[Federal Register Volume 70, Number 25 (Tuesday, February 8, 2005)]
[Notices]
[Pages 6705-6706]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 05-2366]


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DEPARTMENT OF HEALTH AND HUMAN SERVICES

National Institutes of Health


Government-Owned Inventions; Availability for Licensing

AGENCY: National Institutes of Health, Public Health Service, DHHS.

ACTION: Notice.

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SUMMARY: The inventions listed below are owned by an agency of the U.S. 
Government and are available for licensing in the U.S. in accordance 
with 35 U.S.C. 207 to achieve expeditious commercialization of results 
of federally-funded research and development. Foreign patent 
applications are filed on selected inventions to extend market coverage 
for companies and may also be available for licensing.

ADDRESSES: Licensing information and copies of the U.S. patent 
applications listed below may be obtained by writing to the indicated 
licensing contact at the Office of Technology Transfer, National 
Institutes of Health, 6011 Executive Boulevard, Suite 325, Rockville, 
Maryland 20852-3804; telephone: 301/496-7057; fax: 301/402-0220. A 
signed Confidential Disclosure Agreement will be required to receive 
copies of the patent applications.

Monoclonal Antibody 90.12 Recognizes a Novel B Cell Surface Antigen 
Upregulated on Both Activated and Apoptotic Lymphocytes

Marjorie A. Shapiro et al. (FDA).
DHHS Reference No. E-195-2004/0--Research Tool.
Licensing Contact: Cristina Thalhammer-Reyero; 301/435-4507; 
[email protected].

    Monoclonal antibody 90.12 recognizes a molecule expressed on the 
surface of a subset of B lymphocytes and on all types of blood cells. 
This antigen is increased upon stimulation of B and T lymphocytes as 
well as on cells undergoing programmed cell death. Amino acid 
sequencing of the beginning of the protein suggests that it is a member 
of the S100 family of calcium binding proteins. The antibody is further 
described in ``Characterization of a B cell surface antigen with 
homology to the S100 protein MRP8'' by Shapiro MA, Fitzsimmons SP, 
Clark KJ, Biochem Biophys Res Commun. 1999 Sep 16;263(1):17-22 and ``A 
novel activation induced lymphocyte surface antigen, 90.12, is also 
expressed on apoptotic cells'' by Clark KJ, Monser M, Stein KE, Shapiro 
MA, Scand J Immunol. 2000 Feb;51(2):155-63.

Methods for Analyzing High Dimensional Data for Classifying, 
Diagnosing, Prognosticating, and/or Predicting Diseases and Other 
Biological States

Javed Khan and Paul S. Meltzer (NHGRI), et al.
U.S. Patent Application No. 10/133,937 filed 25 Apr 2002 (DHHS 
Reference No. E-324-2001/0-US-01).
Licensing Contact: Cristina Thalhammer-Reyero; 301/435-4507; 
[email protected].

    This invention relates to a method of using supervised pattern 
recognition methods to classifying, diagnosing, predicting, or 
prognosticating various diseases. The method includes

[[Page 6706]]

obtaining high dimensional experimental data, such as gene expression 
profiling data, filtering the data, reducing the dimensionality of the 
data through use of one or more methods, training a supervised pattern 
recognition method, ranking individual data points from the data, 
choosing multiple data points from the data based on the relative 
ranking, and using the multiple data points to determine if an unknown 
set of experimental data indicates a diseased condition, a predilection 
for a diseased condition, or a prognosis about a diseased condition.
    Artificial neural networks (ANNs) are computer-based algorithms 
capable of pattern recognition particularly suited to making diagnoses. 
ANNs do not require explicit encoding of process knowledge in a set of 
rules and can be trained from examples to recognize and categorize 
complex patterns. ANNs learn more efficiently when the data to be input 
into the neural network is preprocessed. Various ANN approaches to the 
analysis of data have seen extensive application to biomedical 
problems, including those in the areas of diagnosis and drug 
development. Unsupervised neural networks are also extensively used for 
the analysis of DNA microarray data.
    The technology is further described in J. Khan et al., 
``Classification and diagnostic prediction of cancers using gene 
expression profiling and artificial neural networks,'' Nature Medicine, 
7(6):673-679, June 2001.

Selections of Genes

Javed Khan and Paul S. Meltzer (NHGRI), et al.
U.S. Patent Application No. 10/159,563 filed 31 May 2002 (DHHS 
Reference No. E-324-2001/1-US-01).
Licensing Contact: Cristina Thalhammer-Reyero; 301/435-4507; 
[email protected].

    The invention provides selections of genes expressed in a cancer 
cell that function to characterize such cancer, and methods of using 
the same for diagnosis and for targeting the therapy of selected 
cancers. In particular, methods are provided to classify cancers 
belonging to distinct diagnostic categories, which often present 
diagnostic dilemmas in clinical practice, such as the small round blue 
cell tumors (SRBCTs) of childhood, including neuroblastoma (NB), 
rhabdomyosarcoma RMS), Burkitt's lymphoma (BL), and the Ewing family of 
tumors (EWS). More specifically, the invention is an application of 
Artificial Neural Networks (ANNs) for the diagnostic classification of 
cancers based on gene expression profiling data derived from cDNA 
microarrays. The ANNs were trained using as models. The ANNs then 
correctly classified all samples tested and identified the genes most 
relevant to the classification. Their study demonstrated the potential 
applications of these methods for tumor diagnosis and for the 
identification of candidate targets for therapy. The uniqueness of this 
method is taking gene expression data generated by microarrays, 
minimizing the genes from the original 1000s to less than 100, 
identifying which genes are the most relevant to a classification, 
which gives an immediate clue to the actual biological processes 
involved, not just surrogate markers which have no bearing on the 
biology.
    The technology is further described in J. Khan et al., 
``Classification and diagnostic prediction of cancers using gene 
expression profiling and artificial neural networks,'' Nature Medicine 
7(6): 673-679, June 2001.

    Dated: February 1, 2005.
Steven M. Ferguson,
Director, Division of Technology Development and Transfer, Office of 
Technology Transfer, National Institutes of Health.
[FR Doc. 05-2366 Filed 2-7-05; 8:45 am]
BILLING CODE 4140-01-U