composition of the brain, we used them to evaluate the chemophysical changes of the aging process. To assess aging-related changes across the brain, we scanned younger and older subjects (18 older adults aged 67 ± 6 years and 23 younger adults aged 27 ± 2 years). First, we identified significant molecular aging-related changes in the MDM signatures of different brain regions (Figs. 6 and 7, right column; Supplementary Fig. 13). Next, we tested whether the changes in MRI measurements, observed with aging, result from a combination of changes in the molecular composition of the tissue and its water content. We found that although it is common to attribute age-related changes in R1 and MTsat to myelin28,30,36, these qMRI parameters combine several physiological aging aspects. For example, using R1 and MTsat we identified significant aging-related changes in the parietal cortex, the thalamus, the parietal white-matter and the temporal white-matter (Figs. 6 and 7, left column). However, the MDM approach revealed that these changes have different biological sources (Figs. 6 and 7, middle columns; see Supplementary Figs. 14–17 for more brain regions).

Screenshot 2019-08-01 at 14.51.53

Screenshot 2019-08-01 at 14.54.44

Screenshot 2019-08-01 at 14.56.06

In agreement with the mosaic hypothesis, we identified distinct aging patterns for different brain regions. For example, in the hippocampus we found a change in R2* values related to a higher iron concentration with age, along with significant reduction in the total hippocampal volume (Fig. 8a). This age-related shrinkage was not accompanied by lower MTV values, indicating conserved tissue density (Fig. 7b). In addition, there was no significant difference in the hippocampal MDM signature with age (Fig. 7b). Cortical gray-matter areas also exhibited similar trends of volume reduction without major loss in tissue density (Fig. 8a). Unlike the gray matter, in the white matter we did not find volume reduction or large iron accumulation with age (Fig. 8a). However, we did find microscale changes with age in tissue composition, as captured by the MDM signature (Figs. 6a and 7c, and Supplementary Fig. 13), accompanied by a significant density-related decline in MTV (Fig. 8a). These findings are consistent with previous histological studies49,50,51 (see Discussion), and provide the ability to monitor in vivo the different components of the aging mosaic.

Last, to test whether the different biological aging trajectories presented in Fig. 8a share a common cause, we evaluated the correlations between them (Fig. 8b). Importantly, the chemophysical trajectory did not correlate significantly with the iron or volume aging patterns. The spatial distribution of water-related changes was found to correlate with iron content alterations (R2 = 0.27) and chemophysical alterations (R2 = 0.25). However, the strongest correlation between aging-related changes was found in volume and iron content (R2 = 0.77). As shown previously, this correlation may be explained to some extent by a systematic bias in automated tissue classification23. Additional analysis revealed that the different dimensions of the MDM signature capture distinct patterns of aging-related changes (Supplementary Fig. 30). Hence, complementary information regarding the various chemophysical mechanisms underlying brain aging could be gained by combining them.

 

Discussion

Normal brain aging involves multiple changes, at both the microscale and macroscale level. MRI is the main tool for in vivo evaluation of such age-related changes in the human brain. Here, we propose to improve the interpretation of MRI findings by accounting for the fundamental effect of the water content on the imaging parameters. This approach allows for non-invasive mapping of the molecular composition in the aging human brain.

Our work is part of a major paradigm shift in the field of MRI toward in vivo histology30,36,52. The MDM approach contributes to this important change by providing a hypothesis-driven biophysical framework that was rigorously developed. We demonstrated the power of our framework, starting from simple pure lipid phantoms to more complicated lipid mixtures, and from there, to the full complexity of the brain. In the brain, we show both in vivo and post-mortem validations for the molecular sensitivity of the MDM signatures. Early observations relate different qMRI parameters to changes in the fraction of myelin20,23,30,31,32,33,36. The current approach enriches this view and provides better sensitivity to the molecular composition and fraction of myelin and other cellular tissues.

We developed a unique phantom system of lipid samples to validate our method. While the phantom system is clearly far from the complexity of brain tissue, its simplicity allowed us to verify the specificity of our method to the chemophysical environment. Remarkably, our approach revealed unique signatures for different lipids, and is therefore sensitive even to relatively subtle details that distinguish one lipid from another. We chose to validate our approach using membrane lipids based on previous experiments40,41,42,43,44,45. Nevertheless, we do acknowledge the fact that brain tissue comprises many other compounds beside lipids, such as proteins, sugars, and ions. As we have shown, these other compounds also exhibit unique dependency on MTV. The effect of such compounds, along with other factors such as microstructure, and multi-compartment organization28 is probably captured when we apply the MDM approach to the in vivo human brain. Therefore, the phantoms were made to examine the MRI sensitivity for the chemophysical environment, and the human brain data was used to measure the true biological effects in a complex in vivo environment.

Our relaxivity approach captures the molecular signatures of the tissue, but is limited in its abilities to describe the full complexity of the chemophysical environment of the human brain. For example, R1 and R2, which are used to generate the MDM signatures, are also sensitive to the iron content23,48,52. However, we found that most of our findings cannot be attributed to alterations in iron content as measured with R2* (for more details see Supplementary Note 5). While there is great importance in further isolating different molecular components, we argue that accounting for the major effect of water on qMRI parameters (for R2 distributions see Supplementary Fig. 5) is a crucial step towards more specific qMRI interpretation.

We provide evidence from lipids samples and post-mortem data for the sensitivity of the MDM signatures to the molecular environment (Figs. 1e, 3b, and 5h). The variability of MDM values between human brain regions also correlated with specific gene-expression profiles (Fig. 4). While the comparison of in vivo human brain measurements to previously published ex vivo findings is based on two different datasets, these measurements are highly stable across normal subjects and the intersubject variabilities are much smaller than the regional variability. The agreement between the modalities provides strong evidence for the ability of our method to capture molecular information.

Remarkably, we were able to demonstrate the sensitivity of MDM signatures to lipid composition using direct comparison on post-mortem porcine brains. Even though there are many challenges in scanning post-mortem tissue, segmenting it, and comparing it to anatomically relevant histological results, we were able to replicate our in vivo findings. We provide histological validation for the MRI estimation of MTV. Moreover, we find that while standard qMRI parameters and MTV do not explain the lipidomic variability across the brain, the MDM signatures are in agreement with histological results. Lipids constitute the majority of the brain’s dry weight and are known to be important for maintaining neural conduction and chemical balance53,54. The brain lipidome was shown to have a great deal of structural and functional diversity and was found to vary according to age, gender, brain region, and cell type55. Disruptions of the brain lipid metabolism have been linked to different disorders, including Alzheimer’s disease, Parkinson’s disease, depression, and anxiety7,8,11,54,55,56,57. Our results indicate that the MDM approach enhances the consistency between MRI-driven measurements and lipidomics, compared with standard qMRI parameters.

The simplicity of our model, which is based on a first-order approximation of qMRI dependencies, has great advantages in the modeling of complex environments. Importantly, we used lipids samples to show that the contributions of different mixture-components can be summed linearly (Fig. 1d). For contrast agents, the relaxivity is used to characterize the efficiency of different agents. Here, we treated the tissue itself, rather than a contrast material, as an agent to compute the relaxivity of the tissue. While relaxivity is usually calculated for R1 and R2, we extended this concept to other qMRI parameters. Our results showed that the tissue relaxivity changes as a function of the molecular composition. This suggests that the relaxivity of the tissue relates to the surface interaction between the water and the chemophysical environment. A theoretical formulation for the effect of the surface interaction on proton relaxation has been proposed before58,59. Specifically, a biophysical model for the linear relationship between R1 and R2 to the inverse of the water content (1/WC = 1/(1 – MTV)) was suggested by Fullerton et al.43. Interestingly, 1/WC varies almost linearly with MTV in the physiological range of MTV values. Applying our approach with 1/WC instead of MTV produces relatively similar results (Supplementary Fig. 28). However, using MTV as a measure of tissue relaxivity allowed us to generalize the linear model to multiple qMRI parameters, thus producing multidimensional MDM signatures.

We show that the MDM signatures allow for better understanding of the biological sources for the aging-related changes observe with MRI. Normal brain aging involves multiple changes, at both the microscale and macroscale levels. Measurements of macroscale brain volume have been widely used to characterize aging-associated atrophy. Our method of analysis can complement such findings and provide a deeper understanding of microscale processes co-occurring with atrophy. Moreover, it allows us to test whether these various microscale and macroscale processes are caused by a common factor or represent the aging mosaic. Notably, we discovered that different brain regions undergo different biological aging processes. Therefore, combining several measurements of brain tissue is crucial in order to fully describe the state of the aged brain. For example, the macroscale aging-related volume reduction in cortical gray areas was accompanied by conserved tissue density, as estimated by MTV, and region-specific chemophysical changes, as estimated by the MDM. In contrast, in white-matter areas both MDM and MTV changed with age. These microscale alterations were not accompanied by macroscale volume reduction. Our in vivo results were validated by previous histological studies, which reported that the cortex shrinks with age, while the neural density remains relatively constant49,50. In contrast, white matter was found to undergo significant loss of myelinated nerve fibers during aging51. In addition, we found that the shrinkage of the hippocampus with age is accompanied with conserved tissue density and chemophysical composition. This is in agreement with histological findings, which predict drastic changes in hippocampal tissue composition in neurological diseases such as Alzheimer, but not in normal aging49,50,60,61. In contrast, hippocampal macroscale volume reduction was observed in both normal and pathological aging2.

It should be noted that most of the human subjects recruited for this study were from the academic community. However, the different age groups were not matched for variables such as IQ and socioeconomic status. In addition, the sample size in our study was quite small. Therefore, the comparison we made between the two age groups may be affected by variables other than age. Our approach may benefit from validation based on larger quantitative MRI datasets27,62. Yet, we believe we have demonstrated the potential of our method to reveal molecular alterations in the brain. Moreover, the agreement of our findings with previous histological aging studies supports the association between the group differences we measured and brain aging. Our results suggest that the MDM approach may be very useful in differentiating the effects of normal aging from those of neurodegenerative diseases. There is also great potential for applications in other brain research fields besides aging. For example, our approach may be used to advance the study and diagnosis of brain cancer, in which the lipidomic environment undergoes considerable changes63,64,65.

To conclude, we have presented here a quantitative MRI approach that decodes the molecular composition of the aging brain. While common MRI measurements are primarily affected by the water content of the tissue, our method employed the tissue relaxivity to expose the sensitivity of MRI to the molecular microenvironment. We presented evidence from lipid samples, post-mortem porcine brains and in vivo human brains for the sensitivity of the tissue relaxivity to molecular composition. Results obtained by this method in vivo disentangled different biological processes occurring in the human brain during aging. We identified region-specific patterns of microscale aging-related changes that are associated with the molecular composition of the human brain. Moreover, we showed that, in agreement with the mosaic theory of aging, different biological age-related processes measured in vivo have unique spatial patterns throughout the brain. The ability to identify and localize different age-derived processes in vivo may further advance human brain research.

Methods

Phantom construction
The full protocol of lipids phantom preparation is described in Shtangel et al.66.

In short, we prepared liposomes from one of the following lipids: phosphatidylserine (PS), phosphatidylcholine (PtdCho), phosphatidylcholine-cholesterol (PtdCho-Chol), Phosphatidylinositol-phosphatidylcholine (PI-PtdCho), or sphingomyelin (Spg). These phantoms were designed to model biological membranes and were prepared from lipids by the hydration–dehydration dry film technique67. The lipids were dissolved over a hot plate and vortexed. Next, the solvent was removed to create a dry film by vacuum-rotational evaporation. The samples were then stirred on a hot plate at 65 °C for 2.5 h to allow the lipids to achieve their final conformation as liposomes. Liposomes were diluted with Dulbecco’s phosphate buffered saline (PBS), without calcium and magnesium (Biological Industries), to maintain physiological conditions in terms of osmolarity, ion concentrations and pH. To change the MTV of the liposome samples we varied the PBS to lipid volume ratios66. Samples were then transferred to the phantom box for scanning in a 4 mL squared polystyrene cuvettes glued to a polystyrene box, which was then filled with ~1% SeaKem Agarose (Ornat Biochemical) and ~0.0005 M Gd (Gadotetrate Melumine, (Dotarem, Guerbet)) dissolved in double distilled water (ddw). The purpose of the agar with Gd (Agar-Gd) was to stabilize the cuvettes, and to create a smooth area in the space surrounding the cuvettes that minimalized air–cuvette interfaces. In some of our experiments we used lipid mixtures composed of several lipids. We prepared nine mixtures containing different combinations of two out of three lipids (PtdChol, Spg and PS) in varying volume ratios (1:1,1:2,2:1). For each mixture, we prepared samples in which the ratio between the different lipid components remained constant while the water-to-lipid volume fraction varied.

For the bovine serum albumin (BSA) phantoms, samples were prepared by dissolving lyophilized BSA powder (Sigma Aldrich) in PBS. To change the MTV of these phantoms, we changed the BSA concentration. For the BSA + Iron phantoms, BSA was additionally mixed with a fixed concentration of 50 µg/mL ferrous sulfate heptahydrate (FeSO4*7H2O). Samples were prepared in their designated concentrations at room temperature. Prepared samples were allowed to sit overnight at 4 ℃ to ensure BSA had fully dissolved, without the need for significant agitation, which is known to cause protein cross-linking. Samples were then transferred to the phantom box for scanning.

For Glucose and Sucrose phantoms, different concentrations of D-( + )-Sucrose (Bio-Lab) and D-( + )-Glucose (Sigma) were dissolved in PBS at 40 ℃. Samples were allowed to reach room temperature before the scan.

MRI acquisition for phantoms

Data was collected on a 3 T Siemens MAGNETOM Skyra scanner equipped with a 32-channel head receive-only coil at the ELSC neuroimaging unit at the Hebrew University.

For quantitative R1 & MTV mapping, three-dimensional (3D) Spoiled gradient (SPGR) echo images were acquired with different flip angles (α = 4°, 8°, 16°, and 30°). The TE/TR was 3.91/18 ms. The scan resolution was 1.1 × 1.1 × 0.9 mm. The same sequence was repeated with a higher resolution of 0.6 × 0.6 × 0.5 mm. The TE/TR was 4.45/18 ms. For calibration, we acquired an additional spin-echo inversion recovery (SEIR) scan. This scan was done on a single slice, with adiabatic inversion pulse and inversion times of TI = 2000, 1200, 800, 400, and 50. The TE/TR was 73/2540 ms. The scan resolution was 1.2 mm isotropic.

For quantitative T2 mapping, images were acquired with a multi spin-echo sequence with 15 equally spaced spin echoes between 10.5 ms and 157.5 ms. The TR was 4.94 s. The scan resolution was 1.2 mm isotropic. For quantitative MTsat mapping, images were acquired with the FLASH Siemens WIP 805 sequence. The TR was 23 ms for all samples except PI:PtdCho for which the TR was 72 ms. Six echoes were equally spaced between 1.93 ms to 14.58 ms. The on-resonance flip angle was 6°, the MT flip angle was 220°, and the RF offset was 700. We used 1.1-mm in-plane resolution with a slice thickness of 0.9 mm. For samples of sucrose and glucose, MTsat mapping was done similar to the human subjects, based on 3D Spoiled gradient (SPGR) echo image with an additional MT pulse. The flip angle was 10°, the TE/TR was 3.91/28 ms. The scan resolution was 1 mm isotropic.

Estimation of qMRI parameters for phantoms

MTV and R1 estimations for the lipids samples were computed based on a the mrQ39 (https://github.com/mezera/mrQ) and Vista Lab (https://github.com/vistalab/vistasoft/wiki) software. The mrQ software was modified to suit the phantom system66. The modification utilizes the fact that the Agar-Gd filling the box around the samples is homogeneous and can, therefore, be assumed to have a constant T1 value. We used this gold standard T1 value generated from the SEIR scan to correct for the excite bias in the spoiled gradient echo scans. While the data was acquired in two different resolutions (see “MRI acquisition”), in our analysis we use the median R1 and MTV of each lipid sample and these are invariant to the resolution of acquisition (Supplementary Fig. 1e). Thus, we were able to use scans with different resolutions without damaging our results. T2 maps were computed by implementing the echo‐modulation curve (EMC) algorithm68.

For quantitative MTsat mapping see the “MTsat estimation” section for human subjects.

MDM computation for phantoms

We computed the dependency of each qMRI parameter (R1, MTsat, and R2) on MTV in different lipids samples. This process was implemented in MATLAB (MathWorks, Natwick, MI, USA). To manipulate the MTV values, we scanned samples of the same lipid in varying concentrations. We computed the median MTV of each sample, along with the median of qMRI parameters. We used these data points to fit a linear model across all samples of the same lipid. The slope of this linear model represents the MTV derivative of the linear equation. We used this derivative estimate of three qMRI parameters (R1, R2, and MTsat) to compute the MDM signatures. The same procedure was used for the MDM computation of lipid mixtures.

 

MDM modeling of lipid mixtures

We tested the ability of MDM to predict the composition of lipid mixtures. For this analysis we used nine mixture phantoms (see “Phantom construction”), along with the three phantoms of the pure lipid constituents of the mixtures (PS, Spg, and Ptd-Cho).

In order to predict the qMRI parameters of a lipid mixture (Fig. 1d) we used Supplementary Eq. 1 (Supplementary Note 1). To further predict the composition of the mixtures (Fig. 1e) we used Supplementary Eq. 5 (Supplementary Note 2). We solved this equation using the QR factorization algorithm.

Ethics

Human experiments complied with all relevant ethical regations. The Helsinki Ethics Committee of Hadassah Hospital, Jerusalem, Israel approved the experimental procedure. Written informed consent was obtained from each participant prior to the procedure.

Human subjects

Human measurements were performed on 23 young adults (aged 27 ± 2 years, 11 females), and 18 older adults (aged 67 ± 6 years, five females). Healthy volunteers were recruited from the community surrounding the Hebrew University of Jerusalem.

MRI acquisition for human subjects

Data was collected on a 3 T Siemens MAGNETOM Skyra scanner equipped with a 32-channel head receive-only coil at the ELSC neuroimaging unit at the Hebrew University.

For quantitative R1, R2*, & MTV mapping, 3D Spoiled gradient (SPGR) echo images were acquired with different flip angles (α = 4°, 10°, 20°, and 30°). Each image included five equally spaced echoes (TE = 3.34–14.02 ms) and the TR was 19 ms (except for six young subjects for which the scan included only one TE = 3.34 ms). The scan resolution was 1 mm isotropic. For calibration, we acquired additional spin-echo inversion recovery scan with an echo-planar imaging (EPI) read-out (SEIR-epi). This scan was done with a slab-inversion pulse and spatial-spectral fat suppression. For SEIR-epi, the TE/TR was 49/2920 ms. TI were 200, 400, 1,200, and 2400 ms. We used 2-mm in-plane resolution with a slice thickness of 3 mm. The EPI read-out was performed using 2 × acceleration.

For quantitative T2 mapping, multi‐SE images were acquired with ten equally spaced spin echoes between 12 ms and 120 ms. The TR was 4.21 s. The scan resolution was 2 mm isotropic. T2 scans of four subjects (one young, three old) were excluded from the analysis due to motion.

For quantitative MTsat mapping, 3D Spoiled gradient (SPGR) echo image were acquired with an additional MT pulse. The flip angle was 10°, the TE/TR was 3.34/27 ms. The scan resolution was 1 mm isotropic.

Whole-brain DTI measurements were performed using a diffusion-weighted spin-echo EPI sequence with isotropic 1.5-mm resolution. Diffusion weighting gradients were applied at 64 directions and the strength of the diffusion weighting was set to b = 2000 s/mm2 (TE/TR = 95.80/6000 ms, G = 45mT/m, δ = 32.25 ms, Δ = 52.02 ms). The data includes eight non-diffusion-weighted images (b = 0). In addition, we collected non-diffusion-weighted images with reversed phase-encode blips. For five subjects (four young, one old) we failed to acquire this correction data and they were excluded from the diffusion analysis.

Anatomical images were acquired with 3D magnetization prepared rapid gradient echo (MP-RAGE) scans for 24 of the subjects (14 from the younger subjects, 10 from the older subjects). The scan resolution was 1 mm isotropic, the TE/TR was 2.98/2300 ms. Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) scans were acquired for the rest of the subjects. The scan resolution was 1 mm isotropic, the TE/TR was 2.98/5000 ms.

Estimation of qMRI parameters for human subjects

Whole-brain MTV and R1 maps, together with bias correction maps of B1 + and B1-, were computed using the mrQ software39,69 (https://github.com/mezera/mrQ). Voxels in which the B1 + inhomogeneities were extrapolated and not interpolated were removed from the MTV and R1 maps. While we did not correct our MTV estimates for R2*, we showed that employing such a correction does not significantly change our results (see Supplementary Note 6, Supplementary Figs. 20–27). MTV maps of four subjects had bias in the lower part of the brain and they were therefore excluded from the analysis presented in Fig. 3, which includes ROIs in the brainstem.

Whole-brain T2 maps were computed by implementing the echo‐modulation curve (EMC) algorithm68. To combine the MTV and T2 we co-registered the quantitative MTV map to the T2 map. We used the ANTS software package70 to calculate the transformation and to warp the MTV map and the segmentation. The registration was computed to match the T1 map to the T2 map. Next, we applied the calculated transformation to MTV map (since MTV and T1 are in the same imaging space) and resampled the MTV map to match the resolution of the T2 map. The same transformation was also applied to the segmentation. R2 maps were calculated as 1/T2.

Whole-brain MTsat maps were computed as described in Helms et al.37. The MTsat measurement was extracted from Eq. (1):

MTsat=𝑀0𝐵1𝛼𝑅1TR𝑆MT−(𝐵1𝛼)22−𝑅1TR
(1)
Where SMT is the signal of the SPGR scan with additional MT pulse, α is the flip angle and TR is the repetition time. Mo (the equilibrium magnetization parameter), B1 (the transmit inhomogeneity), and R1 estimations were computed from the non-MT weighted SPGR scans, during the pipeline described under “MTV & R1 estimation”. Registration of the SMT image to the imaging space of the MTV map was done using a rigid-body alignment (R1, B1, and MO are all in the same space as MTV).

Diffusion analysis was done using the FDT toolbox in FSL71,72. Susceptibility and eddy current induced distortions were corrected using the reverse phase-encode data, with the eddy and topup commands73,74. MD maps were calculated using vistasoft (https://github.com/vistalab/vistasoft/wiki). We used a rigid-body alignment to register the corrected dMRI data to the imaging space of the MTV map (Flirt, FSL). In order to calculate the MD-MTV derivatives, we resampled the MTV map and the segmentation to match the dMRI resolution.

We used the SPGR scans with multiple echoes to estimate R2*. Fitting was done through the MPM toolbox75. As we had four SPGR scans with variable flip angles, we averaged the R2* maps acquired from each of these scans for increased SNR.

Human brain segmentation

Whole-brain segmentation was computed automatically using the FreeSurfer segmentation algorithm76. For subjects who had an MP-RAGE scan, we used it as a reference. For the other subjects the MP2RAGE scan was used as a reference. These anatomical images were registered to the MTV space prior to the segmentation process, using a rigid-body alignment. Sub-cortical gray-matter structures were segmented with FSL’s FIRST tool77. To avoid partial volume effects, we removed the outer shell of each ROI and left only the core.

MDM computation in the human brain

We computed the dependency of each qMRI parameter (R1, MTsat, MD, and R2) on MTV in different brain areas. This process was implemented in MATLAB (MathWorks, Natwick, MI, USA). For each ROI, we extracted the MTV values from all voxels and pooled them into 36 bins spaced equally between 0.05 and 0.40. This was done so that the linear fit would not be heavily affected by the density of the voxels in different MTV values. We removed any bins in which the number of voxels was smaller than 4% of the total voxel count in the ROI. The median MTV of each bin was computed, along with the median of the qMRI parameter. We used these data points to fit the linear model across bins using Eq. (2):

qMRIparameters=𝑎∗MTV+𝑏
(2)
The slope of this linear model (“a”) represents the MTV derivative of the linear equation. We used this derivative estimate to compute the MDM signatures.

For each subject, ROIs in which the total voxel count was smaller than a set threshold of 500 voxels for the MTsat and R1 maps, 150 voxels for the MD map, and 50 voxels for the R2 map were excluded.

Principal component analysis (PCA) in the human brain

To estimate the variability in the MDM signatures across the brain, we computed the first principal component (PC) of MDM. For each MDM dimension (MTV derivatives of R1, MTsat, MD, and R2), we evaluated the median of the different brain areas across the young subjects. As each MDM dimension has different units, we then computed the z-score of each dimension across the different brain area. Finally, we performed PCA. The variables in this analysis were the different MDM dimensions, and the observations were the different brain areas. From this analysis, we derived the first PC that accounts for most of the variability in MDM signatures across the brain. To estimate the median absolute deviations (MAD) across subjects of each MDM measurement in the PC basis, we applied the z-score transformation to the original MAD and then projected them onto the PC basis.

To compute the first PC of standard qMRI parameters we followed the same procedure, but used R1, MTsat, MD, and R2 instead of their MTV derivatives.

For the first PC of molecular composition, we followed the same procedure, but used the phospholipid composition and the ratio between phospholipids to proteins and cholesterol as variables. The data was taken from eight post-mortem human brains7. Brains were obtained from individuals between 54 and 57 years of age, which were autopsied within 24 h after death.

Linear model for prediction of human molecular composition

We used MDM measurements in order to predict the molecular composition of different brain areas (Fig. 3c). For this analysis we used Supplementary Eq. 5 in the Supplementary Note 2. We solved this equation using QR factorization algorithm (for more details see Supplementary Note 3).

Gene-expression dataset

For the gene-expression analysis we followed the work of Ben-David and Shifman46. Microarray data was acquired from the Allen Brain Atlas (http://human.brain-map.org/well_data_files) and included a total of 1340 microarray profiles from donors H0351.2001 and H0351.2002, encompassing the different regions of the human brain. The donors were 24 and 39 years old, respectively, at the time of their death, with no known psychopathologies. We used the statistical analysis described by Ben-David and Shifman46. They constructed a gene network using a weighted gene co-expression network analysis. The gene network included 19 modules of varying sizes, from 38 to 7385 genes. The module eigengenes were derived by taking the first PC of the expression values in each module. In addition, we used the gene ontology enrichment analysis described by Ben-David and Shifman to define the name of each module. The colors of the different modules in the Fig. 4 and Supplementary Fig. 10 are the same as in the original paper.

Next, we matched between the gene-expression data and the MRI measurements. This analysis was done on 35 cortical regions extracted from FreeSurfer cortical parcellation. We downloaded the T1-weighted images of the two donors provided by the Allen Brain Atlas (http://human.brain-map.org/mri_viewers/data) and used them as a reference for FreeSurfer segmentation. We then found the FreeSurfer label of each gene-expression sample using the sample’s coordinates in brain space. We removed samples for which the FreeSurfer label and the label provided in the microarray dataset did not agree (there were 72 such samples out of 697 cortical samples). For each gene module, we averaged over the eigengenes of all samples from the same cortical area across the two donors.

Last, we compared the cortical eigengene of each module to the projection of cortical areas on the first PC of MDM. In addition, we compared the modules’ eigengenes to the MTV values of the cortical areas and to the projection of cortical areas on the first PC of standard qMRI parameters (Supplementary Fig. 10). These 57 correlations were corrected for multiple comparisons using the FDR method.

Brain region’s volume computation

To estimate the volume of different brain regions, we calculated the number of voxels in the FreeSurfer segmentation of each region (see “Brain segmentation”).

R2* correction for MTV
To correct the MTV estimates for R2* we used Eq. (3):

MTV𝐶=1−(1−MTV)⋅exp(TE⋅R2∗)
(3)
Where MTVC is the corrected MTV.

Statistical analysis

The statistical significance of the differences between the age groups was computed using an independent-sample t-test (alpha = 0.05, both right and left tail) and was corrected for multiple comparisons using the false-discovery rate (FDR) method. For this analysis, MRI measurements of both hemispheres of bilateral brain regions were joined together. R2 measurements were adjusted for the number of data points. All statistical tests were two-sided.

Post-mortem tissue acquisition

Two post-mortem porcine brains were purchased from BIOTECH FARM.

Post-mortem MRI acquisition

Brains were scanned fresh (without fixation) in water within 6 h after death. Data was collected on a 3 T Siemens MAGNETOM Skyra scanner equipped with a 32-channel head receive-only coil at the ELSC neuroimaging unit at the Hebrew University.

For quantitative R1, R2*, & MTV mapping, 3D Spoiled gradient (SPGR) echo images were acquired with different flip angles (α = 4°, 10°, 20°, and 30°). Each image included five equally spaced echoes (TE = 4.01 – 16.51 ms) and the TR was 22 ms. The scan resolution was 0.8 mm isotropic. For calibration, we acquired additional spin-echo inversion recovery scan with an echo-planar imaging (EPI) read-out (SEIR-epi). This scan was done with a slab-inversion pulse and spatial-spectral fat suppression. For SEIR-epi, the TE/TR was 49/2920 ms. TI were 50, 200, 400, 1200 ms. The scan resolution was 2 mm isotropic. The EPI read-out was performed using 2 × acceleration.

For quantitative T2 mapping, multi‐SE images were acquired with ten equally spaced spin echoes between 12 and 120 ms. The TR was 4.21 s. The scan resolution was 2 mm isotropic.

For quantitative MTsat mapping, 3D Spoiled gradient (SPGR) echo image were acquired with an additional MT pulse. The flip angle was 10°, the TE/TR was 4.01/40 ms. The scan resolution was 0.8 mm isotropic.

Whole-brain DTI measurements were performed using a diffusion-weighted spin-echo EPI sequence with isotropic 1.5-mm resolution. Diffusion weighting gradients were applied at 64 directions and the strength of the diffusion weighting was set to b = 2000 s/mm2 (TE/TR = 95.80/6000 ms, G = 45mT/m, δ = 32.25 ms, Δ = 52.02 ms). The data includes eight non-diffusion-weighted images (b = 0).

For anatomical images, 3D magnetization prepared rapid gradient echo (MP-RAGE) scans were acquired. The scan resolution was 1 mm isotropic, the TE/TR was 2.98/2300 ms.

Histological analysis

Following the MRI scans the brains were dissected. Total of 42 brain regions were identified. Four samples were excluded as we were not able to properly separate the WM from the GM. One sample was excluded as we could not properly identify its anatomical origin. Additional two samples were too small for TLC analysis.

The non-water fraction (MTV) was determined by desiccation, also known as the dry-wet method. A small fraction of each brain sample (~0.25 g) was weighed. In order to completely dehydrate the fresh tissues, they were left for several days in a vacuum dessicator over silica gel at 4 °C. The experiment ended when no further weight loss occurred. The MTV of each brain sample was calculated based on the difference between the wet (Wwet) and dry (Wdry) weights of the tissue (Eq. 4):

MTV=𝑊wet−𝑊dry𝑊wet
(4)
For lipid extraction and lipidomics analysis78, Brain samples were weighted and homogenized with saline in plastic tubes on ice at concentration of 1 mg/12.5 µL. Two-hundred fifty microliters from each homogenate were utilized for lipid extraction and analysis with thin-layer chromatography (TLC). The lipid species distribution was analyzed by TLC applying 150 µg aliquots. Samples were reconstituted in 10 µL of Folch mixture and spotted on Silica-G TLC plates. Standards for each fraction were purchased from Sigma Aldrich (Rehovot, Israel) and were spotted in separate TLC lanes, i.e., 50 µg of triacylglycerides (TG), cholesterol (Chol), cholesteryl esters (CE), free fatty acids (FFA), lysophospholipids (Lyso), sphingomyelin (Spg), phosphatidylcholine (PtdCho), phosphatidylinositol (PI), phosphatidylserine (PS), and phosphatidylethanolamine (PE). Plates were then placed in a 20 × 20 cm TLC chamber containing petroleum ether, ethyl ether, and acetic acid (80:20:1, v/v/v) for quantification of neutral lipids or chloroform, methanol, acetic acid, and water (65:25:4:2, v:v:v:v) for quantification of polar lipids and run for 45 min. TG, Chol, CE, FFA, phospholipids (PL), Lyso, Spg, PtdCho, PI, PS, and PE bands were visualized with Iodine, scanned and quantified by Optiquant after scanning (Epson V700). Lyso, CE, TG, and PI were excluded from further analysis as their quantification was noisy and demonstrated high variability across TLC plates. This analysis was conducted under the guidance of Prof. Alicia Leikin-Frenkel in the Bert Strassburger Lipid Center, Sheba, Tel Hashomer.

Estimation of qMRI parameters in the post-mortem brain

Similar to human subjects.

Brain segmentation of post-mortem brain

Brain segmentation was done manually. Five tissue samples were excluded as we could not identify their origin location in the MRI scans.

MDM computation in the post-mortem brain

We computed the dependency of each qMRI parameter (R1, MTsat, MD, and R2) on MTV in different brain areas similarly to the analysis of the human subjects.

Principal component analysis (PCA) in the post-mortem brain

To estimate the variability in the MDM signatures across the brain, we computed the first principal component (PC) of MDM. PCA analysis was performed with four variables corresponding to the MDM dimensions (MTV derivatives of R1, MTsat, MD, and R2), and 30 observations corresponding to the different brain regions. As each MDM dimension has different units, we first computed the z-score of each dimension across the different brain areas prior to the PCA. From this analysis we derived the first PC that accounts for most of the variability in MDM signatures across the brain.

To compute the first PC of standard qMRI parameters we followed the same procedure, but used R1, MTsat, MD, and R2 instead of their MTV derivatives.

To estimate the variability in the lipid composition across the brain, we computed the first principal component (PC) of lipidomics. PCA analysis was performed with seven variables corresponding to the different polar and neutral lipids (Chol, FFA, PL, Spg, PtdCho, PS, PE), and 30 observations corresponding to the different brain regions. From this analysis, we derived the first PC that accounts for most of the variability in lipid composition across the brain.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

A toolbox for computing MDM signatures is available at [https://github.com/MezerLab/MDM_toolbox].

The code generating the figures of in the paper is available at [https://github.com/MezerLab/MDM_Gen_Figs].

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Acknowledgements

This work was supported by the ISF grant 0399306, awarded to A.A.M. We acknowledge Ady Zelman for the assistance in collecting the human MRI data. We thank Assaf Friedler for assigning research lab space and advising on the lipid sample experiments. We thank Inbal Goshen for assigning research lab space and advising on the protein and ion samples as well as the porcine brain experiments. We thank Magnus Soderberg for advising on histological data interpretation. We are grateful to Brian A. Wandell, Jason Yeatman, Hermona Soreq, Ami Citri, Mark Does, Yaniv Ziv, Ofer Yizhar, Shai Berman, Roey Schurr, Jonathan Bain, Asier Erramuzpe Aliaga, Menachem Gutman, and Esther Nachliel for their critical reading of the manuscript and very useful comments. We thank Prof. Alicia Leikin-Frenkel for her guidance with the TLC analysis. We thank Rona Shaharabani for guidance and support in the post-mortem experiments.

Affiliations

The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel
Shir Filo, Oshrat Shtangel, Noga Salamon, Adi Kol, Batsheva Weisinger & Aviv A. Mezer
Department of Genetics, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel
Sagiv Shifman
Contributions
S.F., O.S., and A.A.M. conceived of the presented idea. S.F. and A.A.M. wrote the manuscript and designed the figures. S.F. collected the human and non-human brain datasets and analyzed them. O.S. performed the phantom experiments and analyzed them. B.W. performed the phantom experiments for non-lipid compounds. N.S. performed the gene-expression analysis. S.S. assisted and instructed with the gene-expression analysis. A.K. performed the porcine brain dissection.

Corresponding author

Correspondence to Aviv A. Mezer.

Ethics declarations & Competing interests

A.A.M, S.F., O.S. and the Hebrew University of Jerusalem have filed a patent application describing the technology used to measure MDM in this work. The other authors declare no competing interests.

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Marcela’s Story:  A Liver Transplant Gives the Gift of Life

Patient is HCV Positive, liver transplanted from a 22-year-old donor performed at age 70. Interview conducted 14 years post-liver transplant.

Author: Gail S. Thornton, M.A.

Co-Editor: The VOICES of Patients, HealthCare Providers, Caregivers and Families: Personal Experience with Critical Care and Invasive Medical Procedures

For Marcela Almada Calles of Valle de Bravo, Mexico, a picturesque town on the shores of Lake Avándaro about two hours outside of Mexico City where she has lived for 30 years, life is about seizing the moment and having “an open mind and positive attitude.”  An active woman in her 80’s, Marcela’s days are full of professional and personal achievements and a long list of activities still to accomplish. However, life wasn’t always so positive as she put her life on hold for two-and-a-half years to relocate to Los Angeles, California, so that she could have a liver transplant.

“My spirit and attitude have always been what has carried me through life and difficult situations. This time was no different.”

Image SOURCE: Photographs courtesy of Marcela Almada Calles.   

Marcela’s story started 20 years ago during a time when she operated a successful event planning and catering business for high-profile government and social dignitaries, pharmaceutical companies, and luxury department stores.

“I normally worked long hours from early morning until evening, until one day, I felt exceptionally tired and it became a huge effort to concentrate. My ankles were swollen and I was out of breath all the time and my skin was yellow. I felt sleepy and would sometimes become tired during the day. This was unusual for me. I knew something was not right.”

At that point, Marcela decided to make an appointment with her local physician and friend, Dr. Sergio Ulloa, a highly regarded rheumatologist and corporate and government affairs leader in Mexico, who examined her and took several blood tests. When the blood results came back, Dr. Ulloa immediately referred her to Dr. Sergio Kershenovich, a well-regarded hepatologist, at his private clinic, who checked her for symptoms of Hepatitis C. After that Marcela decided to get another opinion and went to see Dr. Fernando Quijano, a general surgeon, who immediately wanted her to have surgery because he had found a cancerous tumor in her liver.

“My doctors’ opinions were that I needed to have a liver transplant immediately because I was in liver failure. It appeared that I had a failing liver — and a tumor there as well and my liver was not working properly.”

Relocating Life to the United States

At that point, my six children – Marcela, Luis, Diego, Rodolfo, Gabriela, Mario — who live in parts of Mexico and Singapore became involved in my health care decisions and treatment plan.

“My son, Luis, believed the best treatment for me was to see a liver specialist in the United States so that I received the best care from a leading liver transplantation hospital. He made some connections with friends and that next day, Dr. Francisco Durazo, chief of Transplant Hepatology and medical director of the Dumont UCLA Liver Transplant Center in Los Angeles, told me to come immediately to see him. I remember my children were supportive and concerned, but were afraid for me as we all knew that I had a long road ahead of me.”

At that time, she was put on a national liver transplant list by the UCLA Transplant Center.

“What I didn’t know was that more than 9,000 potential recipients are currently awaiting liver transplants.”  http://transplants.ucla.edu/site.cfm?id=397

“Dr. Durazo was very concerned and told me that my liver was not working at all and I had to have a liver transplant as soon as possible, so he asked me to stay in Los Angeles, since I was now part of a transplant list.”

Evaluation By Transplant Team

Marcela’s case is no different than any other patient awaiting a liver transplant. According to their web site, the UCLA Transplant Center conducts evaluations over two or three days. During this time, the patients meets with a social worker, transplant hepatologist, surgeon, transplant coordinator, psychiatrist and dietitian, as well as other specialists as needed. The evaluation is customized to each patient’s medical condition. Once the evaluation is completed, each patient’s case is presented at a weekly meeting of the UCLA Liver Transplant Consultation Team. This group includes specialists from surgery, adult and pediatric hepatology, cardiology, pulmonary, nephrology, hematology, infectious disease, as well as transplant coordinators and social workers. At this time, the team determines if any other tests are required to ensure the patient’s candidacy for transplant, then the patient and the physician are notified of the recommendation made by the transplant team. http://transplants.ucla.edu/site.cfm?id=401

Waiting For Answers

Marcela arrived at UCLA in Los Angeles with her family on Mother’s Day — May 10, 1999 — for what she describes as “the best time in her life to be alive with the help of medicine and technology.” That meant that she needed to rent an apartment and live near the hospital in case the doctors received an anonymous donor who would give her the gift of life.

“I had to wear a beeper 24 hours a day and I was never alone. My children took turns over the next two-and-a-half years to give up their lives with their families to live with me and help me navigate the health care system and my upcoming surgery.”

Marcela filled her days at her new apartment in Los Angeles reading about her condition, meditating to quiet her mind, watching television, and talking with family, friends and neighbors.

“The doctors called me two times over the next few months, saying they had an anonymous liver donor and I needed to come now to the hospital for tests. Unfortunately, those blood tests and other diagnostic tests showed that I was not a good match, so the doctors sent me home. It was a frustrating time because I wanted to have the liver transplant surgery and move on with my life.”

Finally, after waiting eight months for a liver transplant, Marcela’s outlook on life was greatly improved when an anonymous donor gave her the gift of life – a new, healthy liver.

“The donor’s blood type was a match for me. The surgery took eight hours and it was successful. The doctors told me that my immune system might reject my new liver, so I was given a cocktail of medicines, such as anti-rejection drugs, corticosteroids, calcinurin inhibitors, mTOR inhibitors, and antibiotics and watched very closely in the hospital.”

Marcela was then permitted to leave the hospital only a week after her surgery.

“That was the happiest day of my life. My spirits were high and I had a life to live.”

Her children served as her strength.

“My children took turns flying back and forth to Los Angeles to stay with me. They had a long list of instructions from the doctor. I could take some walks and eat small meals for the next few weeks, but I couldn’t exert myself in any way. I developed a cold over the next few weeks, as my immune system was low, so I had to take special care to eat right, get enough sleep and, most of all, relax. My body, spirit and mind had much healing to do.”

For the next 1 ½ years, Los Angeles was my “second” home.

“I needed to remain there after the procedure so my doctors could monitor my progress. During that time, I felt stronger each day. The support of my family was a true blessing for me. They were my eyes and ears – and my greatest advocates. My doctor recommended that I come weekly for check-ups and go through a physical therapy program so that I could regain my liver function and physical strength. I followed all my doctor’s orders.”

Day by day, Marcela believed as if she could conquer the world.

“I decided, one day many months after the surgery, to become ‘irresponsible’ and spent time with a few good friends, Gabriela and Guadalupe, who traveled to see me. For a weekend, we went to Las Vegas to see shows and go to the casinos. I laughed, played and walked all I could. My children didn’t even know what I was up to, but I felt good and wanted to enjoy the world and my new freedom.”

Marcela was able to return home to Valle de Bravo with a fresh perspective, a long list of things to do, and many happy memories.

“Since that time, I have kept myself active and busy; I never let my mind and heart rest. I am also forever grateful to my anonymous liver donor because it is because of a 22-year-old young man who died in an unfortunate automobile accident that I am here today.”

Liver Transplant Facts

The liver is the body’s vital organ that you cannot live without. It serves many critical functions, including metabolism of drugs and toxins, removing degradation products of normal body metabolism and synthesis of many proteins and enzyme, which are necessary for blood to clot. Transplantation is the only cure for liver insufficiency or liver failure because no device or machine reliably performs all the functions of the liver. http://transplant.surgery.ucsf.edu/conditions–procedures/liver-transplantation.aspx

According to a hospital transplant web site, overall, outcomes for liver transplantation are very good, but vary significantly depending on the indication for liver transplant as well as factors associated with the donor. Currently, the overall patient survival one year after liver transplant is 88 percent. Patient survival five years after liver transplant is 73 percent. These results vary significantly based on the indication for liver transplantation. The encouraging trend is that over the past 20 years short- and long-term patient survival has continued to improve. With advances in surgical technique, organ preservation, peri-operative care, and immunosuppression, survival will hopefully continue to improve in the future. http://transplant.surgery.ucsf.edu/conditions–procedures/liver-transplantation.aspx

Life For Marcela Today

Science is helping rebalance medicine with the most innovative discoveries and new ways of treating illness.

“I am happy to be part of the solution with a happy ending, too.”

Today, Marcela leads a rich and full life.

“It’s been 14 years since my liver transplant. I continue to feel healthy and alive. Nothing will keep me from doing what I want to do.”

Marcela has an active social life. She takes frequent vacations around the world, including a three-month holiday to Asia, where she travels multiple times to Bali, Cambodia, China and Singapore, where her daughter lives. She is an avid golfer and organizes tournaments in many private golf courses. She is learning to speak French, which is an easy transition (she says) from speaking Spanish. She plays cards with a group of friends weekly, sings in a musical group, and takes dance lessons, too. Life is very, very good.

Editor’s note: We would like to thank Gabriela Contreras, a global communications consultant and patient advocate, for the tremendous help and support that she provided in locating and scheduling time to talk with Marcela Almada Calles.

Marcela Almada Calles provided her permission to publish this interview on July 21, 2016.

 

REFERENCE/SOURCE 

http://www.webmd.com/digestive-disorders/digestive-diseases-liver-transplantation

Other related articles:

Retrieved from http://transplants.ucla.edu/site.cfm?id=397

Retrieved from http://transplant.surgery.ucsf.edu/conditions–procedures/liver-transplantation.aspx

Retrieved from http://transplant.surgery.ucsf.edu/conditions–procedures/liver-transplantation.aspx

Other related articles were published in this Open Access Online Scientific Journal include the following: 

2016

AGENDA for Adoptive T Cell Therapy Delivering CAR, TCR, and TIL from Research to Reality, CHI’S 4TH ANNUAL IMMUNO-ONCOLOGY SUMMIT – SEPTEMBER 1-2, 2016 | Marriott Long Wharf Hotel – Boston, MA

https://pharmaceuticalintelligence.com/2016/07/15/adoptive-t-cell-therapy-delivering-car-tcr-and-til-from-research-to-reality-chis-4th-annual-immuno-oncology-summit-september-1-2-2016-marriott-long-wharf-hotel-boston-ma/

Technologies For Targeting And Delivering Chemotherapeutics Directly To The Tumour Site

https://pharmaceuticalintelligence.com/2016/04/25/technologies-for-targeting-and-delivering-chemotherapeutics-directly-to-the-tumour-site/

2015

3-D Printed Liver

https://pharmaceuticalintelligence.com/2015/11/16/3-d-printed-liver/

Newly discovered cells regenerate liver tissue without forming tumors

https://pharmaceuticalintelligence.com/2015/08/16/newly-discovered-cells-regenerate-liver-tissue-without-forming-tumors/

Novel Approaches to Cancer Therapy 

https://pharmaceuticalintelligence.com/2015/04/11/novel-approaches-to-cancer-therapy-7-12/

 

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Press release on January 29,2016

International Consortium Identifies and Validates Cellular Role of Priority Parkinson’s Disease Drug Target, LRRK2 Kinase
An international public-private consortium MARTINSRIED, GERMANY; DUNDEE, UNITED KINGDOM; NEW YORK, has identified and validated a cellular role of a primary Parkinson’s disease drug target, LRRK2 kinase. This finding was published in the online open access eLife journal (http://dx.doi.org/10.7554/eLife.12813), illuminates a novel route for therapeutic development and intervention.
A team of investigators from the Max Plank Institute of Biochemistry, The University of Dundee, the Michael J. Fox Foundation for Parkinson’s Research (MJFF), GlaxoSmithKline and MSD contributed to systematic testing to determine that LRRK2 kinase regulates cellular trafficking by deactivating certain Rab proteins.
It is known that LRRK2 gene are the greatest known genetic contributor to Parkinson’s disease. Pharmaceutical Companies are developing LRRK2 kinase inhibitors to correct the effect of those mutations and treat Parkinson’s disease. The new breakthrough finding that links Mutant LRRK2 to inappropriate deactivation of Rab function opens the doors to more than 20 years of accumulated knowledge of the roles of Rab proteins to improve the understanding of LRRK2 dysfunction in the Parkinson’s disease process
“The pathological cascade leading to brain diseases such as Parkinson’s likely includes many cellular players,” said Matthias Mann, PhD, Director of the Department of Proteomics and Signal Transduction at the Max Planck Institute of Biochemistry. “The identification of this LRRK2 substrate gives us a central piece in this puzzle and another potential place to intervene in the disease process.”
Marco Baptista, PhD, MJFF Senior Associate Director of Research Programs, said, “Identification of Rab proteins as a LRRK2 substrate presents a tool to measure the impact of these inhibitors not only on LRRK2 levels but also on LRRK2 function. This critical component will advance development of these therapies to slow or stop Parkinson’s disease, patients’ greatest unmet need.”
This MJFF-led consortium used a combination of tools in the discovery, including a knock-in mouse model of the most common LRRK2 mutation strongly associated with Parkinson’s (created by GSK), a second knock-in LRRK2 mouse model generated by MJFF, LRRK2 kinase inhibitors from GSK and Merck, and state-of-the-art mass spectrometry. These tools — and the collaborative spirit that united the partners — were necessary to make this finding.
“This unique model of collaboration and our systematic approach across laboratories using advanced technologies and layers of confirmation provide a firm foundation from which to continue this line of investigation and further refine our understanding of the LRRK2 Rab relationship,” said Dario Alessi, PhD, Director of the Protein Phosphorylation and Ubiquitylation Unit at the University of Dundee.
With additional MJFF funding, this research group now is working to further characterize the Rab proteins modified by LRRK2 and to understand how an imbalance in cellular trafficking leads to the degeneration of neurons seen in Parkinson’s disease.
More information about LRRK2 in this Youtube Video:

About the Max Planck Institute of Biochemistry
The Max Planck Institute of Biochemistry (MPIB) belongs to the Max Planck Society, an independent, non-profit research organization dedicated to top level basic research. As one of the largest Institutes of the Max Planck Society, 850 employees from 45 nations work here in the field of life sciences. In currently eight departments and about 25 research groups, the scientists contribute to the newest findings in the areas of biochemistry, cell biology, structural biology, biophysics and molecular science. The MPIB in Munich-Martinsried is part of the local life-science-campus where two Max Planck Institutes, a Helmholtz Center, the Gene-Center, several bio-medical faculties of two Munich universities and several biotech-companies are located in close proximity. http://www.biochem.mpg.de

 

About the University of Dundee
The University of Dundee is the top ranked University in the UK for biological sciences, according to the 2014 Research Excellence Framework. Dundee is internationally recognised for the quality of its teaching and research and has a core mission to transform lives across society. More than 17,000 students are enrolled at Dundee, helping make the city Scotland’s most student-friendly. The University is the central hub for a multi-million pound biotechnology sector in the east of Scotland, which now accounts for 16% of the local economy. http://www.dundee.ac.uk

 

About The Michael J. Fox Foundation for Parkinson’s Research
As the world’s largest nonprofit funder of Parkinson’s research, The Michael J. Fox Foundation is dedicated to accelerating a cure for Parkinson’s disease and improved therapies for those living with the condition today. The Foundation pursues its goals through an aggressively funded, highly targeted research program coupled with active global engagement of scientists, Parkinson’s patients, business leaders, clinical trial participants, donors and volunteers. In addition to funding more than $525 million in research to date, the Foundation has fundamentally altered the trajectory of progress toward a cure. Operating at the hub of worldwide Parkinson’s research, the Foundation forges groundbreaking collaborations with industry leaders, academic scientists and government research funders; increases the flow of participants into Parkinson’s disease clinical trials with its online tool, Fox Trial Finder; promotes Parkinson’s awareness through high-profile advocacy, events and outreach; and coordinates the grassroots involvement of thousands of Team Fox members around the world.

Source: Michael J Fox Foundation
https://www.michaeljfox.org/foundation/publication-detail.html?id=598&category=7&et_cid=466921&et_rid

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Novel Mechanisms of Resistance to Novel Agents

 

Curators: Larry H. Berstein, M.D. FACP & Stephen J. Williams, Ph.D.

For most of the history of chemotherapy drug development, predicting the possible mechanisms of drug resistance that ensued could be surmised from the drug’s pharmacologic mechanism of action. In other words, a tumor would develop resistance merely by altering the pathways/systems which the drug relied on for mechanism of action. For example, as elucidated in later chapters in this book, most cytotoxic chemotherapies like cisplatin and cyclophosphamide were developed to bind DNA and disrupt the cycling cell, thereby resulting in cell cycle arrest and eventually cell death or resulting in such a degree of genotoxicity which would result in great amount of DNA fragmentation. These DNA-damaging agents efficacy was shown to be reliant on their ability to form DNA adducts and lesions. Therefore increasing DNA repair could result in a tumor cell becoming resistant to these drugs. In addition, if drug concentration was merely decreased in these cells, by an enhanced drug efflux as seen with the ABC transporters, then there would be less drug available for these DNA adducts to be generated. A plethora of literature has been generated on this particular topic.

However in the era of chemotherapies developed against targets only expressed in tumor cells (such as Gleevec against the Bcr-Abl fusion protein in chronic myeloid leukemia), this paradigm had changed as clinical cases of resistance had rapidly developed soon after the advent of these compounds and new paradigms of resistance mechanisms were discovered.

speed of imitinib resistance

Imatinib resistance can be seen quickly after initiation of therapy

mellobcrablresistamplification

Speed of imatinib resistance a result of rapid gene amplification of BCR/ABL target, thereby decreasing imatinib efficacy

 

 

 

 

 

 

 

 

 

 

Although there are many other new mechanisms of resistance to personalized medicine agents (which are discussed later in the chapter) this post is a curation of cellular changes which are not commonly discussed in reviews of chemoresistance and separated in three main categories:

Cellular Diversity and Adaptation

Identifying Cancers and Resistance

Cancer Drug-Resistance Mechanism

p53 tumor drug resistance gene target

Variability of Gene Expression and Drug Resistance

 

Expression of microRNAs and alterations in RNA resulting in chemo-resistance

Drug-resistance Mechanism in Tumor Cells

Overexpression of miR-200c induces chemoresistance in esophageal cancers mediated through activation of the Akt signaling pathway

 

The miRNA–drug resistance connection: a new era of personalized medicine using noncoding RNA begins

 

Gene Duplication of Therapeutic Target

 

The advent of Gleevec (imatinib) had issued in a new era of chemotherapy, a personalized medicine approach by determining the and a lifesaver to chronic myeloid leukemia (CML) patients whose tumors displayed expression of the Bcr-Abl fusion gene. However it was not long before clinical resistance was seen to this therapy and, it was shown amplification of the drug target can lead to tumor outgrowth despite adequate drug exposure. le Coutre, Weisberg and Mahon23, 24, 25 all independently generated imatinib-resistant clones through serial passage of the cells in imatinib-containing media and demonstrated elevated Abl kinase activity due to a genetic amplification of the Bcr–Abl sequence. However, all of these samples were derived in vitro and may not represent a true mode of clinical resistance. Nevertheless, Gorre et al.26 obtained specimens, directly patients demonstrating imatinib resistance, and using fluorescence in situ hybridization analysis, genetic duplication of the Bcr–Abl gene was identified as one possible source of the resistance. Additional sporadic examples of amplification of the Bcr–Abl sequence have been clinically described, but the majority of patients presenting with either primary or secondary imatinib resistance fail to clinically demonstrate Abl amplification as a primary mode of treatment failure.

This is seen in the following papers:

Clinical resistance to STI-571 cancer therapy caused by BCR-ABL gene mutation or amplification.Gorre ME, Mohammed M, Ellwood K, Hsu N, Paquette R, Rao PN, Sawyers CL. Science. 2001 Aug 3;293(5531):876-80. Epub 2001 Jun 21.

and in another original paper by le Coutre et. al.

Induction of resistance to the Abelson inhibitor STI571 in human leukemic cells through gene amplification. le Coutre P1, Tassi E, Varella-Garcia M, Barni R, Mologni L, Cabrita G, Marchesi E, Supino R, Gambacorti-Passerini C. Blood. 2000 Mar 1;95(5):1758-66

The 2-phenylaminopyrimidine derivative STI571 has been shown to selectively inhibit the tyrosine kinase domain of the oncogenic bcr/abl fusion protein. The activity of this inhibitor has been demonstrated so far both in vitro with bcr/abl expressing cells derived from leukemic patients, and in vivo on nude mice inoculated with bcr/abl positive cells. Yet, no information is available on whether leukemic cells can develop resistance to bcr/abl inhibition. The human bcr/abl expressing cell line LAMA84 was cultured with increasing concentrations of STI571. After approximately 6 months of culture, a new cell line was obtained and named LAMA84R. This newly selected cell line showed an IC50 for the STI571 (1.0 microM) 10-fold higher than the IC50 (0.1 microM) of the parental sensitive cell line. Treatment with STI571 was shown to increase both the early and late apoptotic fraction in LAMA84 but not in LAMA84R. The induction of apoptosis in LAMA84 was associated with the activation of caspase 3-like activity, which did not develop in the resistant LAMA84R cell line. LAMA84R cells showed increased levels of bcr/abl protein and mRNA when compared to LAMA84 cells. FISH analysis with BCR- and ABL-specific probes in LAMA84R cells revealed the presence of a marker chromosome containing approximately 13 to 14 copies of the BCR/ABL gene. Thus, overexpression of the Bcr/Abl protein mediated through gene amplification is associated with and probably determines resistance of human leukemic cells to STI571 in vitro. (Blood. 2000;95:1758-1766)

This is actually the opposite case with other personalized therapies like the EGFR inhibitor gefinitib where actually the AMPLIFICATION of the therapeutic target EGFR is correlated with better response to drug in

Molecular mechanisms of epidermal growth factor receptor (EGFR) activation and response to gefitinib and other EGFR-targeting drugs.Ono M, Kuwano M. Clin Cancer Res. 2006 Dec 15;12(24):7242-51. Review.

Abstract

The epidermal growth factor receptor (EGFR) family of receptor tyrosine kinases, including EGFR, HER2/erbB2, and HER3/erbB3, is an attractive target for antitumor strategies. Aberrant EGFR signaling is correlated with progression of various malignancies, and somatic tyrosine kinase domain mutations in the EGFR gene have been discovered in patients with non-small cell lung cancer responding to EGFR-targeting small molecular agents, such as gefitinib and erlotinib. EGFR overexpression is thought to be the principal mechanism of activation in various malignant tumors. Moreover, an increased EGFR copy number is associated with improved survival in non-small cell lung cancer patients, suggesting that increased expression of mutant and/or wild-type EGFR molecules could be molecular determinants of responses to gefitinib. However, as EGFR mutations and/or gene gains are not observed in all patients who respond partially to treatment, alternative mechanisms might confer sensitivity to EGFR-targeting agents. Preclinical studies showed that sensitivity to EGFR tyrosine kinase inhibitors depends on how closely cell survival and growth signalings are coupled with EGFR, and also with HER2 and HER3, in each cancer. This review also describes a possible association between EGFR phosphorylation and drug sensitivity in cancer cells, as well as discussing the antiangiogenic effect of gefitinib in association with EGFR activation and phosphatidylinositol 3-kinase/Akt activation in vascular endothelial cells.

 

Mutant Variants of Therapeutic Target

 

resistant subclones in tissue samples and Tyrosine Kinase tumor activity

 

Mitochondrial Isocitrate Dehydrogenase and Variants

Mutational Landscape of Rare Childhood Brain Cancer: Analysis of 60 Intercranial Germ Cell Tumor Cases using NGS, SNP and Expression Array Analysis – Signaling Pathways KIT/RAS are affected by mutations in IGCTs

 

AND seen with the ALK inhibitors as well (as seen in the following papers

Acquisition of cancer stem cell-like properties in non-small cell lung cancer with acquired resistance to afatinib.

Hashida S, Yamamoto H, Shien K, Miyoshi Y, Ohtsuka T, Suzawa K, Watanabe M, Maki Y, Soh J, Asano H, Tsukuda K, Miyoshi S, Toyooka S. Cancer Sci. 2015 Oct;106(10):1377-84. doi: 10.1111/cas.12749. Epub 2015 Sep 30.

In vivo imaging models of bone and brain metastases and pleural carcinomatosis with a novel human EML4-ALK lung cancer cell line.

Nanjo S, Nakagawa T, Takeuchi S, Kita K, Fukuda K, Nakada M, Uehara H, Nishihara H, Hara E, Uramoto H, Tanaka F, Yano S. Cancer Sci. 2015 Mar;106(3):244-52. doi: 10.1111/cas.12600. Epub 2015 Feb 17.

Identification of a novel HIP1-ALK fusion variant in Non-Small-Cell Lung Cancer (NSCLC) and discovery of ALK I1171 (I1171N/S) mutations in two ALK-rearranged NSCLC patients with resistance to Alectinib. Ou SH, Klempner SJ, Greenbowe JR, Azada M, Schrock AB, Ali SM, Ross JS, Stephens PJ, Miller VA.J Thorac Oncol. 2014 Dec;9(12):1821-5

Reports of chemoresistance due to variants have also been seen with the BRAF inhibitors like vemurafenib and dabrafenib:

The RAC1 P29S hotspot mutation in melanoma confers resistance to pharmacological inhibition of RAF.

Watson IR, Li L, Cabeceiras PK, Mahdavi M, Gutschner T, Genovese G, Wang G, Fang Z, Tepper JM, Stemke-Hale K, Tsai KY, Davies MA, Mills GB, Chin L.Cancer Res. 2014 Sep 1;74(17):4845-52. doi: 10.1158/0008-5472.CAN-14-1232-T. Epub 2014 Jul 23

 

 

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False-Positive Mammogram Results May Be Linked to Higher Risk Later in Life

While screening mammograms aren’t perfect, they are the best way we have right now to detect breast cancer early, when it’s most treatable.

When a screening mammogram shows an abnormal area that looks like a cancer but turns out to be normal, it’s called a false positive. Ultimately the news is good: no breast cancer. But the suspicious area usually requires follow-up with more than one doctor, extra tests, and extra procedures, including a possible biopsy.

A large study suggests that women with false-positive mammogram results have a slightly higher risk of developing invasive breast cancer within the next 10 years.

The research was published online on Dec. 2, 2015 by the journal Cancer Epidemiology, Biomarkers & Prevention. Read the abstract of “Increased Risk of Developing Breast Cancer after a False-Positive Screening Mammogram.”

To do the study, the researchers looked at information from nearly 1.3 million women ages 40 to 70 with no family history of breast cancer who had screening mammograms from 1994 to 2009. The information came from the Breast Cancer Surveillance Consortium database, which is maintained by the National Cancer Institute.

The researchers found that the 1,297,906 women had a total of 2,207,942 screening mammograms. There were:

  • 159,448 false-positive results with a recommendation for more imaging
  • 22,892 false-positive results with a recommendation for biopsy
  • 2,025,602 negative mammograms

Women ages 40 to 49 made up the largest percentage of false-positive mammogram results with a recommendation for more imaging (33.1%). Women with dense breasts also were more likely to have false-positive results.

The researchers then compared the rates of invasive breast cancer between women who had false-positive mammogram results and women who had negative mammogram results:

  • there were 3.91 invasive breast cancers per 1,000 person-years of follow-up among women with negative mammogram results
  • there were 5.51 invasive breast cancers per 1,000 person-years of follow-up among women with false-positive mammogram results with a recommendation for more imaging
  • there were 7.01 invasive breast cancers per 1,000 person-years of follow-up among women with false-positive mammogram results with a recommendation for biopsy

The researchers said the 10-year risk of invasive breast cancer was:

  • 39% higher in women with false-positive results with a recommendation for more imaging
  • 76% higher in women with false-positive results with a recommendation for biopsy

compared to women with negative results.

It’s important to know that the increases above are increases in relative risk — the risk of a woman with a false-positive result relative to the risk of a woman with a negative result.

In terms of absolute risk, the increase is small:

  • women with false-positive results have about a 2% risk of developing invasive disease in the 10 years after the false-positive result
  • women with negative results have about a 1% risk of developing invasive disease in the 10 years after the negative result

The researchers didn’t offer an explanation about why false-positive mammogram results appear to be linked to a slightly higher risk of invasive disease. Many experts think that the subtle changes suggested on the mammogram may be an early clue to cancer before actual cancer exists.

It’s also important to know that this association has been suggested in other studies. But the large number of women in the study and the length of follow-up add more evidence that the link between false-positive results and a somewhat higher risk of invasive disease actually exists.

“The power of this study to show the association is very strong, particularly when you combine it with the results of the other studies that have been done,” said Richard Wender, M.D., chief of cancer control at the American Cancer Society, in an interview. “I think we can now say with confidence that women who have had a previous false-positive mammogram are at somewhat higher risk for breast cancer.”

The researchers who did this study want to incorporate false-positive mammogram results into models that predict breast cancer risk.

“Now that we have this information, our hope is that we can add it into existing risk-prediction models to improve their ability to discriminate between women who will go on to develop breast cancer and those who won’t,” said Louise Henderson, Ph.D., of the University of North Carolina Lineberger Comprehensive Cancer Center, who was the lead author of the study. “We should accept that a false-positive mammogram is a risk factor for predicting future risk of breast cancer.

“In clinical terms, that means women who have a false-positive mammogram need to be particularly vigilant about keeping up with regular mammographic screening,” she continued. “The clinicians caring for these women should have a way to track women who have had a false-positive and make sure that every effort is made to keep up to date with mammography.”

It’s important to know that a false-positive mammogram result doesn’t mean you will be diagnosed with breast cancer.

“Having any history of breast biopsies is associated with a higher risk,” said Breastcancer.org President and Founder Marisa Weiss, M.D. “Breast tissue that is dense or has proliferative changes tends to lead to questions on the breast imaging. Sometimes it leads to biopsies. In contrast, breast tissue that is boring, without any extra activity, rarely leads to any kind of biopsy. That kind of inactive breast tissue is less likely to develop breast cancer.”

“This study doesn’t suggest that having a false-positive leads to breast cancer,” said Brian Wojciechowski, M.D., Breastcancer.org’s medical adviser. “Rather, it reflects an association between breast cancer risk and abnormal breast imaging. Women should not worry that getting mammograms will increase their risk of breast cancer in the future.”

There’s only one of you and you deserve the best care possible. Don’t let any obstacles get in the way of your regular screening mammograms, especially if you’ve had a false-positive result.

  • If you’re worried about cost, talk to your doctor, a local hospital social worker, or staff members at a mammogram center. Ask about free programs in your area.
  • If you’re having problems scheduling a mammogram, call the National Cancer Institute (800-4-CANCER) or the American College of Radiology (800-227-5463) to find certified mammogram providers near you.
  • If you find mammograms painful, ask the mammography center staff members how the experience can be as easy and as comfortable as possible for you.
  • If you’re concerned about unknown results or being called back for more testing, talk to your doctor about what happens when mammogram results are unclear, as well as what to expect if you’re called back for more testing.

For more information on mammograms and other tests to detect and diagnose breast cancer, visit the Breastcancer.org Screening and Testing section.


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A Curated History of the Science Behind the Ovarian Cancer β-Blocker Trial

Curator: Stephen J. Williams, Ph.D.

 

This post is a follow-up on the two reports found in this Open Access Journal

https://pharmaceuticalintelligence.com/2015/09/16/ovarian-cancer-survival-increased-5-months-overall-with-beta-blockers-study-the-speaker/

AND

https://pharmaceuticalintelligence.com/2013/04/08/beta-blockers-help-in-better-survival-in-ovarian-cancer/

in order to explain some of the background which went into the development of these reports.

A recent paper by Anil Sood’s group at MD Anderson in Journal of Cancer: Clinical impact of selective and nonselective beta-blockers on survival in patients with ovarian cancer describes a retrospective pathologic evaluation of ovaries from patients taking various beta blockers for currently approved indications.

The history of this finding is quite interesting and, as I remember in a talk given by Dr. Sood in mid-2000’s, a microarray conducted by his lab had showed overexpression of the β2-AR (β2 adrenergic receptor in ovarian cancer cells relative to normal epithelium. At the time it appeared an interesting result however most of the cancer (and ovarian cancer) field were concentrating on the tyrosine kinase signaling pathways as potential therapeutic targets, as much promising translational research in this area was in focus at the time. As a result of this finding and noticing that sustained β-adrenergic stimulation can promote ovarian cancer cell growth (Sood, 2006), Dr. Sood’s group have been studying the effects of β-adrenergic signaling om ovarian cancer. In addition it has been shown that propanalol can block VEGF signaling and norepinephrine increased MMP2 and MMP9 expression, an effect mediated by the β2-AR.

The above re-post of a Scoop-IT describes promising results of a clinical trial for use of selective beta blockers in ovarian cancer.   As to date, there have been many clinical trials initiated in ovarian cancer and most have not met with success for example the following posts:

Good and Bad News Reported for Ovarian Cancer Therapy

a follow-up curation on the problems encountered with the PARP-inhibitor olaparib

enough is enough: Treat ‘Each Patient as an Individual’

which contains an interview with Dr. Maurie Markman (Vice President, Patient Oncology Services, and National Director for Medical Oncology, Cancer Treatment Centers of America) and Dr. Kathy D. Miller, Indiana University School of Medicine) and discusses how each patient’s ovarian cancer is genetically unique and needs to be treated as such

Therefore the mainstay therapy is still carboplatin plus a taxane (Taxotere, Abraxane). The results of this clinical trial show a 5 month improvement in survival, which for a deadly disease like ovarian cancer is a significant improvement.

First below is a SUMMARY of the paper’s methodology and findings.

Methods:

  • Four participating institutions collected retrospective patient data and pathology reports from 1425 patients diagnosed with epithelial ovarian cancer (EOC)
  • Medical records were evaluated for use of both selective and nonselective β-blockers
  • β-blockers were used for various indications however most common indication was treatment for hypertension (71% had used β1 selective blockers while rest of patients taking β blockers were given nonselective blockers for a host of other indications)
  • most patients had stage III/IV disease and in general older (median age 63 years)
  • The authors looked at overall survival (OS) however progression free survival PFS) was not calculated

Results:

  • Hypertension was associated with decreased survival (40.1 monts versus 47.4 months for normotensive patients)
  • Overall Survival for patients on any β blockers was 47.8 months versus 42.0 months for nonusers
  • Patients receiving nonselective β blockers has an OS of 94.9 months versus 38 months for EOC patients receiving β1-selective blockers
  • No effect of diabetes mellitus on survival

Authors Note on Limitations of Study:

  • Retrospective in nature
  • Lack of documentation of dosage, trade-name and duration of β-blocker use
  • Important to stratify patients on selectivity of β-blocker since Eskander et. al. found no difference of Progression Free Survival and non-selective β-blocker
  • Several β adrenergic receptor polymorphisms may exist and no downstream biomarker evaluated to determine effect on signaling; could it be a noncanonical effect?

The goal of this brief, added curation is to paint a historical picture, and highlight the scientific findings which led up to the rationale behind this clinical trial.

How the βeta Adrenergic Receptor (βAR) Became a Target for Ovarian Cancer

.

A. βAR and its signaling over-expressed in ovarian cancer

Role of mitogen-activated protein kinase/extracellular signal-regulated kinase cascade in gonadotropin-releasing hormone-induced growth inhibition of a human ovarian cancer cell line.

Kimura A, Ohmichi M, Kurachi H, Ikegami H, Hayakawa J, Tasaka K, Kanda Y, Nishio Y, Jikihara H, Matsuura N, Murata Y.

Cancer Res. 1999 Oct 15;59(20):5133-42.

Cyclic AMP induces integrin-mediated cell adhesion through Epac and Rap1 upon stimulation of the beta 2-adrenergic receptor.

Rangarajan S, Enserink JM, Kuiperij HB, de Rooij J, Price LS, Schwede F, Bos JL.

J Cell Biol. 2003 Feb 17;160(4):487-93. Epub 2003 Feb 10.

B. Mechanistic Link Between Chronic Stress From Excess Adrenergic Stimulation and Angiogenesis and Metastasis

Stress-related mediators stimulate vascular endothelial growth factor secretion by two ovarian cancer cell lines.

Lutgendorf SK, Cole S, Costanzo E, Bradley S, Coffin J, Jabbari S, Rainwater K, Ritchie JM, Yang M, Sood AK.

Clin Cancer Res. 2003 Oct 1;9(12):4514-21.PMID:

Norepinephrine up-regulates the expression of vascular endothelial growth factor, matrix metalloproteinase (MMP)-2, and MMP-9 in nasopharyngeal carcinoma tumor cells.

Yang EV, Sood AK, Chen M, Li Y, Eubank TD, Marsh CB, Jewell S, Flavahan NA, Morrison C, Yeh PE, Lemeshow S, Glaser R.

Cancer Res. 2006 Nov 1;66(21):10357-64.

VEGF is differentially regulated in multiple myeloma-derived cell lines by norepinephrine.

Yang EV, Donovan EL, Benson DM, Glaser R.

Brain Behav Immun. 2008 Mar;22(3):318-23. Epub 2007 Nov 5.

Chronic stress promotes tumor growth and angiogenesis in a mouse model of ovarian carcinoma.

Thaker PH, Han LY, Kamat AA, Arevalo JM, Takahashi R, Lu C, Jennings NB, Armaiz-Pena G, Bankson JA, Ravoori M, Merritt WM, Lin YG, Mangala LS, Kim TJ, Coleman RL, Landen CN, Li Y, Felix E, Sanguino AM, Newman RA, Lloyd M, Gershenson DM, Kundra V, Lopez-Berestein G, Lutgendorf SK, Cole SW, Sood AK.

Nat Med. 2006 Aug;12(8):939-44. Epub 2006 Jul 23.

Norepinephrine up-regulates the expression of vascular endothelial growth factor, matrix metalloproteinase (MMP)-2, and MMP-9 in nasopharyngeal carcinoma tumor cells.

Yang EV, Sood AK, Chen M, Li Y, Eubank TD, Marsh CB, Jewell S, Flavahan NA, Morrison C, Yeh PE, Lemeshow S, Glaser R.

Cancer Res. 2006 Nov 1;66(21):10357-64.

C. In Vivo Studies Confirm In Vitro Findings That Chronic Stress Via Adrenergic overstimulation Increases Ovarian Cancer Growth

Chronic stress promotes tumor growth and angiogenesis in a mouse model of ovarian carcinoma.

Thaker PH, Han LY, Kamat AA, Arevalo JM, Takahashi R, Lu C, Jennings NB, Armaiz-Pena G, Bankson JA, Ravoori M, Merritt WM, Lin YG, Mangala LS, Kim TJ, Coleman RL, Landen CN, Li Y, Felix E, Sanguino AM, Newman RA, Lloyd M, Gershenson DM, Kundra V, Lopez-Berestein G, Lutgendorf SK, Cole SW, Sood AK.

Nat Med. 2006 Aug;12(8):939-44. Epub 2006 Jul 23.

Stress hormone-mediated invasion of ovarian cancer cells.

Sood AK, Bhatty R, Kamat AA, Landen CN, Han L, Thaker PH, Li Y, Gershenson DM, Lutgendorf S, Cole SW.

Clin Cancer Res. 2006 Jan 15;12(2):369-75.

The neuroendocrine impact of chronic stress on cancer.

Thaker PH, Lutgendorf SK, Sood AK.

Cell Cycle. 2007 Feb 15;6(4):430-3. Epub 2007 Feb 9. Review.

Surgical stress promotes tumor growth in ovarian carcinoma.

Lee JW, Shahzad MM, Lin YG, Armaiz-Pena G, Mangala LS, Han HD, Kim HS, Nam EJ, Jennings NB, Halder J, Nick AM, Stone RL, Lu C, Lutgendorf SK, Cole SW, Lokshin AE, Sood AK.

Clin Cancer Res. 2009 Apr 15;15(8):2695-702. doi: 10.1158/1078-0432.CCR-08-2966. Epub 2009 Apr 7.

Sood group wanted to mimic the surgical stress after laparoscopic surgery to see if surgical stress would promote the growth of micrometasteses remaining after surgical tumor removal. Propranolol completely blocked the effects of surgical stress on tumor growth, indicating a critical role for beta-adrenergic receptor signaling in mediating the effects of surgical stress on tumor growth. In the HeyA8 and SKOV3ip1 models, surgery significantly increased microvessel density (CD31) and vascular endothelial growth factor expression, which were blocked by propranolol treatment. Tumor growth after surgery was decreased in a mouse null for βAR. Levels of cytokines G-CSF, IL-1a, IL-6, and IL-15were increased after surgery

Stress effects on FosB- and interleukin-8 (IL8)-driven ovarian cancer growth and metastasis J Biol Chem. 2010 Nov 12;285(46):35462-70. doi: 10.1074/jbc.M110.109579. Epub 2010 Sep 8.

Shahzad MM1, Arevalo JM, Armaiz-Pena GN, Lu C, Stone RL, Moreno-Smith M, Nishimura M, Lee JW, Jennings NB, Bottsford-Miller J, Vivas-Mejia P, Lutgendorf SK, Lopez-Berestein G, Bar-Eli M, Cole SW, Sood AK.

Free PMC Article

Abstract

A growing number of studies indicate that chronic stress can accelerate tumor growth due to sustained sympathetic nervous system activation. Our recent findings suggest that chronic stress is associated with increased IL8 levels. Here, we examined the molecular and biological significance of IL8 in stress-induced tumor growth. Norepinephrine (NE) treatment of ovarian cancer cells resulted in a 250-300% increase in IL8 protein and 240-320% increase in its mRNA levels. Epinephrine treatment resulted in similar increases. Moreover, NE treatment resulted in a 3.5-4-fold increase in IL8 promoter activity. These effects were blocked by propranolol. Promoter deletion analyses suggested that AP1 transcription factors might mediate catecholamine-stimulated up-regulation of IL8. siRNA inhibition studies identified FosB as the pivotal component responsible for IL8 regulation by NE. In vivo chronic stress resulted in increased tumor growth (by 221 and 235%; p < 0.01) in orthotopic xenograft models involving SKOV3ip1 and HeyA8 ovarian carcinoma cells. This enhanced tumor growth was completely blocked by IL8 or FosB gene silencing using 1,2-dioleoyl-sn-glycero-3-phosphatidylcholine nanoliposomes. IL8 and FosB silencing reduced microvessel density (based on CD31 staining) by 2.5- and 3.5-fold, respectively (p < 0.001). Our findings indicate that neurobehavioral stress leads to FosB-driven increases in IL8, which is associated with increased tumor growth and metastases. These findings may have implications for ovarian cancer management.

Dopamine blocks stress-mediated ovarian carcinoma growth.

Moreno-Smith M, Lu C, Shahzad MM, Pena GN, Allen JK, Stone RL, Mangala LS, Han HD, Kim HS, Farley D, Berestein GL, Cole SW, Lutgendorf SK, Sood AK.

Clin Cancer Res. 2011 Jun 1;17(11):3649-59. doi: 10.1158/1078-0432.CCR-10-2441. Epub 2011 Apr 29.

D. Additional mechanisms iincluding JAK/STAT modulation, prostaglandin synthesis, AKT, and Slug implicated in Stress (norepinephrine) induced increase in Ovarian Tumor Growth

Sustained adrenergic signaling leads to increased metastasis in ovarian cancer via increased PGE2 synthesis.

Nagaraja AS, Dorniak PL, Sadaoui NC, Kang Y, Lin T, Armaiz-Pena G, Wu SY, Rupaimoole R, Allen JK, Gharpure KM, Pradeep S, Zand B, Previs RA, Hansen JM, Ivan C, Rodriguez-Aguayo C, Yang P, Lopez-Berestein G, Lutgendorf SK, Cole SW, Sood AK.

Oncogene. 2015 Aug 10. doi: 10.1038/onc.2015.302. [Epub ahead of print]

The antihypertension drug doxazosin suppresses JAK/STATs phosphorylation and enhances the effects of IFN-α/γ-induced apoptosis.

Park MS, Kim BR, Kang S, Kim DY, Rho SB.

Genes Cancer. 2014 Nov;5(11-12):470-9.

hTERT mediates norepinephrine-induced Slug expression and ovarian cancer aggressiveness.

Choi MJ, Cho KH, Lee S, Bae YJ, Jeong KJ, Rha SY, Choi EJ, Park JH, Kim JM, Lee JS, Mills GB, Lee HY.

Oncogene. 2015 Jun;34(26):3402-12. doi: 10.1038/onc.2014.270. Epub 2014 Aug 25.

The antihypertension drug doxazosin inhibits tumor growth and angiogenesis by decreasing VEGFR-2/Akt/mTOR signaling and VEGF and HIF-1α expression.

Park MS, Kim BR, Dong SM, Lee SH, Kim DY, Rho SB.

Oncotarget. 2014 Jul 15;5(13):4935-44.

Meeting Abstracts on the Subject

From 2007 AACR Meeting

Neuroendocrine Modulation of Signal Transducer and Activator of Transcription-3 in Ovarian Cancer

  1. Requests for reprints:
    Anil K. Sood, Departments of Gynecologic Oncology and Cancer Biology, The University of Texas M. D. Anderson Cancer Center, 1155 Herman Pressler, CPB6.3244, Unit 1362, Houston, TX 77230-1439. Phone: 713-745-5266; Fax: 713-792-7586; E-mail: asood@mdanderson.org.

Abstract

There is growing evidence that chronic stress and other behavioral conditions are associated with cancer pathogenesis and progression, but the mechanisms involved in this association are poorly understood. We examined the effects of two mediators of stress, norepinephrine and epinephrine, on the activation of signal transducer and activator of transcription-3 (STAT3), a transcription factor that contributes to many promalignant pathways. Exposure of ovarian cancer cell lines to increasing concentrations of norepinephrine or epinephrine showed that both independently increased levels of phosphorylated STAT3 in a dose-dependent fashion. Immunolocalization and ELISA of nuclear extracts confirmed increased nuclear STAT3 in response to norepinephrine. Activation of STAT3 was inhibited by blockade of the β1- and β2-adrenergic receptors with propranolol, and by blocking protein kinase A with KT5720, but not with the α receptor blockers prazosin (α1) and/or yohimbine (α2). Catecholamine-mediated STAT3 activation was not inhibited by pretreatment with an anti–interleukin 6 (IL-6) antibody or with small interfering RNA (siRNA)–mediated decrease in IL-6 or gp130. Regarding the effects of STAT3 activation, exposure to norepinephrine resulted in an increase in invasion and matrix metalloproteinase (MMP-2 and MMP-9) production. These effects were completely blocked by STAT3-targeting siRNA. In mice, treatment with liposome-incorporated siRNA directed against STAT3 significantly reduced isoproterenol-stimulated tumor growth. These studies show IL-6–independent activation of STAT3 by norepinephrine and epinephrine, proceeding through the β1/β2-adrenergic receptors and protein kinase A, resulting in increased matrix metalloproteinase production, invasion, and in vivo tumor growth, which can be ameliorated by the down-regulation of STAT3. [Cancer Res 2007;67(21):10389–96]

From 2009 AACR Meeting

Abstract #2506: Functional \#946;2 adrenergic receptors (ADRB2) on human ovarian tumors portend worse clinical outcome

Abstract

Objective: Stress hormones such as catecholamines can augment tumor metastasis and angiogenesis; however, the prevalence and clinical significance of adrenergic receptors in human ovarian cancer is unknown and is the focus of the current study. Methods: After IRB approval, paraffin-embedded samples from 137 patients with invasive epithelial ovarian carcinoma were examined for \#946;1- and \#946;2-adrenergic receptor (ADRB1 and ADRB2, respectively) expression. Correlations with clinical outcomes were determined using parametric and non-parametric tests. Survival analyses were performed using the Kaplan-Meier method. Expression of ADRB1 and -2 was examined by quantitative RT-PCR in 15 freshly extracted human ovarian carcinoma cells. Human ovarian carcinoma cells then underwent time-variable adrenergic stimulation, and tumorigenic and angiogenic cytokine levels were examined by ELISA. Results: Sixty-six percent of the tumors had high expression of ADRB1; 80% of specimens highly expressed ADRB2. Univariate analyses demonstrated that high ADRB1 expression was associated with serous histology (p=0.03) and the presence of ascites (p=0.03), while high expression of ADRB2 was associated with advanced stage (p=0.008). Moreover, high ADRB2 expression was associated with the lower overall survival (2.2 vs. 6.5 years; p<0.001). In multivariate analysis, controlling for FIGO stage, grade, cytoreduction, age, and ADRB expression, only FIGO stage, cytoreduction status, age, and ADRB status retained statistical significance in predicting overall survival. In tumor cells freshly isolated from human ovarian cancers, 75% of samples had high expression of ADRB2 while most lacked ADRB1 compared to normal surface epithelium. Stimulation of the freshly isolated ADRB2-positive human ovarian cancer cells with norepinephrine resulted in increased levels of cAMP and increased angiogenic cytokines IL-6 and VEGF. Conclusions: ADRB2 are frequently found on human ovarian tumors and are strongly associated with poor clinical outcome. These findings support a direct mechanism by which stress hormones modulate ovarian cancer growth and metastasis as well as provide a basis for therapeutic targeting.

And from the 2015 AACR Meeting:

Abstract 3368: Sustained adrenergic signaling activates pro-inflammatory prostaglandin network in ovarian carcinoma

  1. Archana S. Nagaraja1,

Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA

Abstract

Purpose: Catecholamine mediated stress effects are known to induce production of various pro-inflammatory cytokines. However, the mechanism and functional effect of adrenergic signaling in driving inflammation via pro-inflammatory metabolites is currently unknown. Here we address the functional and biological consequences of adrenergic-induced Cox2/PGE2 axis activation in ovarian cancer metastasis.

Methods: We first analyzed global metabolic changes in tumors isolated from patients with known Center for Epidemiologic Studies Depression Scale (CES-D; depressive) scores and tumoral norepinephrine (NE) levels. Beta-adrenergic receptor (ADRB) positive cells (Skov3 and HeyA8) were used to study gene and protein levels of PTGS2 (cyclooxygenase2), PTGES (prostaglandin E synthase) and metabolite PGE2 in vitro and in vivo. To study tumor-specific effects on catecholamine-derived expression of PTGS2, we used a novel DOPC delivery system of PTGS2 siRNA.

Results: Our results revealed that levels of PGs were significantly increased in patients with high depressive scores (>16). PGE2 was upregulated by 2.38 fold when compared to the low CES-D scores. A similar trend was also observed with other pro-inflammatory eicosanoids, such as 6-keto prostaglandin F1 Alpha (2.03), prostaglandin A2 (1.39) and prostaglandin E1 (1.39). Exposure to NE resulted in increased PTGS2 and PTGES (prostaglandin E2 synthase) gene expression and protein levels in Skov3 and HeyA8. PGE2 ELISA confirmed that upon treatment with NE, PGE2 levels were increased in conditioned medium from Skov3 and HeyA8 cells. Treatment with a broad ADRB agonist (isoproterenol) or ADRB2 specific agonist (terbutaline) led to increases in expression of PTGS2 and PTGES as well as PGE2 levels in supernatant. Conversely, treatment with a broad antagonist (propranolol) or an ADRB2 specific antagonist (butoxamine) in the presence of NE abrogated gene expression changes of PTGS2 and PTGES. ChIP analysis showed enrichment of Nf-kB binding to the promoter region of PTGS2 and PTGES by 2.4 and 4.0 fold respectively when Skov3ip1 cells were treated with NE. Silencing PTGS2 resulted in significantly decreased migration (40%) and invasion (25%) of Skov3 cells in the presence of NE. Importantly, in the Skov3-ip1 restraint stress orthotopic model, silencing PTGS2 abrogated stress mediated effects and decreased tumor burden by 70% compared to control siRNA with restraint stress.

Conclusion Increased adrenergic stimulation results in a pro-inflammatory milieu mediated by prostaglandins that drives tumor progression and metastasis in ovarian cancer.

Citation Format: Archana S. Nagaraja, Piotr Dorniak, Nouara Sadaoui, Guillermo Armaiz-Pena, Behrouz Zand, Sherry Y. Wu, Julie K. Allen, Rajesha Rupaimoole, Cristian Rodriguez-Aguayo, Sunila Pradeep, Lin Tan, Rebecca A. Previs, Jean M. Hansen, Peiying Yang, Garbiel Lopez-Berestein, Susan K. Lutgendorf, Steve Cole, Anil K. Sood. Sustained adrenergic signaling activates pro-inflammatory prostaglandin network in ovarian carcinoma. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3368. doi:10.1158/1538-7445.AM2015-3368

Other Article in This Open Access Journal on Ovarian Cancer Include

Beta-Blockers help in better survival in ovarian cancer

Ovarian Cancer Survival Increased 5 Months Overall With Beta Blockers – Study – The Speaker

Model mimicking clinical profile of patients with ovarian cancer @ Yale School of Medicine

Preclinical study identifies ‘master’ proto-oncogene that regulates stress-induced ovarian cancer metastasis | MD Anderson Cancer Center

Beta-Blockers help in better survival in ovarian cancer

Role of Primary Cilia in Ovarian Cancer

Dasatinib in Combination With Other Drugs for Advanced, Recurrent Ovarian Cancer

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New Guidelines and Meeting Information on Advanced Thyroid Cancer as Reported by Cancer Network (Meeting Highlights)

 

Reporter: Stephen J. Williams, Ph.D.

Cancer Network presents exclusive coverage on thyroid cancer from the 15th International Thyroid Congress (ITC) and 85th Annual Meeting of the American Thyroid Association (ATA), held October 18-23 in Lake Buena Vista, Florida.

Vista, Florida.
Conference Reports
ATA Updates Guidelines for Differentiated Thyroid Cancers
Release of newly revised, evidence-based clinical management guidelines for thyroid nodules and differentiated thyroid cancers were announced at the 85th Annual Meeting of the ATA.
FAM83F Protein Implicated in Papillary Thyroid Cancer and Drug Resistance
The FAM83F protein contributes to papillary thyroid cancer cell viability and doxorubicin resistance, according to a study presented at the 85th Annual Meeting of the ATA.
Autophagy Implicated in Vemurafenib Resistance in BRAF-Mutant Thyroid Cancer
Preclinical findings suggest that autophagy inhibition might prove useful in overcoming BRAF-mutant thyroid cancers resistant to vemurafenib.

 

Summary of Newly Released Guidelines on Management of Thyroid Nodules and Differentiated Thyroid Cancers

See Cancer.gov for more information on thyroid cancer

Release of newly revised, evidence-based clinical management guidelines for thyroid nodules and differentiated thyroid cancers were announced at the 15th International Thyroid Congress (ITC) and 85th Annual Meeting of the American Thyroid Association (ATA) in Lake Buena Vista, Florida, and published in Thyroid.

  • The ATA Guidelines Taskforce on Thyroid Nodules and Differentiated Thyroid Cancer authored the guidelines. The Taskforce was chaired by Bryan R. Haugen, MD, of the University of Colorado School of Medicine in Aurora, Colorado.

The updated guidelines reflect

  • advances in the interpretation of biopsy and the use of molecular-marker studies in the clinical differentiation of benign thyroid nodules from thyroid cancer,
  • risk assessment,
  • cancer screening,
  • the management of benign thyroid nodules,
  • the diagnosis and the initial and long-term management of differentiated thyroid cancer.
  • Guidelines modified for long-term management of differentiated thyroid cancer
  • additional research and recommendations needed “for clinical trials and targeted therapy.”

The United States saw an estimated 63,000 newly diagnosed cases of thyroid cancer cases in 2014, up sharply from 37,200 in 2009, when the ATA guidelines were last revised.

– See more at: http://www.cancernetwork.com/ata-2015-thyroid-cancer/ata-updates-guidelines-differentiated-thyroid-cancers?GUID=D63BFB74-A7FD-4892-846F-A7D1FFE0F131&XGUID=&rememberme=1&ts=20102015#sthash.yXbBrS2x.dpuf

 

 

 

Vemurafenib

From 2011 FDA press release on approval of vemurafenib:

FDA NEWS RELEASE

For Immediate Release: Aug. 17, 2011
Media Inquiries: Erica Jefferson, 301-796-4988, erica.jefferson@fda.hhs.gov
Consumer Inquiries: 888-INFO-FDA

FDA approves Zelboraf and companion diagnostic test for late-stage skin cancer
Second melanoma drug approved this year that improves overall survival

The U.S. Food and Drug Administration today approved Zelboraf (vemurafenib), a drug to treat patients with late-stage (metastatic) or unresectable (cannot be removed by surgery) melanoma, the most dangerous type of skin cancer.

Zelboraf is specifically indicated for the treatment of patients with melanoma whose tumors express a gene mutation called BRAF V600E. The drug has not been studied in patients whose melanoma tests negative for that mutation by an FDA approved diagnostic.

Zelboraf is being approved with a first-of-a-kind test called the cobas 4800 BRAF V600 Mutation Test, a companion diagnostic that will help determine if a patient’s melanoma cells have the BRAF V600E mutation.

The BRAF protein is normally involved in regulating cell growth, but is mutated in about half of the patients with late-stage melanomas. Zelboraf is a BRAF inhibitor that is able to block the function of the V600E-mutated BRAF protein.

“This has been an important year for patients with late-stage melanoma. Zelboraf is the second new cancer drug approved that demonstrates an improvement in overall survival,” said Richard Pazdur, M.D., director of the Office of Oncology Drug Products in the FDA’s Center for Drug Evaluation and Research. “In March, we approved Yervoy (ipilimumab), another new treatment for late-stage melanoma that also showed patients live longer after receiving the drug.”

Zelboraf was reviewed under the FDA’s priority review program that provides for an expedited six-month review of drugs that may offer major advances in treatment or that provide a treatment when no adequate therapy exists. Zelboraf and the companion BRAF V600E test are being approved ahead of the drug’s Oct. 28, 2011 goal date and the companion diagnostics’ Nov. 12, 2011 goal date.

Zelboraf’s safety and effectiveness were established in a single international trial of 675 patients with late-stage melanoma with the BRAF V600E mutation who had not received prior therapy. Patients were assigned to receive either Zelboraf or dacarbazine, another anti-cancer therapy. The trial was designed to measure overall survival (the length of time between start of treatment and death of a patient).

The median survival (the length of time a patient lives after treatment) of patients receiving Zelboraf has not been reached (77 percent still living) while the median survival for those who received dacarbazine was 8 months (64 percent still living).

“Today’s approval of Zelboraf and the cobas test is a great example of how companion diagnostics can be developed and used to ensure patients are exposed to highly effective, more personalized therapies in a safe manner,” said Alberto Gutierrez, Ph.D., director of the Office of In Vitro Diagnostic Device Evaluation and Safety in the FDA’s Center for Devices and Radiological Health.

The FDA’s approval of the cobas 4800 BRAF V600 Mutation Test was based on data from the clinical study that also evaluated the safety and effectiveness of Zelboraf. Samples of a patient’s melanoma tissue were collected to test for the mutation.

The most common side effects reported in patients receiving Zelboraf included joint pain, rash, hair loss, fatigue, nausea, and skin sensitivity when exposed to the sun. About 26 percent of patients developed a skin-related cancer called cutaneous squamous cell carcinoma, which was managed with surgery. Patients treated with Zelboraf should avoid sun exposure.

Zelboraf is being approved with a Medication Guide to inform health care professionals and patients of Zelboraf’s potential risks.

In July 2011, the FDA issued a new draft guidance to facilitate the development and review of companion diagnostics. The guidance, currently available for public comment, is intended to provide companies with guidance on the agency’s policy for reviewing a companion diagnostic and the corresponding drug therapy.

Melanoma is the leading cause of death from skin disease. The National Cancer Institute estimated that 68,130 new cases of melanoma were diagnosed in the United States during 2010; about 8,700 people died from the disease.

Zelboraf is marketed by South San Francisco based-Genentech, a member of the Roche Group. The cobas 4800 BRAF V600 Mutation Test is manufactured by Roche Molecular Systems in Pleasanton, Calif.

 

More Articles in this Open Access Journal on Thyroid Cancer Include

 

The Experience of a Patient with Thyroid Cancer

Thyroid Cancer: The Evolution of Treatment Options

The Relation between Coagulation and Cancer affects Supportive Treatments

 

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