Ex Parte Schadt et al

Patent Trial and Appeal Board

Sep 6, 2016

10523143 (P.T.A.B. Sep. 6, 2016)

UNITED STA TES p A TENT AND TRADEMARK OFFICE
APPLICATION NO. FILING DATE FIRST NAMED INVENTOR
10/523,143 08/16/2005 Eric E Schadt
26389 7590 09/08/2016
CHRISTENSEN O'CONNOR JOHNSON KINDNESS PLLC
1201 THIRD A VENUE
SUITE 3600
SEATTLE, WA 98101-3029
UNITED STATES DEPARTMENT OF COMMERCE
United States Patent and Trademark Office
Address: COMMISSIONER FOR PATENTS
P.O. Box 1450
Alexandria, Virginia 22313-1450
www .uspto.gov
ATTORNEY DOCKET NO. CONFIRMATION NO.
ROSA134261 6414
EXAMINER
BRUSCA, JOHNS
ART UNIT PAPER NUMBER
1631
NOTIFICATION DATE DELIVERY MODE
09/08/2016 ELECTRONIC
Please find below and/or attached an Office communication concerning this application or proceeding.
The time period for reply, if any, is set in the attached communication.
Notice of the Office communication was sent electronically on above-indicated "Notification Date" to the
following e-mail address( es):
efiling@cojk.com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
BEFORE THE PATENT TRIAL AND APPEAL BOARD
Ex parte ERIC E. SCHADT and
STEPHANIE A. MONKS 1
Appeal2013-008350
Application 10/523,143
Technology Center 1600
Before ERIC B. GRIMES, ULRIKE W. JENKS, and JACQUELINE T.
HARLOW, Administrative Patent Judges.
GRIMES, Administrative Patent Judge.
DECISION ON APPEAL
This is an appeal under 35 U.S.C. § 134 involving claims to a method
of associating a human gene with a clinical trait, which have been rejected as
obvious. We have jurisdiction under 35 U.S.C. § 6(b).
We reverse.
STATEMENT OF THE CASE
Genetics data have been used in the field of trait analysis in order
to attempt to identify the genes that affect such traits. A key
development in such pursuits has been the development of large
1 Appellants identify the Real Party in Interest as Merck Sharp & Dohme
Corp. (Appeal Br. 1.)
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Application 10/523,143
collections of molecular genetic markers, which can be used to
construct detailed genetic maps of species, such as humans.
These maps are used in Quantitative Trait Locus (QTL) mapping
methodologies. . . [that] provide statistical analysis of the
association between phenotypes and genotypes for the purpose
of understanding and dissecting the regions of a genome that
affect traits.
(Spec. 3--4.)
"A quantitative trait locus (QTL) is a region of any genome that is
responsible for some percentage of the variation in the quantitative trait of
interest." (Id. at 4.)
Claims 1, 3, 4, 6-21, 23-25, 28-30, 33, 35-37, 40, 42--49, 54, 107,
252, 258-262, 273, and 274 are on appeal. Claims 1 and 273 are illustrative
and read as follows:
1. A method for associating a gene G in the human genome with a
clinical trait T exhibited by one or more individuals in a plurality of
individuals, the method comprising:
(A) identifying an expression quantitative trait loci (eQTL) for said
gene G using a first quantitative trait loci (QTL) analysis, wherein said first
QTL analysis uses a plurality of expression statistics for said gene G as a
quantitative trait, wherein each expression statistic in said plurality of
expression statistics represents an expression value for said gene G in an
individual in said plurality of individuals, and wherein said first QTL
analysis comprises (1) testing for linkages between the plurality of
expression statistics for said gene G, and a plurality of locations along at
least one chromosome of the plurality of individuals, comprising (i) testing
for a linkage between (a) the genotypes of said plurality of individuals at a
position in the at least one chromosome and (b) said plurality of expression
statistics for said gene G; (ii) advancing the position in the chromosome by
an amount; and (iii) repeating steps (i) and (ii) until the end of the
chromosome is reached, or (2) comparing genotype data from each
individual in the plurality of individuals to the plurality of expression
statistics for said gene G using allelic association analysis;
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(B) identifying a clinical quantitative trait loci ( cQTL) that is linked
to said clinical trait Tusing a second QTL analysis, wherein said second
QTL analysis uses a plurality of phenotypic values as a quantitative trait,
wherein each phenotypic value in said plurality of phenotypic values is a
phenotypic value for said clinical trait T in an individual in said plurality of
individuals; and
(C) determining (1) whether said eQTL and said cQTL co localize
to the same locus in the human genome by performing a test for pleiotropy
to determine whether said eQTL and said cQTL are represented by a QTL
that is common to both said eQTL and said cQTL, and (2) whether the locus
of said eQTL corresponds to the physical location of said gene G in the
human genome;
wherein, when said test for pleiotropy indicates that said eQTL and
said cQTL colocalize to the same locus, and when said locus of said eQTL
corresponds to the physical location of said gene G in the human genome,
said gene G is deemed to be associated with said clinical trait T; and
wherein the identifying step (A), the identifying step (B), and the
determining step (C)(l) are executed using a suitably programmed
computer.
273. A method for associating a gene Gin the human genome with a
clinical trait T exhibited by one or more individuals in a plurality of
individuals, the method comprising:
(A) identifying an expression quantitative trait loci (eQTL) for said
gene G using a first quantitative trait loci (QTL) analysis, wherein said first
QTL analysis uses a plurality of expression statistics for said gene G as a
quantitative trait, wherein each expression statistic in said plurality of
expression statistics represents an expression value for said gene G in an
individual in said plurality of individuals, and wherein said first QTL
analysis comprises (1) testing for linkages between the plurality of
expression statistics for said gene G and a plurality of locations along a
genetic map of the plurality of individuals, or (2) comparing genotype data
from each individual in the plurality of individuals to the plurality of
expression statistics for said gene G using allelic association analysis;
(B) identifying a clinical quantitative trait loci ( cQTL) that is linked to
said clinical trait T using a second QTL analysis, wherein said second QTL
analysis uses a plurality of phenotypic values as a quantitative trait, wherein
each phenotypic value in said plurality of phenotypic values is a phenotypic
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value for said clinical trait T in an individual in said plurality of individuals,
wherein the second QTL analysis comprises (i) testing for linkage between
(a) the genotypes of said plurality of individuals at a position in a
chromosome of said plurality of individuals and (b) said plurality of
phenotypic values; (ii) advancing the position in said chromosome by an
amount; and (iii) repeating steps (i) and (ii) until the end of the chromosome
is reached; and
(C) determining (1) whether said eQTL and said cQTL co localize to
the same locus in the human genome by performing a test for pleiotropy to
determine whether said eQTL and said cQTL are represented by a QTL that
is common to both said eQTL and said cQTL, and (2) whether the locus of
said eQTL corresponds to the physical location of said gene G in the human
genome, wherein, when said test for pleiotropy indicates that said eQTL and
said cQTL colocalize to the same locus, and said locus of said eQTL
corresponds to the physical location of said gene G in the human genome,
said gene G is deemed to be associated with said clinical trait T, and
wherein the identifying step (A), the identifying step (B), and the
determining step (C)(l) are executed using a suitably programmed
computer.
Claims 54 and 107 are also independent, and are directed to a
computer program product and a computer system, respectively, for carrying
out the process recited in claim 1.
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The claims stand rejected under 35 U.S.C. § 103(a) as follows:
Claims 1, 6-14, 17, 18, 23-25, 28-30, 35-37, 40, 42--49, 273, and 274
based on Aitman '99,2 Aitman '97,3 and Comuzzie4 (Ans. 7);
Claims 1 and 12-16 based on Aitman '99, Aitman '97, Comuzzie, and
Hamilton5 (Ans. 13);
Claims 1, 54, 107, 252, 258, and 262 based on Aitman '99, Aitman
'97, Comuzzie, and Manly6 (Ans. 15);
Claims 1, 3, 4, 19-21, 29, and 33 based on Aitman '99, Aitman '97,
Comuzzie, and Murray7 (Ans. 17); and
Claims 54 and 107 based on Aitman '99, Aitman '97, and Manly
(Ans. 2).
2 Aitman et al., Identification of Cd3 (Fat) as an insulin-resistance gene
causing defective fatty acid and glucose metabolism in hypertensive rats, 21
Nature Genetics 76-83 (1999).
3 Aitman et al., Quantitative trait loci for cellular defects in glucose and
fatty acid metabolism in hypertensive rats, 16 Nature Genetics 197-201
(1997).
4 Comuzzie et al., A major quantitative trait locus determining serum leptin
levels andfat mass is located on human chromosome 2, 15 Nature Genetics
273-276 (1997).
5 Hamilton et al., Increased obese mRNA expression in omental fat cells
from massively obese humans, 1 Nature Medicine 953-956(1995).
6 Manly and Olson, Overview of QTL mapping software and introduction to
Map Manager QT, 10 Mammalian Genome 327-334 (1999).
7 Murray et al., A Comprehensive Human Linkage Map with Centimorgan
Density, 265 Science 2049-2054 (1994).
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DISCUSSION
All of the claims stand rejected as obvious based on the combination
of Aitman '99, Aitman '97, and Comuzzie.8 The same issue is dispositive
for each of the rejections.
The Examiner finds that Aitman '99 discloses "correlating a cQTL of
quantitative traits of diabetes (insulin-mediated glucose uptake and
catecholamine-mediated lipolysis) with an eQTL (Cd36) that correlates with
the same traits" in spontaneously hypertensive rats. (Ans. 8.) The Examiner
notes that Aitman '99 does not disclose scanning of chromosomes to map
either an eQTL or a cQTL, and does not disclose analysis of a human QTL.
(Id. at 10.)
The Examiner finds that Aitman '97 discloses "a quantitative trait
locus on [rat] chromosome 4 for defective insulin and catecholamine action.
This locus is equivalent to a clinical QTL because it correlates a quantitative
clinical trait with a genetic locus." (Id. at 11.) The Examiner finds that
Comuzzie discloses "a genome scan for a human quantitative trait locus."
(Id.)
The Examiner concludes that it would have been obvious "to scan
chromosomes to map an expression QTL because any quantitative trait
(either clinical or expression) could be mapped to a locus by the method of
Aitman et al. '97, and Aitman et al. '99 shows the importance of mapping an
expression QTL." (Id. at 12.) The Examiner also concludes that "[i]t would
8 Claims 54 and 107 also stand rejected based on Aitman '99, Aitman '97,
and Manly (Ans. 2) but this rejection is cumulative to the rejection based on
Aitman '99, Aitman '97, Comuzzie, and Manly (Ans. 15).
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have been further obvious to test for a human quantitative trait locus because
Comuzzie et al. successfully tests a set of human patients for a quantitative
trait locus." (Id.)
Appellants argue that
the claimed invention integrates a first quantitative trait locus
(QTL) analysis for a gene expression trait with an independent
second QTL analysis for a clinical trait, and culminates in the
comparison of the two independent analyses using a pleiotropy
test to determine statistically if the identified QTLs co localize to
the same locus in the genome.
(Br. 12.)
Appellants argue that Aitman '99 does not disclose the eQTL analysis
of claim 1 "because it fails to teach or suggest either linkage or allelic
association analysis of the expression-based data with the genotypes of each
individual." (Id. at 15.) Appellants argue that Aitman '97 also does not
disclose the recited eQTL analysis (id. at 20-21) and "Comuzzie does not
teach or suggest determining whether an eQTL and a cQTL colocalize to the
same locus in the human genome." (Id. at 39.) Appellants conclude that the
claimed method would not have been obvious based on the cited references
because they do not teach all of the claimed elements and there would have
been no motivation to modify the references to practice the claimed method.
(Id. at 45.)
We agree with Appellants that the Examiner has not established a
prima facie case of obviousness based on the cited references. Claims 1 and
273 both require identifying an expression quantitative trait locus ( eQTL)
using expression statistics that represent expression values of a gene in
different individuals. Claims 1 and 273 require the eQTL analysis to test for
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linkage between different locations of individuals' genomes and the
expression statistics. 9
Aitman '97 describes "two quantitative trait loci (QTLs) for defective
insulin action, on chromosomes 4 and 12" in the spontaneously hypertensive
rat (SHR) (Aitman '97 at 197, left col.) Aitman '97 thus identifies a clinical
quantitative trait locus ( cQTL) via QTL analysis, in which a region of the
genome is associated with a particular phenotype. Although Aitman '97
identifies "[ s ]everal interesting candidate[]" genes that map to the region of
chromosome 4 (id. at 198, right col.), the Examiner has not pointed to any
eQTL analysis that tests for linkage between expression statistics for a gene
in different individuals and locations in a genome.
Comuzzie similarly discloses a QTL on human chromosome 2 that is
associated with serum leptin levels. (Comuzzie 273, title and abstract.)
Comuzzie identifies several genes in the region of chromosome 2 that could
affect obesity (id. at 274, bridging paragraph) but the Examiner has not
pointed to any eQTL analysis in Comuzzie that tests for linkage between
expression statistics for a gene in different individuals with locations in a
genome.
Aitman '99 builds on the work reported in Aitman '97. Aitman '99
reports "the identification of a defective SHR gene, Cd3 6, that is at the peak
of linkage to SHR defects in glucose and fatty acid metabolism, has multiple
coding sequence variants in its cDNA and whose protein product is
undetectable in SHR adipocyte plasma membrane." (Aitman '99 at 76, right
9 The claims also recite an alternative method ("allelic association analysis")
but the Examiner does not rely on that method.
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col.) Aitman '99 reports that "differential expression studies using DNA
microarrays" showed that Cd3 6 was underexpressed in SHR compared to
two other strains. (Id. at 77, right col.)
Aitman '99 states that further experiments were performed to map
Cd3 6 in the rat genome, using "PCR to detect chromosomal fragments that
retain Cd36 in a rat/hamster RH [radiation hybrid] panel. The retention-
fragment pattern for Cd3 6 ... mapped the gene to within our microsatellite
framework map of the chromosome 4 QTL." (Id. at 78, left col.) Thus,
Aitman '99 discloses identifying a gene with a reduced expression level in
an inbred strain of rats, then performing a separate mapping experiment to
locate the gene in the rat genome.
While Aitman '99 therefore describes linkage between the expression
of the Cd3 6 gene and a single position on a chromosome, it does not
describe testing for linkages between expression statistics for a gene in
different individuals with a plurality of locations in a genome, as required by
claims 1 and 273. Independent claims 54 and 107 also include the same
limitation.
The Examiner cites Manly for its review of computer software for
carrying out QTL analysis (Ans. 5, 16), Hamilton for its disclosure of a
correlation between obesity and expression of the OB gene (id. at 14), and
Murray for its disclosure of a human chromosome map (id. at 17). The
Examiner therefore has not pointed to any disclosure in the remaining
references that makes up for the deficiency discussed above.
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SUMMARY
Because a preponderance of the evidence does not show that the cited
references would have made obvious all of the limitations of the claims, we
reverse all of the rejections on appeal.
REVERSED
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