News Column

Researchers Submit Patent Application, "Method for Integrating Large Scale Biological Data with Imaging", for Approval

July 28, 2014



By a News Reporter-Staff News Editor at Cancer Gene Therapy Week -- From Washington, D.C., NewsRx journalists report that a patent application by the inventor Kuo, Michael D. (San Diego, CA), filed on December 6, 2013, was made available online on July 17, 2014 (see also Algeomics, Llc).

The patent's assignee is Algeomics, Llc.

News editors obtained the following quote from the background information supplied by the inventors: "Biomedical imaging is a powerful tool that can provide systems-wide, real time in vivo contextual insights into biology. From the time of the first X-ray, in vivo imaging has provided a vital function for medical research and diagnosis, by permitting the clinician to assess, in real time and space, what is happening within the patient's body. In addition to nuclear medicine and MRI, other imaging methods including positron emission tomography (PET), computerized tomography (CT), ultrasonography (US), optical imaging, infrared imaging, in vivo microscopy and x-ray radiography have also been used for obtaining morphologic, metabolic and functional information of living tissues in vivo in a spatially and temporally resolved manner.

"For example, magnetic resonance imaging (MRI) is an imaging technique used primarily in medical settings to produce high quality images of the inside of the body. MRI is based on the absorption and emission of energy in the radio frequency range of the electromagnetic spectrum. Although there is a limitation on imaging objects smaller than the wavelength of the energy being used to image, MRI gets around this limitation by producing images based on spatial variations in the phase and frequency of the radio frequency energy being absorbed and emitted by the imaged object.

"Contrast enhanced MRI is a powerful tool for the diagnosis of a variety of malignancies. MRI has both high spatial and temporal resolution, with current imaging systems capable of visualizing changes in tissue contrast with micron spatial resolution and millisecond temporal resolution. It has been demonstrated that malignant tumors tend to have faster and higher levels of enhancement when compared to normal surrounding tissues. Furthermore, the kinetics of contrast enhancement on MRI has been correlated to tumor grades and aggressiveness in different tumors. The precise mechanism and origin of contrast enhancement in tumors therefore seems to be related to the complex biological processes associated with tissue perfusion and vascular permeability such as neovascularization and tumor angiogenesis. This may account for the correlation between tumor grade and aggressiveness and contrast enhancement on MRI.

"In the field of nuclear medicine, pathological conditions are localized by imaging the internal distribution of administered radioactively labeled tracer compounds that accumulate specifically at the pathological site. A variety of radionuclides are known to be useful for radioimaging, including .sup.67Ga, .sup.99mTc, .sup.111In .sup.123I, .sup.125I, .sup.169Yb and .sup.186Re. In PET, positron emitting isotopes are conjugated to tracer compounds that also accumulate in pathologic tissues.

"Specificity of accumulation may be provided by conjugating the radioactive tracer to a binding moiety that binds to the cells of interest. Many examples of such binding moieties have been used experimentally and clinically. For example, anticancer antibodies labeled with different radionuclides have been studied in human tumor xenografts and in clinical trials. Molecular targets for binding moieties include a variety of tumor-associated antigens. For example, in breast cancer, these molecular targets have included carcinoembryonic antigen (CEA) and the polymorphic epithelial mucin antigen, MUC1, and more recently the growth factor receptors, EGF-R and HER-2/neu. Imaging and image-guided therapeutic agents that target the alpha-v-beta-3 integrin have utilized antibodies conjugated to a liposome surface. Such agents can show changes in spatial and temporal distribution of the receptor using imaging.

"Alternatively, radiolabelled peptides have been used for imaging a variety of tumors, infection/inflammation and thrombus. A number of .sup.99mTc-labelled bioactive peptides and peptidomimetics have proven to be useful diagnostic imaging agents. Due to their small size, these molecules exhibit favorable pharmacokinetic characteristics, such as rapid uptake by target tissue and rapid blood clearance, which potentially allows images to be acquired earlier following the administration of .sup.99mTc-labelled radiopharmaceuticals.

"Traditionally, imaging has been used as a noninvasive surrogate for histopathologic assessment of disease and response to treatment. Indeed, the vast majority of advances in biomedical imaging have sought to improve imaging spatial resolution so that imaging can better approach the capabilities of microscopy and histopathology. However, as genomics has demonstrated in recent years, histopathology does not capture much of the underlying molecular diversity inherent in disease processes. It is also clear that the multi-dimensional information provided by clinical imaging is currently underutilized. Presently, the biological detail that imaging can provide is substantially limited because among other things, it relies on the inherent limitations of histopathology, which is the current diagnostic gold standard for discrimination of and characterization of normal and diseased tissue.

"Histopathology evaluates the microscopic features of a small section of a tissue (which it then assumes to be representative of the entire tissue) including its composite cells and their surrounding environment and then tries to classify the predominant cell of origin, determine if they are normal or diseased and then subclassify the diseased tissue based on various morphologic features seen by microscopy. However, it is increasingly clear that this type of analysis fails to capture the underlying molecular heterogeneity and diversity that contribute to these disease processes which is evident in histopathology's inability to capture heterogeneous biological processes or predict disease prognosis or treatment outcome with any high level of reliability. Further, pathology relies on tissue for diagnosis and thus is an invasive procedure placing the patient at potential risk any time a histopathologic diagnosis is attempted. But even more, histopathologic analyses are ex vivo representative portraits where the entire disease is assumed to be captured by the snapshot provided by a small representative tissue sampling. Conversely, imaging is a noninvasive tool that can capture in vivo high throughput volumetric data with excellent spatial and temporal resolution. Because it is noninvasive it is inherently safer. Further, imaging can capture real-time, multi-dimensional information about a disease process such as morphologic, physiologic, functional, metabolic, compositional and structural information of an entire system all within the native context of the disease process and against the context of adjacent normal tissues and systems, thus providing global, in vivo and contextual information.

"DNA microarrays are powerful tools to survey the expression levels of thousands of genes simultaneously. By identifying differential changes in the expression level of many genes simultaneously, thematic expression patterns can emerge that are canonical of underlying biological processes and provide insights into the transcriptional state of a cell. These high throughput biological approaches have been broadly applied to the study of biology including disease and development and have uncovered significant molecular and biologic heterogeneity within a large number of biological systems, processes, states and conditions. For example, in the realm of cancer, these data have permitted delineation of genetic programs and molecular markers associated with tumor biology, treatment response, and prognosis for a large variety of human cancers on a tumor-by-tumor basis.

"Further, the recent explosion of information in high throughput biology as exemplified in the fields of genomics, and proteomics has also provided a rich ground for the discovery of molecular targets against which therapeutic and/or diagnostic agents can be directed. Tissues for potential target discovery may include any type of tissue including but not exclusively limited to tumors and other malignant or benign growths, or infected or inflamed tissues. For example, methods have been described for gene expression profiling of tumor cells (see any one of Ono et al. (2000) Cancer Res. 60(18):5007-11; Svaren et al. (2000) J Biol Chem.; or Forozan et al. (2000) Cancer Res. 60(16):4519-25 for examples). Similarly, proteomics has been used to profile the protein expression in tumor samples (see Minowa et al (2000) Electrophoresis 21(9):1782-6; Cole et al. (2000) Electrophoresis 21(9):1772-81; Simpson et al. (2000) Electrophoresis 21(9): 1707-32); etc.

"While powerful, these genomics approaches currently depend on fresh tissue specimens and specialized equipment. Further, genomic and proteomic analysis is performed on tissue samples without consideration of known differences in imaging patterns within the same tissue over space and time. It would be preferable to acquire gene expression information noninvasively. Further, because current genomics and proteomic approaches still require tissue specimens for analysis, although they can provide much greater molecular detail of a tissue specimen, these approaches still suffer from the same inherent limitations of histopathology as previously described above. Additionally, these current methods of tissue analysis for discovery of new imaging and therapeutic agents do not take into consideration the spatial and temporal variation in gene and protein expression within the target tissues. There is a need to resolve the tissue analysis data both spatially and temporally so that the most relevant targets can be identified. Similarly, there is a clinical need to be able to determine the location and/or extent of sites of focal or localized lesions for initial evaluation, and for following the effects of therapy.

"Given this current gap between biomedical imaging, histopathology and new high throughput biological methods, it is evident that new approaches are needed. Clearly, as described above, efforts to make medical imaging a better 'noninvasive microscope' suffer from a number of inherent limitations. Conversely, a large number of scientists have tried to resolve these shortcomings with molecular imaging approaches. However, much of the ongoing work in the burgeoning field of molecular imaging focuses on designing new imaging technologies and targeted biologic probes. It is possible however, that many of the imaging characteristics visible using available biomedical imaging modalities reflect molecular properties of underlying states, systems, processes or diseases that are as of yet unrecognized or uncharacterized. Accordingly, it is of interest to determine whether the regulation of gene or protein expression can be correlated with imaging information, thereby allowing imaging to serve as a powerful non-invasive tool for characterizing biological systems, processes, states, conditions, and diseases.

"Determining if and how patterns of variation in large scale biological approaches such as genome-wide gene or protein expression data are encoded in dynamic imaging features in biomedical imaging would provide a number of important differential insights. This would allow for example, one to predict strictly based on imaging, regulation of gene or protein expression programs that predict underlying tumor biology, outcome, or response to a particular drug or therapy, and even expression of specific individual genes or proteins of interest. These insights could be used alone or in combination with markers identified from other tests to infer new or differential insights or improve diagnostic accuracy. Similarly, information from this approach could also be used to predict genome wide molecular targets for diagnosis or therapy based on imaging. It is possible that this could all be achieved by the integration of biomedical imaging tools with large scale biological data. This would have far reaching applications for understanding, categorizing and treating disease processes on a molecular level and on a patient-by-patient level.

"US 2002/0146371 A1 discloses methods for the discovery, screening and development of novel therapeutic and/or diagnostic targets, based on the use of in vivo imaging of lesions to detect spatial and temporal variations in gene and protein expression. Using the present invention there is provided a broader analysis of gene expression of the index disease as opposed to focusing on particular features than described by the prior art disclosed above. It also allows the analysis without having to obtain a sample from the patient."

As a supplement to the background information on this patent application, NewsRx correspondents also obtained the inventor's summary information for this patent application: "The present invention is a method of extracting biological information about an index disease, state, condition, system or organism from non-invasive imaging by correlating the imaging features with corresponding large scale biological data. This is achieved generally by providing a specimen having the biological state of interest and collecting or providing large scale biological, biochemical or molecular data of said biological state. The specimen is then imaged and then correlating the information contained in the images of said specimen with the generated or provided large scale biological, biochemical or molecular information to determine an imaging trait that will be indicative of the biological state of interest.

DETAILED DESCRIPTION

"The current invention can be used in many different applications including medical diagnostics, therapeutics, drug discovery and drug testing. Also, given that it is now possible to relate imaging to specific large scale biology and vice versa (relate large scale biology with imaging) this would impact, for example, the design of imaging tools and equipment, imaging protocols, the design, implementation, and interpretation of contrast agents (which are themselves drug-like compounds), software tools for both imaging and the large scale biological data as well as for analyzing and integrating the imaging and genomics, all aspects of drug discovery and testing, patient disease screening, diagnosis and characterization of diseases either by imaging alone or in combination with serological tests. Delineation of the invention and how it in general empowers the aforementioned is detailed below.

"The invention comprises correlation of large scale biological data with associated imaging data. Such imaging-large scale biology or imaging-genomic, or radiological-genomic (radiogenomic) analyses yield a detailed and bi-directional association map between the imaging and the associated large-scale biology. The biological data comprises large scale profile data about a particular biological, molecular or biochemical species typically representing a given state. Such data can represent genomic data that might include for example, profiling of gene expression, protein expression or modification, microRNA, DNA copy number, DNA sequence, single nucleotide polymorphisms, or networks, modules or pathways and is characterized by the number of a particular species measured at a given time or state which are greater than one. Examples of large scale data would include but are not limited to gene or protein expression profiling, Serial Analaysis of Gene Expression (SAGE), nuclear magnetic resonance, protein-interaction screens, chromatin immunoprecipitation-Chip, isotope coded affinity tagging, activity based reagents, gel or chromatographic separation, RNAi screens, tissue arrays or mass spectrometry in which a large number of genes, proteins or metabolites are measured in a single experiment or assay.

"The imaging data can embody, but is not limited to imaging obtained with magnetic resonance imaging (MRI), nuclear medicine, positron emission tomography (PET), computerized tomography (CT), ultrasonography (US), optical imaging, infrared imaging, in vivo microscopy and x-ray radiography. Imaging can be coupled with medical devices, drugs or compounds, contrast agents or other agents or stimuli that may be used to elicit additional information from the imaging. Images are obtained using these modalities of the lesion, tissue, specimen, system, organism, or patient and can be static or dynamic images both in time and/or space.

"The imaging is initially matched to the tissue, specimen, system, organism, or patient from which the large scale biological data is obtained. Imaging information is extracted from each image, imaging study or studies or examinations, and can consists of quantitative or qualitative imaging features that may embody but are not limited to differences in morphology, composition, structure, physiology or function of the lesion, a tissue, specimen, system, organism, or patient. Examples of imaging information include but are not limited to imaging features that may be extracted from multi-phase contrast enhanced dynamic CT, functional imaging, magnetic resonance spectroscopy, diffusion tensor imaging, diffusion or perfusion based imaging as well as targeted imaging encapsulated by nuclear medicine or PET.

"The constituent imaging features that are extracted and analyzed as described above, are associated with a given image(s), imaging study(s) or examination(s). These extracted or abstracted image features independently or combinatorally define elements or components of the image, or the composite imaging appearance itself, and are called imaging phenotypes. The imaging phenotypes are then correlated with the large scale biological data. The resulting imaging phenotype-large scale biological data association is now termed a radiophenotype.

"An association map between each radiophenotype and the large scale biological data is thus constructed based on said correlation. The underlying large scale molecular associations with each radiophenotype (and vice versa) are defined as the radiogenotype (i.e. the molecular associations that define, or are associated with a particular radiophenotype(s)). Thus, the association map that is constructed consists of any N number of radiophenotypes associated to any X number of constituents from the large scale biological dataset yielding any Y number of these constituents that are associated to each radiophenotype, resulting in a radiogenotype. These radiophenotype-radiogenotype associations, or radiogenomic associations, result in a detailed association map which can then serve as a reference against which other images, imaging studies or examinations and/or larges scale biology can then be independently and bi-directionally evaluated against. Additionally, new radiophenotypes and radiogenotypes, and thus radiogenomic associations can be constructed and thus defined, from the application of mathematical or logical operations applied to existing associations. An example would be addition or subtraction of radiophenotypes from an existing radiophenotype to create or define a new radiophenotype, or inclusion of conditional statements (e.g. radiophenotype A=radiophenotype X, plus radiophenotype Y and radiophenotype Z, minus radiophenotype 1). Similarly, this can be applied to radiogenotypes to construct new radiogenotypes, or to radiogenomic associations as well. Thus, the radiophenotypes, radiogenotypes, and radiogenomic associations can then all ultimately be evaluated independently of the original association map.

"Thus, radiophenotypes are imaging phenotypes that are associated with large scale biology. A radiophenotype, although it is intimately linked to its large scale biological association, can thus, in one embodiment be viewed as a molecular surrogate of its radiogenotype, and can now exist independent of this. Radiogenotypes are the molecular constituents from the large scale biological data that are associated with the radiophenotype. Similarly, radiogenotypes, can in one embodiment, be viewed as surrogates for their underlying imaging phenotype or radiophenotype and can now exist independent of this as well. The bi-directional relationship between each radiophenotype and its radiogenotype is called a radiogenomic association. The association map is the composite of all the radiogenomic associations.

"The following examples demonstrate the present invention."

For additional information on this patent application, see: Kuo, Michael D. Method for Integrating Large Scale Biological Data with Imaging. Filed December 6, 2013 and posted July 17, 2014. Patent URL: http://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.html&r=2751&p=56&f=G&l=50&d=PG01&S1=20140710.PD.&OS=PD/20140710&RS=PD/20140710

Keywords for this news article include: Algeomics, Algeomics Llc, Biochemical, Biochemistry, Cancer Gene Therapy, Chemicals, Chemistry, Emerging Technologies, Genetics, Histopathology, Magnetic Resonance Imaging, Molecular Imaging, Nanotechnology, Nuclear Medicine, Oncology, Perfusion, Protein Expression, Proteomics, Therapeutics.

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Source: Cancer Gene Therapy Week


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