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Archive for the ‘Pharmaceutical Discovery’ Category


Imaging of Cancer Cells

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Microscope uses nanosecond-speed laser and deep learning to detect cancer cells more efficiently

April 13, 2016

Scientists at the California NanoSystems Institute at UCLA have developed a new technique for identifying cancer cells in blood samples faster and more accurately than the current standard methods.

In one common approach to testing for cancer, doctors add biochemicals to blood samples. Those biochemicals attach biological “labels” to the cancer cells, and those labels enable instruments to detect and identify them. However, the biochemicals can damage the cells and render the samples unusable for future analyses. There are other current techniques that don’t use labeling but can be inaccurate because they identify cancer cells based only on one physical characteristic.

Time-stretch quantitative phase imaging (TS-QPI) and analytics system

The new technique images cells without destroying them and can identify 16 physical characteristics — including size, granularity and biomass — instead of just one.

The new technique combines two components that were invented at UCLA:

A “photonic time stretch” microscope, which is capable of quickly imaging cells in blood samples. Invented by Barham Jalali, professor and Northrop-Grumman Optoelectronics Chair in electrical engineering, it works by taking pictures of flowing blood cells using laser bursts (similar to how a camera uses a flash). Each flash only lasts nanoseconds (billionths of a second) to avoid damage to cells, but that normally means the images are both too weak to be detected and too fast to be digitized by normal instrumentation. The new microscope overcomes those challenges by using specially designed optics that amplify and boost the clarity of the images, and simultaneously slow them down enough to be detected and digitized at a rate of 36 million images per second.

A deep learning computer program, which identifies cancer cells with more than 95 percent accuracy. Deep learning is a form of artificial intelligence that uses complex algorithms to extract patterns and knowledge from rich multidimenstional datasets, with the goal of achieving accurate decision making.

The study was published in the open-access journal Nature Scientific Reports. The researchers write in the paper that the system could lead to data-driven diagnoses by cells’ physical characteristics, which could allow quicker and earlier diagnoses of cancer, for example, and better understanding of the tumor-specific gene expression in cells, which could facilitate new treatments for disease.

The research was supported by NantWorks, LLC.

 

Abstract of Deep Learning in Label-free Cell Classification

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.

references:

Claire Lifan Chen, Ata Mahjoubfar, Li-Chia Tai, Ian K. Blaby, Allen Huang, Kayvan Reza Niazi & Bahram Jalali. Deep Learning in Label-free Cell Classification. Scientific Reports 6, Article number: 21471 (2016); doi:10.1038/srep21471 (open access)

Supplementary Information

 

Deep Learning in Label-free Cell Classification

Claire Lifan Chen, Ata Mahjoubfar, Li-Chia Tai, Ian K. Blaby, Allen Huang,Kayvan Reza Niazi & Bahram Jalali

Scientific Reports 6, Article number: 21471 (2016)    http://dx.doi.org:/10.1038/srep21471

Deep learning extracts patterns and knowledge from rich multidimenstional datasets. While it is extensively used for image recognition and speech processing, its application to label-free classification of cells has not been exploited. Flow cytometry is a powerful tool for large-scale cell analysis due to its ability to measure anisotropic elastic light scattering of millions of individual cells as well as emission of fluorescent labels conjugated to cells1,2. However, each cell is represented with single values per detection channels (forward scatter, side scatter, and emission bands) and often requires labeling with specific biomarkers for acceptable classification accuracy1,3. Imaging flow cytometry4,5 on the other hand captures images of cells, revealing significantly more information about the cells. For example, it can distinguish clusters and debris that would otherwise result in false positive identification in a conventional flow cytometer based on light scattering6.

In addition to classification accuracy, the throughput is another critical specification of a flow cytometer. Indeed high throughput, typically 100,000 cells per second, is needed to screen a large enough cell population to find rare abnormal cells that are indicative of early stage diseases. However there is a fundamental trade-off between throughput and accuracy in any measurement system7,8. For example, imaging flow cytometers face a throughput limit imposed by the speed of the CCD or the CMOS cameras, a number that is approximately 2000 cells/s for present systems9. Higher flow rates lead to blurred cell images due to the finite camera shutter speed. Many applications of flow analyzers such as cancer diagnostics, drug discovery, biofuel development, and emulsion characterization require classification of large sample sizes with a high-degree of statistical accuracy10. This has fueled research into alternative optical diagnostic techniques for characterization of cells and particles in flow.

Recently, our group has developed a label-free imaging flow-cytometry technique based on coherent optical implementation of the photonic time stretch concept11. This instrument overcomes the trade-off between sensitivity and speed by using Amplified Time-stretch Dispersive Fourier Transform12,13,14,15. In time stretched imaging16, the object’s spatial information is encoded in the spectrum of laser pulses within a pulse duration of sub-nanoseconds (Fig. 1). Each pulse representing one frame of the camera is then stretched in time so that it can be digitized in real-time by an electronic analog-to-digital converter (ADC). The ultra-fast pulse illumination freezes the motion of high-speed cells or particles in flow to achieve blur-free imaging. Detection sensitivity is challenged by the low number of photons collected during the ultra-short shutter time (optical pulse width) and the drop in the peak optical power resulting from the time stretch. These issues are solved in time stretch imaging by implementing a low noise-figure Raman amplifier within the dispersive device that performs time stretching8,11,16. Moreover, warped stretch transform17,18can be used in time stretch imaging to achieve optical image compression and nonuniform spatial resolution over the field-of-view19. In the coherent version of the instrument, the time stretch imaging is combined with spectral interferometry to measure quantitative phase and intensity images in real-time and at high throughput20. Integrated with a microfluidic channel, coherent time stretch imaging system in this work measures both quantitative optical phase shift and loss of individual cells as a high-speed imaging flow cytometer, capturing 36 million images per second in flow rates as high as 10 meters per second, reaching up to 100,000 cells per second throughput.

Figure 1: Time stretch quantitative phase imaging (TS-QPI) and analytics system; A mode-locked laser followed by a nonlinear fiber, an erbium doped fiber amplifier (EDFA), and a wavelength-division multiplexing (WDM) filter generate and shape a train of broadband optical pulses. http://www.nature.com/article-assets/npg/srep/2016/160315/srep21471/images_hires/m685/srep21471-f1.jpg

 

Box 1: The pulse train is spatially dispersed into a train of rainbow flashes illuminating the target as line scans. The spatial features of the target are encoded into the spectrum of the broadband optical pulses, each representing a one-dimensional frame. The ultra-short optical pulse illumination freezes the motion of cells during high speed flow to achieve blur-free imaging with a throughput of 100,000 cells/s. The phase shift and intensity loss at each location within the field of view are embedded into the spectral interference patterns using a Michelson interferometer. Box 2: The interferogram pulses were then stretched in time so that spatial information could be mapped into time through time-stretch dispersive Fourier transform (TS-DFT), and then captured by a single pixel photodetector and an analog-to-digital converter (ADC). The loss of sensitivity at high shutter speed is compensated by stimulated Raman amplification during time stretch. Box 3: (a) Pulse synchronization; the time-domain signal carrying serially captured rainbow pulses is transformed into a series of one-dimensional spatial maps, which are used for forming line images. (b) The biomass density of a cell leads to a spatially varying optical phase shift. When a rainbow flash passes through the cells, the changes in refractive index at different locations will cause phase walk-off at interrogation wavelengths. Hilbert transformation and phase unwrapping are used to extract the spatial phase shift. (c) Decoding the phase shift in each pulse at each wavelength and remapping it into a pixel reveals the protein concentration distribution within cells. The optical loss induced by the cells, embedded in the pulse intensity variations, is obtained from the amplitude of the slowly varying envelope of the spectral interferograms. Thus, quantitative optical phase shift and intensity loss images are captured simultaneously. Both images are calibrated based on the regions where the cells are absent. Cell features describing morphology, granularity, biomass, etc are extracted from the images. (d) These biophysical features are used in a machine learning algorithm for high-accuracy label-free classification of the cells.

On another note, surface markers used to label cells, such as EpCAM21, are unavailable in some applications; for example, melanoma or pancreatic circulating tumor cells (CTCs) as well as some cancer stem cells are EpCAM-negative and will escape EpCAM-based detection platforms22. Furthermore, large-population cell sorting opens the doors to downstream operations, where the negative impacts of labels on cellular behavior and viability are often unacceptable23. Cell labels may cause activating/inhibitory signal transduction, altering the behavior of the desired cellular subtypes, potentially leading to errors in downstream analysis, such as DNA sequencing and subpopulation regrowth. In this way, quantitative phase imaging (QPI) methods24,25,26,27 that categorize unlabeled living cells with high accuracy are needed. Coherent time stretch imaging is a method that enables quantitative phase imaging at ultrahigh throughput for non-invasive label-free screening of large number of cells.

In this work, the information of quantitative optical loss and phase images are fused into expert designed features, leading to a record label-free classification accuracy when combined with deep learning. Image mining techniques are applied, for the first time, to time stretch quantitative phase imaging to measure biophysical attributes including protein concentration, optical loss, and morphological features of single cells at an ultrahigh flow rate and in a label-free fashion. These attributes differ widely28,29,30,31 among cells and their variations reflect important information of genotypes and physiological stimuli32. The multiplexed biophysical features thus lead to information-rich hyper-dimensional representation of the cells for label-free classification with high statistical precision.

We further improved the accuracy, repeatability, and the balance between sensitivity and specificity of our label-free cell classification by a novel machine learning pipeline, which harnesses the advantages of multivariate supervised learning, as well as unique training by evolutionary global optimization of receiver operating characteristics (ROC). To demonstrate sensitivity, specificity, and accuracy of multi-feature label-free flow cytometry using our technique, we classified (1) OT-IIhybridoma T-lymphocytes and SW-480 colon cancer epithelial cells, and (2) Chlamydomonas reinhardtii algal cells (herein referred to as Chlamydomonas) based on their lipid content, which is related to the yield in biofuel production. Our preliminary results show that compared to classification by individual biophysical parameters, our label-free hyperdimensional technique improves the detection accuracy from 77.8% to 95.5%, or in other words, reduces the classification inaccuracy by about five times.     ……..

 

Feature Extraction

The decomposed components of sequential line scans form pairs of spatial maps, namely, optical phase and loss images as shown in Fig. 2 (see Section Methods: Image Reconstruction). These images are used to obtain biophysical fingerprints of the cells8,36. With domain expertise, raw images are fused and transformed into a suitable set of biophysical features, listed in Table 1, which the deep learning model further converts into learned features for improved classification.

The new technique combines two components that were invented at UCLA:

A “photonic time stretch” microscope, which is capable of quickly imaging cells in blood samples. Invented by Barham Jalali, professor and Northrop-Grumman Optoelectronics Chair in electrical engineering, it works by taking pictures of flowing blood cells using laser bursts (similar to how a camera uses a flash). Each flash only lasts nanoseconds (billionths of a second) to avoid damage to cells, but that normally means the images are both too weak to be detected and too fast to be digitized by normal instrumentation. The new microscope overcomes those challenges by using specially designed optics that amplify and boost the clarity of the images, and simultaneously slow them down enough to be detected and digitized at a rate of 36 million images per second.

A deep learning computer program, which identifies cancer cells with more than 95 percent accuracy. Deep learning is a form of artificial intelligence that uses complex algorithms to extract patterns and knowledge from rich multidimenstional datasets, with the goal of achieving accurate decision making.

The study was published in the open-access journal Nature Scientific Reports. The researchers write in the paper that the system could lead to data-driven diagnoses by cells’ physical characteristics, which could allow quicker and earlier diagnoses of cancer, for example, and better understanding of the tumor-specific gene expression in cells, which could facilitate new treatments for disease.

The research was supported by NantWorks, LLC.

 

http://www.nature.com/article-assets/npg/srep/2016/160315/srep21471/images_hires/m685/srep21471-f2.jpg

The optical loss images of the cells are affected by the attenuation of multiplexed wavelength components passing through the cells. The attenuation itself is governed by the absorption of the light in cells as well as the scattering from the surface of the cells and from the internal cell organelles. The optical loss image is derived from the low frequency component of the pulse interferograms. The optical phase image is extracted from the analytic form of the high frequency component of the pulse interferograms using Hilbert Transformation, followed by a phase unwrapping algorithm. Details of these derivations can be found in Section Methods. Also, supplementary Videos 1 and 2 show measurements of cell-induced optical path length difference by TS-QPI at four different points along the rainbow for OT-II and SW-480, respectively.

Table 1: List of extracted features.

Feature Name    Description         Category

 

Figure 3: Biophysical features formed by image fusion.

(a) Pairwise correlation matrix visualized as a heat map. The map depicts the correlation between all major 16 features extracted from the quantitative images. Diagonal elements of the matrix represent correlation of each parameter with itself, i.e. the autocorrelation. The subsets in box 1, box 2, and box 3 show high correlation because they are mainly related to morphological, optical phase, and optical loss feature categories, respectively. (b) Ranking of biophysical features based on their AUCs in single-feature classification. Blue bars show performance of the morphological parameters, which includes diameter along the interrogation rainbow, diameter along the flow direction, tight cell area, loose cell area, perimeter, circularity, major axis length, orientation, and median radius. As expected, morphology contains most information, but other biophysical features can contribute to improved performance of label-free cell classification. Orange bars show optical phase shift features i.e. optical path length differences and refractive index difference. Green bars show optical loss features representing scattering and absorption by the cell. The best performed feature in these three categories are marked in red.

Figure 4: Machine learning pipeline. Information of quantitative optical phase and loss images are fused to extract multivariate biophysical features of each cell, which are fed into a fully-connected neural network.

The neural network maps input features by a chain of weighted sum and nonlinear activation functions into learned feature space, convenient for classification. This deep neural network is globally trained via area under the curve (AUC) of the receiver operating characteristics (ROC). Each ROC curve corresponds to a set of weights for connections to an output node, generated by scanning the weight of the bias node. The training process maximizes AUC, pushing the ROC curve toward the upper left corner, which means improved sensitivity and specificity in classification.

….   How to cite this article: Chen, C. L. et al. Deep Learning in Label-free Cell Classification.

Sci. Rep. 6, 21471; http://dx.doi.org:/10.1038/srep21471

 

Computer Algorithm Helps Characterize Cancerous Genomic Variations

http://www.genengnews.com/gen-news-highlights/computer-algorithm-helps-characterize-cancerous-genomic-variations/81252626/

To better characterize the functional context of genomic variations in cancer, researchers developed a new computer algorithm called REVEALER. [UC San Diego Health]

Scientists at the University of California San Diego School of Medicine and the Broad Institute say they have developed a new computer algorithm—REVEALER—to better characterize the functional context of genomic variations in cancer. The tool, described in a paper (“Characterizing Genomic Alterations in Cancer by Complementary Functional Associations”) published in Nature Biotechnology, is designed to help researchers identify groups of genetic variations that together associate with a particular way cancer cells get activated, or how they respond to certain treatments.

REVEALER is available for free to the global scientific community via the bioinformatics software portal GenePattern.org.

“This computational analysis method effectively uncovers the functional context of genomic alterations, such as gene mutations, amplifications, or deletions, that drive tumor formation,” said senior author Pablo Tamayo, Ph.D., professor and co-director of the UC San Diego Moores Cancer Center Genomics and Computational Biology Shared Resource.

Dr. Tamayo and team tested REVEALER using The Cancer Genome Atlas (TCGA), the NIH’s database of genomic information from more than 500 human tumors representing many cancer types. REVEALER revealed gene alterations associated with the activation of several cellular processes known to play a role in tumor development and response to certain drugs. Some of these gene mutations were already known, but others were new.

For example, the researchers discovered new activating genomic abnormalities for beta-catenin, a cancer-promoting protein, and for the oxidative stress response that some cancers hijack to increase their viability.

REVEALER requires as input high-quality genomic data and a significant number of cancer samples, which can be a challenge, according to Dr. Tamayo. But REVEALER is more sensitive at detecting similarities between different types of genomic features and less dependent on simplifying statistical assumptions, compared to other methods, he adds.

“This study demonstrates the potential of combining functional profiling of cells with the characterizations of cancer genomes via next-generation sequencing,” said co-senior author Jill P. Mesirov, Ph.D., professor and associate vice chancellor for computational health sciences at UC San Diego School of Medicine.

 

Characterizing genomic alterations in cancer by complementary functional associations

Jong Wook Kim, Olga B Botvinnik, Omar Abudayyeh, Chet Birger, et al.

Nature Biotechnology (2016)              http://dx.doi.org:/10.1038/nbt.3527

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes

 

Figure 2: REVEALER results for transcriptional activation of β-catenin in cancer.close

(a) This heatmap illustrates the use of the REVEALER approach to find complementary genomic alterations that match the transcriptional activation of β-catenin in cancer. The target profile is a TCF4 reporter that provides an estimate of…

 

An imaging-based platform for high-content, quantitative evaluation of therapeutic response in 3D tumour models

Jonathan P. Celli, Imran Rizvi, Adam R. Blanden, Iqbal Massodi, Michael D. Glidden, Brian W. Pogue & Tayyaba Hasan

Scientific Reports 4; 3751  (2014)    http://dx.doi.org:/10.1038/srep03751

While it is increasingly recognized that three-dimensional (3D) cell culture models recapitulate drug responses of human cancers with more fidelity than monolayer cultures, a lack of quantitative analysis methods limit their implementation for reliable and routine assessment of emerging therapies. Here, we introduce an approach based on computational analysis of fluorescence image data to provide high-content readouts of dose-dependent cytotoxicity, growth inhibition, treatment-induced architectural changes and size-dependent response in 3D tumour models. We demonstrate this approach in adherent 3D ovarian and pancreatic multiwell extracellular matrix tumour overlays subjected to a panel of clinically relevant cytotoxic modalities and appropriately designed controls for reliable quantification of fluorescence signal. This streamlined methodology reads out the high density of information embedded in 3D culture systems, while maintaining a level of speed and efficiency traditionally achieved with global colorimetric reporters in order to facilitate broader implementation of 3D tumour models in therapeutic screening.

The attrition rates for preclinical development of oncology therapeutics are particularly dismal due to a complex set of factors which includes 1) the failure of pre-clinical models to recapitulate determinants of in vivo treatment response, and 2) the limited ability of available assays to extract treatment-specific data integral to the complexities of therapeutic responses1,2,3. Three-dimensional (3D) tumour models have been shown to restore crucial stromal interactions which are missing in the more commonly used 2D cell culture and that influence tumour organization and architecture4,5,6,7,8, as well as therapeutic response9,10, multicellular resistance (MCR)11,12, drug penetration13,14, hypoxia15,16, and anti-apoptotic signaling17. However, such sophisticated models can only have an impact on therapeutic guidance if they are accompanied by robust quantitative assays, not only for cell viability but also for providing mechanistic insights related to the outcomes. While numerous assays for drug discovery exist18, they are generally not developed for use in 3D systems and are often inherently unsuitable. For example, colorimetric conversion products have been noted to bind to extracellular matrix (ECM)19 and traditional colorimetric cytotoxicity assays reduce treatment response to a single number reflecting a biochemical event that has been equated to cell viability (e.g. tetrazolium salt conversion20). Such approaches fail to provide insight into the spatial patterns of response within colonies, morphological or structural effects of drug response, or how overall culture viability may be obscuring the status of sub-populations that are resistant or partially responsive. Hence, the full benefit of implementing 3D tumour models in therapeutic development has yet to be realized for lack of analytical methods that describe the very aspects of treatment outcome that these systems restore.

Motivated by these factors, we introduce a new platform for quantitative in situ treatment assessment (qVISTA) in 3D tumour models based on computational analysis of information-dense biological image datasets (bioimage-informatics)21,22. This methodology provides software end-users with multiple levels of complexity in output content, from rapidly-interpreted dose response relationships to higher content quantitative insights into treatment-dependent architectural changes, spatial patterns of cytotoxicity within fields of multicellular structures, and statistical analysis of nodule-by-nodule size-dependent viability. The approach introduced here is cognizant of tradeoffs between optical resolution, data sampling (statistics), depth of field, and widespread usability (instrumentation requirement). Specifically, it is optimized for interpretation of fluorescent signals for disease-specific 3D tumour micronodules that are sufficiently small that thousands can be imaged simultaneously with little or no optical bias from widefield integration of signal along the optical axis of each object. At the core of our methodology is the premise that the copious numerical readouts gleaned from segmentation and interpretation of fluorescence signals in these image datasets can be converted into usable information to classify treatment effects comprehensively, without sacrificing the throughput of traditional screening approaches. It is hoped that this comprehensive treatment-assessment methodology will have significant impact in facilitating more sophisticated implementation of 3D cell culture models in preclinical screening by providing a level of content and biological relevance impossible with existing assays in monolayer cell culture in order to focus therapeutic targets and strategies before costly and tedious testing in animal models.

Using two different cell lines and as depicted in Figure 1, we adopt an ECM overlay method pioneered originally for 3D breast cancer models23, and developed in previous studies by us to model micrometastatic ovarian cancer19,24. This system leads to the formation of adherent multicellular 3D acini in approximately the same focal plane atop a laminin-rich ECM bed, implemented here in glass-bottom multiwell imaging plates for automated microscopy. The 3D nodules resultant from restoration of ECM signaling5,8, are heterogeneous in size24, in contrast to other 3D spheroid methods, such as rotary or hanging drop cultures10, in which cells are driven to aggregate into uniformly sized spheroids due to lack of an appropriate substrate to adhere to. Although the latter processes are also biologically relevant, it is the adherent tumour populations characteristic of advanced metastatic disease that are more likely to be managed with medical oncology, which are the focus of therapeutic evaluation herein. The heterogeneity in 3D structures formed via ECM overlay is validated here by endoscopic imaging ofin vivo tumours in orthotopic xenografts derived from the same cells (OVCAR-5).

 

Figure 1: A simplified schematic flow chart of imaging-based quantitative in situ treatment assessment (qVISTA) in 3D cell culture.

(This figure was prepared in Adobe Illustrator® software by MD Glidden, JP Celli and I Rizvi). A detailed breakdown of the image processing (Step 4) is provided in Supplemental Figure 1.

A critical component of the imaging-based strategy introduced here is the rational tradeoff of image-acquisition parameters for field of view, depth of field and optical resolution, and the development of image processing routines for appropriate removal of background, scaling of fluorescence signals from more than one channel and reliable segmentation of nodules. In order to obtain depth-resolved 3D structures for each nodule at sub-micron lateral resolution using a laser-scanning confocal system, it would require ~ 40 hours (at approximately 100 fields for each well with a 20× objective, times 1 minute/field for a coarse z-stack, times 24 wells) to image a single plate with the same coverage achieved in this study. Even if the resources were available to devote to such time-intensive image acquisition, not to mention the processing, the optical properties of the fluorophores would change during the required time frame for image acquisition, even with environmental controls to maintain culture viability during such extended imaging. The approach developed here, with a mind toward adaptation into high throughput screening, provides a rational balance of speed, requiring less than 30 minutes/plate, and statistical rigour, providing images of thousands of nodules in this time, as required for the high-content analysis developed in this study. These parameters can be further optimized for specific scenarios. For example, we obtain the same number of images in a 96 well plate as for a 24 well plate by acquiring only a single field from each well, rather than 4 stitched fields. This quadruples the number conditions assayed in a single run, at the expense of the number of nodules per condition, and therefore the ability to obtain statistical data sets for size-dependent response, Dfrac and other segmentation-dependent numerical readouts.

 

We envision that the system for high-content interrogation of therapeutic response in 3D cell culture could have widespread impact in multiple arenas from basic research to large scale drug development campaigns. As such, the treatment assessment methodology presented here does not require extraordinary optical instrumentation or computational resources, making it widely accessible to any research laboratory with an inverted fluorescence microscope and modestly equipped personal computer. And although we have focused here on cancer models, the methodology is broadly applicable to quantitative evaluation of other tissue models in regenerative medicine and tissue engineering. While this analysis toolbox could have impact in facilitating the implementation of in vitro 3D models in preclinical treatment evaluation in smaller academic laboratories, it could also be adopted as part of the screening pipeline in large pharma settings. With the implementation of appropriate temperature controls to handle basement membranes in current robotic liquid handling systems, our analyses could be used in ultra high-throughput screening. In addition to removing non-efficacious potential candidate drugs earlier in the pipeline, this approach could also yield the additional economic advantage of minimizing the use of costly time-intensive animal models through better estimates of dose range, sequence and schedule for combination regimens.

 

Microscope Uses AI to Find Cancer Cells More Efficiently

Thu, 04/14/2016 – by Shaun Mason

http://www.mdtmag.com/news/2016/04/microscope-uses-ai-find-cancer-cells-more-efficiently

Scientists at the California NanoSystems Institute at UCLA have developed a new technique for identifying cancer cells in blood samples faster and more accurately than the current standard methods.

In one common approach to testing for cancer, doctors add biochemicals to blood samples. Those biochemicals attach biological “labels” to the cancer cells, and those labels enable instruments to detect and identify them. However, the biochemicals can damage the cells and render the samples unusable for future analyses.

There are other current techniques that don’t use labeling but can be inaccurate because they identify cancer cells based only on one physical characteristic.

The new technique images cells without destroying them and can identify 16 physical characteristics — including size, granularity and biomass — instead of just one. It combines two components that were invented at UCLA: a photonic time stretch microscope, which is capable of quickly imaging cells in blood samples, and a deep learning computer program that identifies cancer cells with over 95 percent accuracy.

Deep learning is a form of artificial intelligence that uses complex algorithms to extract meaning from data with the goal of achieving accurate decision making.

The study, which was published in the journal Nature Scientific Reports, was led by Barham Jalali, professor and Northrop-Grumman Optoelectronics Chair in electrical engineering; Claire Lifan Chen, a UCLA doctoral student; and Ata Mahjoubfar, a UCLA postdoctoral fellow.

Photonic time stretch was invented by Jalali, and he holds a patent for the technology. The new microscope is just one of many possible applications; it works by taking pictures of flowing blood cells using laser bursts in the way that a camera uses a flash. This process happens so quickly — in nanoseconds, or billionths of a second — that the images would be too weak to be detected and too fast to be digitized by normal instrumentation.

The new microscope overcomes those challenges using specially designed optics that boost the clarity of the images and simultaneously slow them enough to be detected and digitized at a rate of 36 million images per second. It then uses deep learning to distinguish cancer cells from healthy white blood cells.

“Each frame is slowed down in time and optically amplified so it can be digitized,” Mahjoubfar said. “This lets us perform fast cell imaging that the artificial intelligence component can distinguish.”

Normally, taking pictures in such minuscule periods of time would require intense illumination, which could destroy live cells. The UCLA approach also eliminates that problem.

“The photonic time stretch technique allows us to identify rogue cells in a short time with low-level illumination,” Chen said.

The researchers write in the paper that the system could lead to data-driven diagnoses by cells’ physical characteristics, which could allow quicker and earlier diagnoses of cancer, for example, and better understanding of the tumor-specific gene expression in cells, which could facilitate new treatments for disease.   …..  see also http://www.nature.com/article-assets/npg/srep/2016/160315/srep21471/images_hires/m685/srep21471-f1.jpg

Chen, C. L. et al. Deep Learning in Label-free Cell Classification.    Sci. Rep. 6, 21471;   http://dx.doi.org:/10.1038/srep21471

 

 

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Invivoscribe, Thermo Fisher Ink Cancer Dx Development Deal

Reporter: Stephen J. Williams, PhD

 

NEW YORK (GenomeWeb) – Invivoscribe Technologies announced today that it has formed a strategic partnership with Thermo Fisher Scientific to develop multiple next-generation sequencing-based in vitro cancer diagnostics.

Under the deal, Invivoscribe will develop and commercialize immune-oncology molecular diagnostics that run on Thermo’s Ion PGM Dx system, as well as associated bioinformatics software for applications in liquid biopsies. The tests will be specifically designed for both the diagnosis and minimal residual disease (MRD) monitoring of various hematologic cancers.

Additional terms of the arrangement were not disclosed.

“We are … very excited to provide our optimized NGS tests with comprehensive bioinformatics software so our customers can perform the entire testing and reporting process, including MRD testing, within their laboratories,” Invivoscribe CEO Jeffrey Miller said in a statement.

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Roche/Genentech’s Late-Stage Pipeline beyond Cancer: Ocrelizumab, against primary progressive MS & relapsing/remitting MS – $2.7 billion peak sales forecast

 

Reporter: Aviva Lev-Ari, PhD, RN

 

SOURCE

http://www.fool.com/investing/general/2016/03/19/youll-never-guess-which-pharma-likely-owns-40-of-2.aspx

 

Beyond Cancer

 

1. ocrelizumab, $2.7 billion peak sales forecast


What has the multiple sclerosis market excited about ocrelizumab is its success against primary progressive MS. Until orcrelizumab, no treatment in history has succeeded in a Phase III trial against this extremely debilitating form of MS.

Ocrelizumab is also being positioned for relapsing/remitting MS. Clinical trial data released in October showed that the treatment cut MS relapses by almost half compared with Merck’s competing drug, Rebif.

On a commercial basis, ocrelizumab’s expanded label (to include both forms of MS) should greatly increase its revenue potential. While a conservative estimate of ocrelizumab’s peak sales puts it at $2.7 billion, some see a peak sales potential for ocrelizumab in the neighborhood of $6 billion. That’s certainly a long shot, but not out of the question, since it is based on a MS market that is now worth $19 billion growing at 5% annually, with ocrelizumab eventually reaching a 30% market share.

Roche has stated plans for applying for regulatory approval for ocrelizumab in the first half of 2016. The drug’s accelerated approval status means an expedited review, with the FDA likely to take action on the application within 6 months. While ocrelizumab’s timeline depends on many variables, there is potential for sales to begin by year-end 2016.

 

Cancer Indications

 

2. Atezolizumab: $2.5 billion peak sales projected


Roche’s immuno-oncology drug atezolizumab follows ocrelizumab in blockbuster potential. Drugs such as atezolizumab (atezo) work by turning off cancer’s ability to remain undetected by the immune system, and atezo has put up some impressive data in its clinical trials. For example, in its POPLAR trial against advanced non-small-cell lung cancer, atezo doubled the likelihood of survival in patients taking the drug relative to placebo.

Being first matters, however. The market already has powerful competitors for atezo in Merck’s Keytruda and Bristol-Myers Squibb‘s (NYSE:BMY) Opdivo. On the other hand, both Keytruda and Opdivo are PD-1 treatments, and atezo works through another mechanism, PD-L1.

Genentech researchers believe PD-L1 is a more significant engine in cancer than PD-1. If they are correct, atezo will have a more long-lasting effect on stopping cancer growth, which would make the drug a potential first choice. Roche is driving some 36 studies  toward making a broad case for atezo with the FDA. Encouraging data keeps coming in. But investors should realize that how this drug will perform against competition from Keytruda and Opdivo is still very much an open question.

A more immediate commercial advantage for atezo is that Roche has a powerful in-house diagnostic division providing tools that can tag patients likely to respond to the drug. Many cancer therapies are ineffective with a large percentage of patients, and by specifically identifying those cancer patients who should benefit, Roche can personalize cancer treatment. That’s a big plus with payers, who naturally want to conserve their money for therapies more likely to be effective. As personalized medicine becomes steadily more widespread, full-year sales for Roche’s diagnostic division have grown–increasing 6% in 2015 to $10.7 billion.

Atezo’s breakthrough therapy designation gives it a solid chance of rolling out this year, but some industry watchers are deferring atezo’s projected launch date until 2017. Calculating a launch date is an inexact science, so that’s certainly possible.

3. Venetoclax: $1.4 billion projected for Roche

Roche’s third blockbuster speeding toward FDA approval is AbbVie partnered venetoclax. The drug is targeted to treat a highly virulent form of leukemia (chronic lymphocytic leukemia), specifically in those patients with a mutation that makes the cancer more aggressive and often results in shortened survival. Late-stage trials are also ongoing in non-Hodgkin’s lymphoma, acute myeloid leukemia, and multiple myeloma.

Roche has U.S. marketing rights  to the drug, and FiercePharma estimates Roche’s share of peak sales at $1.4 billion by 2020. The drug, which has already been fast-tracked for approval under the agency’s breakthrough designation last May, scored a priority review from the FDA in January. Roche expects FDA clearance in 2016.

 

SOURCE

http://www.fool.com/investing/general/2016/03/19/youll-never-guess-which-pharma-likely-owns-40-of-2.aspx

 

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

Immune-Oncology Molecules In Development & Articles on Topic in @pharmaceuticalintelligence.com

Curators: Stephen J Williams, PhD and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/01/11/articles-on-immune-oncology-molecules-in-development-pharmaceuticalintelligence-com/

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The late Cambridge Mayor Alfred Vellucci welcomed Life Sciences Labs to Cambridge, MA – June 1976

Reporter: Aviva Lev-Ari, PhD, RN

How Cambridge became the Life Sciences Capital

Worth watching is the video below, which captures the initial Cambridge City Council hearing on recombinant DNA research from June 1976. The first speaker is the late Cambridge mayor Alfred Vellucci.

Vellucci hoped to pass a two-year moratorium on gene splicing in Cambridge. Instead, the council passed a three-month moratorium, and created a board of nine Cambridge citizens — including a nun and a nurse — to explore whether the work should be allowed, and if so, what safeguards would be necessary. A few days after the board was created, the pro and con tables showed up at the Kendall Square marketplace.

At the time, says Phillip Sharp, an MIT professor, Cambridge felt like a manufacturing town that had seen better days. He recalls being surrounded by candy, textile, and leather factories. Sharp hosted the citizens review committee at MIT, explaining what the research scientists there planned to do. “I think we built a relationship,” he says.

By early 1977, the citizens committee had proposed a framework to ensure that any DNA-related experiments were done under fairly stringent safety controls, and Cambridge became the first city in the world to regulate research using genetic material.

 

WATCH VIDEO

How Cambridge became the life sciences capital

Scott Kirsner can be reached at kirsner@pobox.com. Follow him on Twitter@ScottKirsner and on betaboston.com.

SOURCE

How Cambridge became the life sciences capital

http://www.betaboston.com/news/2016/03/17/how-cambridge-became-the-life-sciences-capital/

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Dopamine-β-Hydroxylase Functional Variants

Curator: Larry H. Bernstein, MD, FCAP

 

 

Deep sequencing identifies novel regulatory variants in the distal promoter region of the dopamine-β-hydroxylase gene.

OBJECTIVE:

Dopamine-β-hydroxylase (DBH), an enzyme that converts dopamine into norepinephrine, is a drug target in cardiovascular and neuropsychiatric disorders. We aimed to identify functional variants in this gene by deep sequencing and enzyme phenotyping in an Indian cohort.

MATERIALS AND METHODS:

Targeted resequencing of 12 exons and 10 kb upstream sequences of DBH in healthy volunteers (n=50) was performed using the Ion Personal Genome Machine System. Enzyme quantity and activity in their sera samples were determined by ELISA and ultra performance liquid chromatography, respectively. The association of markers with phenotypes was determined using Matrix eQTL. Global P-values for haplotypes generated using UNPHASED 3.1.5 were graphed using GrASP v.082 beta.

RESULTS:

Of the 49 variants identified, nine were novel (minor allele frequency≥0.01). Though individual markers associated with enzyme quantity did not withstand multiple corrections, a novel distal promoter block driven by rs113249250 (global P=1.5×10) was associated. Of the nine single nucleotide polymorphisms (SNPs) associated with enzyme activity, rs3025369, rs1076151 and rs1611115, all from the upstream region, withstood false discovery rate correction (false discovery rate=0.03, 0.03 and 2.9×10, respectively). Conditioning for rs1611115 identified rs1989787 also to affect activity. Importantly, we report an association of a novel haplotype block distal to rs1076151 driven by rs3025369 (global P=8.9×10) with enzyme activity. This regulatory SNP explained 4.9% of the total 46.1% of variance in DBH activity caused by associated SNPs.

CONCLUSION:

This first study combining deep sequencing and enzyme phenotyping identified yet another regulatory SNP suggesting that regulatory variants may be central in the physiological or metabolic role of this gene of therapeutic and pharmacological relevance.

 

 

Correlation of plasma dopamine beta-hydroxylase activity with polymorphisms in DBH gene: a study on Eastern Indian population.

Plasma dopamine beta-hydroxylase activity (plDbetaH) is tightly regulated by the DBH gene and several genetic polymorphisms have been found to independently exert their influence. In the present investigation, association of four DBH polymorphisms, DBH-STR, rs1611115, rs1108580, and rs2519152 with plDbetaH was examined in blood samples from 100 unrelated individuals belonging to the state of West Bengal, Eastern India. Genotypes obtained after PCR amplification and restriction digestion were used for statistical analyses. plDbetaH was measured using a photometric assay and its correlation with the genetic polymorphisms was analyzed using analysis of variance and linear regression. Moderate linkage disequilibrium (LD) was observed between DBH-STR and rs1611115, while rs1108580 and rs2519152 were in strong LD. ‘T’ allele of rs1611115 showed strong negative correlation with plDbetaH, whereas DBH-STR, rs1108580 and rs2519152 had no major effect. Four haplotypes showed significant influence on plDbetaH. This is the first report on the effect of genetic polymorphisms on plDbetaH from the Indian sub-continent. rs1611115 was the only polymorphism that showed substantial control over plDbetaH. Other polymorphisms which did not show individual effects could possibly be part of larger haplotype blocks that carry the functional polymorphisms controlling plDbetaH.
Polymorphisms and low plasma activity of dopamine-beta-hydroxylase in ADHD children.
Attention-deficit Hyperactivity disorder (ADHD) is a multifactorial disorder clinically characterized by inattentiveness, impulsivity and hyperactivity. The occurrence of this disorder is between 3 and 6% of the children population, with boys predominating over girls at a ratio of 3:1 or more. The research of some candidate genes (DRD4, DAT, DRD5, DBH, 5HTT, HTR1B and SNAP25) brought consistent results confirming the heredity of ADHD syndromes. Dopamine-beta-hydroxylase (DBH) is an enzyme responsible for the conversion of dopamine into noradrenaline. Alteration of the dopamine/noradrenaline levels can result in hyperactivity. The DBH protein is released in response to stimulation. DBH activity, derived largely from sympathetic nerves, can be measured in human plasma. Patients with ADHD showed decreased activities of DBH in serum and urine. Low DBH levels correlate indirectly with the seriousness of the hyperkinetic syndrome in children [19,20]. In the DBH gene, the G444A, G910T, C1603T, C1912T, C-1021T, 5 -ins/del and TaqI polymorphisms occur frequently and may affect the function of gene products or modify gene expression and thus influence the progression of ADHD. This article reviews the DBH itself and polymorphisms in the DBH gene that influence the DBH activity in the serum and the CSF level of DBH. All those are evaluated in connection with ADHD.
Candidate gene studies of attention-deficit/hyperactivity disorder.
A growing body of behavioral and molecular genetics literature has indicated that the development of attention-deficit/hyperactivity disorder (ADHD) may be attributed to both genetic and environmental factors. Family, twin, and adoption studies provide compelling evidence that genes play a strong role in mediating susceptibility to ADHD. Molecular genetic studies suggest that the genetic architecture of ADHD is complex, while the handful of genome-wide scans conducted thus far is not conclusive. In contrast, the many candidate gene studies of ADHD have produced substantial evidence implicating several genes in the etiology of the disorder. For the 8 genes for which the same variant has been studied in 3 or more case-control or family-based studies, 7 show statistically significant evidence of association with ADHD based on pooled odds ratios across studies: the dopamine D4 receptor gene (DRD4), the dopamine D5 receptor gene (DRD5), the dopamine transporter gene (DAT), the dopamine beta-hydroxylase gene (DBH), the serotonin transporter gene (5-HTT), the serotonin receptor 1B gene (HTR1B), and the synaptosomal-associated protein 25 gene (SNAP25). Recent pharmacogenetic studies have correlated treatment nonresponse with particular gene markers, while preclinical studies have increased our understanding of gene expression paradigms and potential analogs for human trials. This literature review discusses the relevance and implications of genetic associations with ADHD for clinical practice and future research
Lack of significant association between -1021C–>T polymorphism in the dopamine beta hydroxylase gene and attention deficit hyperactivity disorder.
Recent trends in medications for attention deficit hyperactivity disorder (ADHD) suggest that norepinephrine (NE) deficiency may contribute to the disease etiology. Dopamine beta hydroxylase (DBH) is the key enzyme which converts dopamine to NE and since DBH gene is considered a major quantitative trait locus for plasma DBH activity, genetic polymorphism may lead to altered NE neurotransmission. Several polymorphisms including a 5′ flanking -1021C–>T polymorphism, was reported to be associated with changed DBH activity and an association between -1021C–>T polymorphism with ADHD was observed in Han Chinese children. We have carried out family-based studies with three polymorphisms in the DBH gene, -1021C–>T polymorphism, exon 2*444g/a and intron 5 TaqI RFLP, to explore their association with Indian ADHD cases. Allele and genotype frequency of these polymorphisms in ADHD cases were compared with that of their parents and a control group. Haplotypes obtained were analyzed for linkage disequilibrium (LD). Haplotype-based haplotype relative risk analysis and transmission disequilibrium test showed lack of significant association between transmission of the polymorphisms and ADHD. A haplotype comprising of allele 1 of all polymorphisms showed a slight positive trend towards transmission from parents to ADHD probands. Strong LD was observed between *444g/a and TaqI RFLP in all the groups. However, low D’ values and corresponding log of odds scores in the control group as compared to the ADHD families indicated that, the incidence of the two polymorphisms being transmitted together could be higher in ADHD families.
Association of the dopamine beta hydroxylase gene with attention deficit hyperactivity disorder: genetic analysis of the Milwaukee longitudinal study.
Attention deficit hyperactivity disorder (ADHD) is a highly heritable and common disorder that partly reflects disturbed dopaminergic function in the brain. Recent genetic studies have shown that candidate genes involved in dopamine signaling and metabolism contribute to ADHD susceptibility. We have initiated genetic studies in a unique cohort of 158 ADHD and 81 control adult subjects who have been followed longitudinally since childhood in the Milwaukee study of ADHD. From this cohort, genetic analysis was performed in 105 Caucasian subjects with ADHD and 68 age and ethnicity-matched controls for the DRD4 exon 3 VNTR, the SLC6A3 (DAT1) 3′ UTR VNTR, dopamine beta hydroxylase (DBH) TaqI A polymorphism, and the DBH GT microsatellite repeat polymorphism that has been quantitatively associated with serum levels of DBH activity, but not previously studied in ADHD. Results indicate a significant association between the DBH TaqI A1 allele and ADHD (P = 0.018) with a relative risk of 1.33. The DBH GT repeat 4 allele, which is associated with high serum levels of DBH, occurred more frequently in the ADHD group than controls, but the difference did not reach statistical significance. Associations were not found with the SLC6A3 10 repeat or DRD4 7 repeat alleles. These results indicate that the DBH TaqI A allele, or another polymorphism in linkage disequilibrium with this allele, may confer increased susceptibility towards ADHD.
Polymorphisms of the dopamine transporter gene: influence on response to methylphenidate in attention deficit-hyperactivity disorder.
Attention deficit-hyperactivity disorder (ADHD) is a very common and heterogeneous childhood-onset psychiatric disorder, affecting between 3% and 5% of school age children worldwide. Although the neurobiology of ADHD is not completely understood, imbalances in both dopaminergic and noradrenergic systems have been implicated in the origin and persistence of core symptoms, which include inattention, hyperactivity, and impulsivity. The role of a genetic component in its etiology is strongly supported by genetic studies, and several investigations have suggested that the dopamine transporter gene (DAT1; SLC6A3 locus) may be a small-effect susceptibility gene for ADHD. Stimulant medication has a well-documented efficacy in reducing ADHD symptoms. Methylphenidate, the most prescribed stimulant, seems to act mainly by inhibiting the dopamine transporter protein and dopamine reuptake. In fact, its effect is probably related to an increase in extracellular levels of dopamine, especially in brain regions enriched in this protein (i.e. striatum). It is also important to note that dopamine transporter densities seem to be particularly elevated in the brain of ADHD patients, decreasing after treatment with methylphenidate. Altogether, these observations suggest that the dopamine transporter does play a major role in ADHD. Among the several polymorphisms already described in the SLC6A3 locus, a 40 bp variable number of tandem repeats (VNTR) polymorphism has been extensively investigated in association studies with ADHD. Although there are some negative results, the findings from these reports indicate the allele with ten copies of the 40 bp sequence (10-repeat allele) as the risk allele for ADHD. Some investigations have suggested that this polymorphism can be implicated in dopamine transporter gene expression in vitro and dopamine transporter density in vivo, even though it is located in a non-coding region of the SLC6A3 locus. Despite all these data, few studies have addressed the relationship between genetic markers (specifically the VNTR) at the SLC6A3 locus and response to methylphenidate in ADHD patients. A significant effect of the 40 bp VNTR on response to methylphenidate has been detected in most of these reports. However, the findings are inconsistent regarding both the allele (or genotype) involved and the direction of this influence (better or worse response). Thus, further investigations are required to determine if genetic variation due to the VNTR in the dopamine transporter gene is able to predict different levels of clinical response and palatability to methylphenidate in patients with ADHD, and how this information would be useful in clinical practice.
Pharmacogenomics in psychiatry: the relevance of receptor and transporter polymorphisms.
The treatment of severe mental illness, and of psychiatric disorders in general, is limited in its efficacy and tolerability. There appear to be substantial interindividual differences in response to psychiatric drug treatments that are generally far greater than the differences between individual drugs; likewise, the occurrence of adverse effects also varies profoundly between individuals. These differences are thought to reflect, at least in part, genetic variability. The action of psychiatric drugs primarily involves effects on synaptic neurotransmission; the genes for neurotransmitter receptors and transporters have provided strong candidates in pharmacogenetic research in psychiatry. This paper reviews some aspects of the pharmacogenetics of neurotransmitter receptors and transporters in the treatment of psychiatric disorders. A focus on serotonin, catecholamines and amino acid transmitter systems reflects the direction of research efforts, while relevant results from some genome-wide association studies are also presented. There are many inconsistencies, particularly between candidate gene and genome-wide association studies. However, some consistency is seen in candidate gene studies supporting established pharmacological mechanisms of antipsychotic and antidepressant response with associations of functional genetic polymorphisms in, respectively, the dopamine D2 receptor and serotonin transporter and receptors. More recently identified effects of genes related to amino acid neurotransmission on the outcome of treatment of schizophrenia, bipolar illness or depression reflect the growing understanding of the roles of glutamate and γ-aminobutyric acid dysfunction in severe mental illness. A complete understanding of psychiatric pharmacogenomics will also need to take into account epigenetic factors, such as DNA methylation, that influence individual responses to drugs.
Pharmacogenetics of psychotropic drug response.

OBJECTIVE:

Molecular genetic approaches provide a novel method of dissecting the heterogeneity of psychotropic drug response. These pharmacogenetic strategies offer the prospect of identifying biological predictors of psychotropic drug response and could provide the means of determining the molecular substrates of drug efficacy and drug-induced adverse events.

METHOD:

The authors discuss methods issues in executing pharmacogenetic studies, review the first generation of pharmacogenetic studies of psychotropic drug response, and consider future directions for this rapidly evolving field.

RESULTS:

Pharmacogenetics has been most commonly used in studies of antipsychotic drug efficacy, antidepressant drug response, and drug-induced adverse effects. Data from antipsychotic drug studies indicate that polymorphisms within the serotonin 2A and dopamine receptor 2 genes may influence drug efficacy in schizophrenia. Moreover, a growing body of data suggests a relationship between the serotonin transporter gene and clinical effects of the selective serotonin reuptake inhibitors used to treat depression. A significant relationship between genetic variation in the cytochrome P450 system and drug-induced adverse effects may exist for certain medications. Finally, a number of independent studies point to a significant effect of a dopamine D(3) receptor polymorphism on susceptibility to tardive dyskinesia.

CONCLUSIONS:

Initial research into the pharmacogenetics of psychotropic drug response suggests that specific genes may influence phenotypes associated with psychotropic drug administration. These results remain preliminary and will require further replication and validation. New developments in molecular biology, human genomic information, statistical methods, and bioinformatics are ongoing and could pave the way for the next generation of pharmacogenetic studies in psychiatry.

OBJECTIVE: Molecular genetic approaches provide a novel method of dissecting the heterogeneity of psychotropic drug response. These pharmacogenetic strategies offer the prospect of identifying biological predictors of psychotropic drug response and could provide the means of determining the molecular substrates of drug efficacy and drug-induced adverse events. METHOD: The authors discuss methods issues in executing pharmacogenetic studies, review the first generation of pharmacogenetic studies of psychotropic drug response, and consider future directions for this rapidly evolving field. RESULTS: Pharmacogenetics has been most commonly used in studies of antipsychotic drug efficacy, antidepressant drug response, and drug-induced adverse effects. Data from antipsychotic drug studies indicate that polymorphisms within the serotonin 2A and dopamine receptor 2 genes may influence drug efficacy in schizophrenia. Moreover, a growing body of data suggests a relationship between the serotonin transporter gene and clinical effects of the selective serotonin reuptake inhibitors used to treat depression. A significant relationship between genetic variation in the cytochrome P450 system and drug-induced adverse effects may exist for certain medications. Finally, a number of independent studies point to a significant effect of a dopamine D3 receptor polymorphism on susceptibility to tardive dyskinesia. CONCLUSIONS: Initial research into the pharmacogenetics of psychotropic drug response suggests that specific genes may influence phenotypes associated with psychotropic drug administration. These results remain preliminary and will require further replication and validation. New developments in molecular biology, human genomic information, statistical methods, and bioinformatics are ongoing and could pave the way for the next generation of pharmacogenetic studies in psychiatry.

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A Reconstructed View of Personalized Medicine

Author: Larry H. Bernstein, MD, FCAP

 

There has always been Personalized Medicine if you consider the time a physician spends with a patient, which has dwindled. But the current recognition of personalized medicine refers to breakthrough advances in technological innovation in diagnostics and treatment that differentiates subclasses within diagnoses that are amenable to relapse eluding therapies.  There are just a few highlights to consider:

  1. We live in a world with other living beings that are adapting to a changing environmental stresses.
  2. Nutritional resources that have been available and made plentiful over generations are not abundant in some climates.
  3. Despite the huge impact that genomics has had on biological progress over the last century, there is a huge contribution not to be overlooked in epigenetics, metabolomics, and pathways analysis.

A Reconstructed View of Personalized Medicine

There has been much interest in ‘junk DNA’, non-coding areas of our DNA are far from being without function. DNA has two basic categories of nitrogenous bases: the purines (adenine [A] and guanine [G]), and the pyrimidines (cytosine [C], thymine [T], and  no uracil [U]),  while RNA contains only A, G, C, and U (no T).  The Watson-Crick proposal set the path of molecular biology for decades into the 21st century, culminating in the Human Genome Project.

There is no uncertainty about the importance of “Junk DNA”.  It is both an evolutionary remnant, and it has a role in cell regulation.  Further, the role of histones in their relationship the oligonucleotide sequences is not understood.  We now have a large output of research on noncoding RNA, including siRNA, miRNA, and others with roles other than transcription. This requires major revision of our model of cell regulatory processes.  The classic model is solely transcriptional.

  • DNA-> RNA-> Amino Acid in a protein.

Redrawn we have

  • DNA-> RNA-> DNA and
  • DNA->RNA-> protein-> DNA.

Neverthess, there were unrelated discoveries that took on huge importance.  For example, since the 1920s, the work of Warburg and Meyerhoff, followed by that of Krebs, Kaplan, Chance, and others built a solid foundation in the knowledge of enzymes, coenzymes, adenine and pyridine nucleotides, and metabolic pathways, not to mention the importance of Fe3+, Cu2+, Zn2+, and other metal cofactors.  Of huge importance was the work of Jacob, Monod and Changeux, and the effects of cooperativity in allosteric systems and of repulsion in tertiary structure of proteins related to hydrophobic and hydrophilic interactions, which involves the effect of one ligand on the binding or catalysis of another,  demonstrated by the end-product inhibition of the enzyme, L-threonine deaminase (Changeux 1961), L-isoleucine, which differs sterically from the reactant, L-threonine whereby the former could inhibit the enzyme without competing with the latter. The current view based on a variety of measurements (e.g., NMR, FRET, and single molecule studies) is a ‘‘dynamic’’ proposal by Cooper and Dryden (1984) that the distribution around the average structure changes in allostery affects the subsequent (binding) affinity at a distant site.

What else do we have to consider?  The measurement of free radicals has increased awareness of radical-induced impairment of the oxidative/antioxidative balance, essential for an understanding of disease progression.  Metal-mediated formation of free radicals causes various modifications to DNA bases, enhanced lipid peroxidation, and altered calcium and sulfhydryl homeostasis. Lipid peroxides, formed by the attack of radicals on polyunsaturated fatty acid residues of phospholipids, can further react with redox metals finally producing mutagenic and carcinogenic malondialdehyde, 4-hydroxynonenal and other exocyclic DNA adducts (etheno and/or propano adducts). The unifying factor in determining toxicity and carcinogenicity for all these metals is the generation of reactive oxygen and nitrogen species. Various studies have confirmed that metals activate signaling pathways and the carcinogenic effect of metals has been related to activation of mainly redox sensitive transcription factors, involving NF-kappaB, AP-1 and p53.

I have provided mechanisms explanatory for regulation of the cell that go beyond the classic model of metabolic pathways associated with the cytoplasm, mitochondria, endoplasmic reticulum, and lysosome, such as, the cell death pathways, expressed in apoptosis and repair.  Nevertheless, there is still a missing part of this discussion that considers the time and space interactions of the cell, cellular cytoskeleton and extracellular and intracellular substrate interactions in the immediate environment.

There is heterogeneity among cancer cells of expected identical type, which would be consistent with differences in phenotypic expression, aligned with epigenetics.  There is also heterogeneity in the immediate interstices between cancer cells.  Integration with genome-wide profiling data identified losses of specific genes on 4p14 and 5q13 that were enriched in grade 3 tumors with high microenvironmental diversity that also substratified patients into poor prognostic groups. In the case of breast cancer, there is interaction with estrogen , and we refer to an androgen-unresponsive prostate cancer.

Finally,  the interaction between enzyme and substrates may be conditionally unidirectional in defining the activity within the cell.  The activity of the cell is dynamically interacting and at high rates of activity.  In a study of the pyruvate kinase (PK) reaction the catalytic activity of the PK reaction was reversed to the thermodynamically unfavorable direction in a muscle preparation by a specific inhibitor. Experiments found that in there were differences in the active form of pyruvate kinase that were clearly related to the environmental condition of the assay – glycolitic or glyconeogenic. The conformational changes indicated by differential regulatory response were used to present a dynamic conformational model functioning at the active site of the enzyme. In the model, the interaction of the enzyme active site with its substrates is described concluding that induced increase in the vibrational energy levels of the active site decreases the energetic barrier for substrate induced changes at the site. Another example is the inhibition of H4 lactate dehydrogenase, but not the M4, by high concentrations of pyruvate. An investigation of the inhibition revealed that a covalent bond was formed between the nicotinamide ring of the NAD+ and the enol form of pyruvate.  The isoenzymes of isocitrate dehydrogenase, IDH1 and IDH2 mutations occur in gliomas and in acute myeloid leukemias with normal karyotype. IDH1 and IDH2 mutations are remarkably specific to codons that encode conserved functionally important arginines in the active site of each enzyme. In this case, there is steric hindrance by Asp279 where the isocitrate substrate normally forms hydrogen bonds with Ser94.

Personalized medicine has been largely viewed from a lens of genomics.  But genomics is only the reading frame.  The living activities of cell processes are dynamic and occur at rapid rates.  We have to keep in mind that personalized in reference to genotype is not complete without reconciliation of phenotype, which is the reference to expressed differences in outcomes.

 

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A Perspective on Personalized Medicine

Curator: Larry H. Bernstein, MD, FCAP

 

 

A book has recently been reviewed by Laura Fisher (Feb 19 2016) titled “Junk DNA: a journey through the dark matter of the genome” (Nessa Carey  Icon Books 2015 | 352pp  ISBN 9781848319158).  http://www.rsc.org/chemistryworld/2016/02/junk-dna-genome-nessa-carey-book-review  It is important in its focus on, ‘junk DNA’, a term coined in the 1960s that refers to regions of our DNA that don’t code for proteins.  It is now known that a large portion of the genome is noncoding. These non-coding areas of our DNA are far from being without function. Whether regulating gene expression and transcription, or providing protein attachment sites, this once-dismissed part of the genome is vital for all life, and this is the focus of Junk DNA.  However, in 1869 Friedrich Miescher discovered a new substance (Dahm, 2008) from the cell nuclei that had chemical properties unlike any protein, including a much higher phosphorous content and resistance to proteolysis (protein digestion).  He wrote, “It seems probable to me that a whole family of such slightly varying phosphorous-containing substances will appear, as a group of nucleins, equivalent to proteins” (Wolf, 2003). In 1971, Chargaff  noted that Miescher’s discovery of nucleic acids was unique among the discoveries of the four major cellular components (i.e., proteins, lipids, polysaccharides, and nucleic acids) in that it could be “dated precisely… [to] one man one place, one date.”  We now know that there are two basic categories of nitrogenous bases: the purines (adenine [A] and guanine [G]), each with two fused rings, and the pyrimidines (cytosine [C], thymine [T], and uracil [U]), each with a single ring. Furthermore, it is now widely accepted that RNA contains only A, G, C, and U (no T), whereas DNA contains only A, G, C, and T (no U).  Keeping this in mind, the Watson-Crick proposal, as important as it was, was a discovery out of historical proportion, and it set the path of molecular biology for the remainder of the 20th century. A consequence of this seminal event was that the direction of biochemistry and molecular biology became set for several generations into the 21st century, culminating in the Human Genome Project.

As important as this discovery and others related that followed, there were a number of unrelated discoveries that took on huge importance, immediately recognized, but not so soon integrated with the evolving body of knowledge.  For example, since the 1920s, the work of Warburg and Meyerhoff, followed by that of Krebs, Kaplan, Chance, and others built a solid foundation in the knowledge of enzymes, coenzymes, adenine and pyridine nucleotides, and metabolic pathways, not to mention the importance of Fe3+, Cu2+, Zn2+, and other metal cofactors.  There was also a relevance of the work of Jacob, Monod and Changeux, and the effects of cooperativity in allosteric systems and of repulsion in tertiary structure of proteins related to hydrophobic and hydrophilic interactions. This involves the effect of one ligand on the binding or catalysis of another with no direct interaction between the two ligands. This was demonstrated by the end-product inhibition of the enzyme, L-threonine deaminase (Changeux 1961), L-isoleucine, which differs sterically from the reactant, L-threonine whereby binding at a different, nonoverlapping (regulatory) site, the former could inhibit the enzyme without competing with the latter. Pauling (Pauling 1935) had earlier proposed a model for intramolecular control in hemoglobin to explain the positive cooperativity observed in the binding of oxygen molecules. But  Monod, Wyman, and Changeux  substantially updated the view of allostery in 1965 with their landmark paper.  Present day applications of computational methods to biomolecular systems, combined with structural, thermodynamic, and kinetic studies, make possible an approach to that question, so as to provide a deeper understanding of the requirements for allostery. The current view is that a variety of measurements (e.g., NMR, FRET, and single molecule studies) are providing additional data beyond that available previously from structural, thermodynamic, and kinetic results. These should serve to continue to improve our understanding of the molecular mechanism of allostery, particularly when supplemented by simulations and theoretical analyses. A ‘‘dynamic’’ proposal by Cooper and Dryden (1984) is that the distribution around the average structure changes in allostery; which in turn, affects the subsequent (binding) affinity at a distant site. Such a model focuses on the vibrational contribution to the entropy as the origin of cooperativity, as discussed for the CAPN dimer.  Why is this important?  It is because it brings a different focus into the conception of how living cells engage with their neighbors and external environment.  Moreover, this is not all that has to be considered.

What else do we have to consider?  Oxidative stress is essentially an imbalance between the production of free radicals and the ability of the body to counteract or detoxify their harmful effects through neutralization by antioxidants. The measurement of free radicals has increased awareness of radical-induced impairment of the oxidative/antioxidative balance, essential for an understanding of disease progression.  Metal-mediated formation of free radicals causes various modifications to DNA bases, enhanced lipid peroxidation, and altered calcium and sulfhydryl homeostasis. Lipid peroxides, formed by the attack of radicals on polyunsaturated fatty acid residues of phospholipids, can further react with redox metals finally producing mutagenic and carcinogenic malondialdehyde, 4-hydroxynonenal and other exocyclic DNA adducts (etheno and/or propano adducts). The unifying factor in determining toxicity and carcinogenicity for all these metals is the generation of reactive oxygen and nitrogen species. Common mechanisms involving the Fenton reaction, generation of the superoxide radical and the hydroxyl radical appear to be involved for iron, copper, chromium, vanadium and cobalt primarily associated with mitochondria, microsomes and peroxisomes. Various studies have confirmed that metals activate signaling pathways and the carcinogenic effect of metals has been related to activation of mainly redox sensitive transcription factors, involving NF-kappaB, AP-1 and p53.

In addition to what I have identified, there is substantial work in the last decade to indicate a more complex model of cellular regulatory processes.  On the one hand, there is no uncertainty about the importance of “Junk DNA”.  Indeed, not only is “Junk DNA” not junk, but it has either a presence that is an evolutionary remnant, or it has a role in cell regulation, much of which has yet to be understood.  Moreover, the relationship between the oligonucleotide sequences to their histones are largely unknown.  Beyond the DNA sequences, the language of the gene, we now have a large output of research on noncoding RNA.  We now have siRNA, miRNA, and others with roles other than transcription. This is a very active field of investigation that requires major revision of our model of cell regulatory processes.  The classic model is solely transcriptional.  DNA-> RNA-> Amino Acid in a protein.  This would now have to be redrawn because DNA-> RNA-> DNA and DNA->RNA-> protein-> DNA.

I have provided a series of four mechanisms explanatory for transcription and for regulation of the cell. This is not adequate for a more full comprehension because there is a layer beyond the classic model of metabolic pathways associated with the cytoplasm, mitochondria, endoplasmic reticulum, and lysosome, there are critical paths beyond oxidative phosphorylation and glycolysis, such as the cell death pathways, expressed in a homeostasis between apoptosis and repair.  Nevertheless, there is still a missing part of this discussion. The missing piece gets at the time and space interactions of the cell, cellular cytoskeleton and extracellular and intracellular substrate interactions in the immediate environment.  This can’t be simply accounted for by genetics or epigenetics. There have been papers that call attention to heterogeneity among cancer cells of expected identical type, which would be consistent with differences in phenotypic expression, aligned with epigenetics.  There is now the recent publication of the finding that there is heterogeneity in the immediate interstices between cancer cells, which may seem surprising, but it should not be.  This refers to the complexity of the cells arranged as tissues and to their immediate environment, which I shall elaborate on. Integration with genome-wide profiling data identified losses of specific genes on 4p14 and 5q13 that were enriched in grade 3 tumors with high microenvironmental diversity that also substratified patients into poor prognostic groups. I did introduce the word gene into this reference, and we are well aware of mutations that occur in cancer progression.  In the case of breast cancer, mention is not made of interaction with a hormone, as we refer to in androgen-unresponsive prostate cancer.  This is particularly relevant, but incomplete.

The fifth item for discussion is the interaction between enzyme and substrates that may be conditionally unidirectional in defining the activity within the cell.  When we speak of the genome, we are dealing with a code defined by an oligonucleotide sequence that has an element of stability, but that can conditionally be altered by a process termed mutagenesis.  The altered code can be expected to have a negative, positive, or no effect, depending. In any case, there is a substantial stability inherent in the code that is essential to all living creatures.  The activity of the cell is dynamically interacting and at high rates of activity.  There are many examples of this.  The first example is in a study of energy for reverse pyruvate kinase (PK) reaction.  This catalytic activity of the PK reaction was reversed to the thermodynamically unfavorable direction in a muscle preparation by a specific inhibitor. Using the same crude supernatant for the two opposite activities of this enzyme some of the results found in the regulatory assays indicated differences in the active form of pyruvate kinase that were clearly related to the environmental condition – glycolitic or glyconeogenetic – of the assay. The conformational changes indicated by differential regulatory response found in the conditions studied, together with the role of similar factors, for instance, substrates and pH, in the structural states proposed by others, were used together to present a dynamic conformational model functioning at the active site of the enzyme. In the model, the interaction of the enzyme active site with its substrates is described according to its vibrational, translational and rotational components and the activating ions – induced increase in the vibrational energy levels of the active site decreases the energetic barrier for substrate induced changes at the site.

Another example is the pyridine nucleotide-linked dehydrogenases.   The lactate dehydrogenase (LD) reaction is ordered so that NADH binds to the enzyme before pyruvate can bind. The H-type isoenzyme, but not the M-type, is characterized by substrate inhibition at high pyruvate concentrations. The inhibition of the H4 lactate dehydrogenase, but not the M4, by high concentrations of pyruvate is caused by the formation of an abortive complex consisting of the enzyme, pyruvate, and NADH. An investigation of the structural properties of the ternary complex revealed that the complex possesses an absorption maximum at 335 nm and that a covalent bond was formed between the nicotinamide ring of the NAD+ and the pyruvate moiety. The same study demonstrated that the enol form of pyruvate is responsible for the complex formation.  It was suggested that abortive complex formation is a significant metabolic control mechanism, and the different behavior of the H and M forms has been rationalized in terms of different functional roles for the two isoenzymes.  However, similar experiments carried out with the mitochondrial malate dehydrogenase suggested a similar inhibition, but in this case only the mitochondrial malate dehydrogenase is sensitive to inhibition by high concentrations of oxaloacetate. Further studies showed the inhibition is promoted by an abortive binary complex formed by the enzymes and the enol form of oxalacetate. Neither the oxidized coenzyme nor the reduced coenzyme appears to be involved in the formation of this complex. These results suggest that the mechanism of substrate inhibition that occurs with the pig heart malate dehydrogenases is different from that observed with the lactate dehydrogenases.

It was established years later that there is an isoenzyme of isocitrate dehydrogenase that is characteristic for cancer cells. IDH1 and IDH2 mutations occur frequently in some types of World Health Organization grades 2–4 gliomas and in acute myeloid leukemias with normal karyotype. IDH1 and IDH2 mutations are remarkably specific to codons that encode conserved functionally important arginines in the active site of each enzyme. To date, all IDH1 mutations have been identified at the Arg132 codon. Mutations in IDH2 have been identified at the Arg140 codon, as well as at Arg172, which is aligned with IDH1 Arg132. IDH1 and IDH2 mutations are heterozygous in cancer, and they catalyze the production of α-2-hydroxyglutarate. The study found human IDH1 transitions between an inactive open, an inactive semi-open, and a catalytically active closed conformation. In the inactive open conformation, Asp279 occupies the position where the isocitrate substrate normally forms hydrogen bonds with Ser94. This steric hindrance by Asp279 to isocitrate binding is relieved in the active closed conformation.

Finally, what does this have to do with personalized medicine? Personalized medicine has been largely view from a lens of genomics.  But genomics is only the reading frame, even taking into consideration the mutations that are found in transition.  The living activities of cell processes are dynamic and occur at rapid rates.  When we refer to homeostasis and to neoplasia, we have to keep in mind that personalized in reference to genotype is not complete without reconciliation of phenotype, which is the reference to expressed differences in outcomes.

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