US 2007/0105136 A1Published application

Methods for identifying, diagnosing, and predicting survival of lymphomas

AssigneesThe United States of America, as represented by the Secretary, Department of Health and Human Services, Board of Regents of the University of Nebraska, University of Rochester, Arizona Board of Regents/Behalf of University of Arizona, Universitat De Barcelona, Fundacio Clinic, Hospital Clinic, Julius-Maximilians-University of Wuerzburg, British Columbia Cancer Agency Branch, Oslo University Hospital HF, Queen Mary & Westfield College, University of London

InventorsLouis Staudt, George Wright, Sandeep Dave, Bruce Tan, John Powell, Wyndham Wilson, Elaine Jaffe, Wing Chan, Timothy Greiner, Dennis Weisenburger, James Armitage, Kai Fu, Richard Fisher, Lisa Rimsza, Thomas Miller, Thomas Grogan, Elias Campo, Silvia Bea, Itziar Salaverria, Armando Lopez-Guillermo, Emili Montserrat, Victor Moreno, Andreas Zettl, German Ott, Hans-Konrad Muller-Hermelink, Andreas Rosenwald, Julie Vose, Randy Gascoyne, Joseph Connors, Erlend Smeland, Stein Kvaloy, Harald Holte, Jan Delabie, T. Andrew Lister

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Abstract

Gene expression data provides a basis for more accurate identification and diagnosis of lymphoproliferative disorders. In addition, gene expression data can be used to develop more accurate predictors of survival. The present invention discloses methods for identifying, diagnosing, and predicting survival in a lymphoma or lymphoproliferative disorder on the basis of gene expression patterns. The invention discloses a novel microarray, the Lymph Dx microarray, for obtaining gene expression data from a lymphoma sample. The invention also discloses a variety of methods for utilizing lymphoma gene expression data to determine the identity of a particular lymphoma and to predict survival in a subject diagnosed with a particular lymphoma. This information will be useful in developing the therapeutic approach to be used with a particular subject.

Claims11

Dependent claims

A method for predicting survival in a diffuse large B cell lymphoma (DLBCL) subject comprising the steps of:

a) obtaining a biopsy sample from said subject;
b) obtaining gene expression data for a set of genes in said biopsy sample;
c) averaging the gene expression level of genes from a proliferation gene expression signature to obtain a proliferation gene expression signature value;
d) averaging the gene expression level of genes from a germinal center B cell gene expression signature to obtain a germinal center B cell gene expression signature value;
e) averaging the gene expression level of genes from an MHC class II gene expression signature to obtain an MHC class II gene expression signature value;
f) averaging the gene expression level of genes from a lymph node gene expression signature to obtain a lymph node gene expression signature value;
g) measuring the expression level of BMP6 to obtain a BMP6 expression value;
h) calculating a survival predictor score using an equation:
[0.241*(proliferation gene expression signature value)]+[0.310*(BMP6 expression value)]−[0.290*(germinal center B cell gene expression signature value)]−[0.311*(MHC class II gene expression signature value)]−[0.249*(lymph node gene expression signature value)], wherein a higher survival predictor score is associated with worse survival;
i) analyzing said sample for a gain or amplification in the 3p11-p12 region of chromosome 3, wherein a gain or amplification in said region is associated with worse survival.

7 more elements

A method for classifying a lymphoma biopsy sample as cyclin D1-negative MCL comprising:

a) obtaining a candidate sample comprising a biopsy sample with MCL morphology that does not exhibit cyclin D1 expression;
b) creating one or more lymphoma type pairs wherein each lymphoma type pair represents a combination of a first lymphoma type and a second lymphoma type, wherein said first lymphoma type is MCL;
c) for each lymphoma type pair, obtaining gene expression data for a set of genes G in a set of known samples of said first lymphoma type and a set of known sample of said second lymphoma type;
d) calculating a series of scale factors, wherein each scale factor represents a difference in gene expression between said first lymphoma type and said second lymphoma type for one of the genes belonging to said set of genes G;
e) identifying a subset of genes g that are differentially expressed between said first lymphoma type and said second lymphoma type;
f) generating a series of linear predictor scores for said set of known samples of said first lymphoma type and said set of known samples of said second lymphoma type based on the expression of said subset of genes g identified in step (e);
g) obtaining gene expression data for said subset of genes g for said candidate sample;
h) generating a linear predictor score for said candidate sample based on the expression of said subset of genes g;
i) calculating a probability q that said candidate sample is MCL by:
q
=
ϕ
⁡
(
LPS
⁡
(
X
)
;
μ
^
1
,
σ
^
1
)
ϕ
⁡
(
LPS
⁡
(
X
)
;
μ
^
1
,
σ
^
1
)
+
ϕ
⁡
(
LPS
⁡
(
X
)
;
μ
^
2
,
σ
^
2
)
wherein LPS(X) is the linear predictor score for said candidate sample, φ(x;
μ, σ) is the normal density function with mean μ and standard deviation σ, {circumflex over (μ)}1 and {circumflex over (σ)}1 are the mean and variance of the linear predictor scores for said set of known samples of said first lymphoma type, and {circumflex over (μ)}2 and {circumflex over (σ)}2 are the mean and variance of the linear predictor scores for said set of known samples of said second lymphoma type, and wherein a probability q greater than 90% indicates that said candidate sample is cyclin D1-negative MCL.

62 more elements

A method of identifying a lymphoma sample as BL comprising:

a) creating a series of lymphoma type pairs, wherein each lymphoma type pair represents a combination of a first lymphoma type and a second lymphoma type, and wherein said first lymphoma type is BL;
b) for each lymphoma type pair, obtaining gene expression data for a set of genes G in said first lymphoma type and said second lymphoma type;
c) calculating a series of scale factors, wherein each scale factor represents a difference in gene expression between said first lymphoma type and said second lymphoma type for one of the genes belonging to said set of genes G;
d) dividing said set of genes G into two gene subsets G1 and G2, wherein G1 consists of c-myc and c-myc target genes and G2 consists of z genes from said set of genes G with the largest scale factors, and wherein G2 does not include any genes falling into G1;
e) generating a first and second series of linear predictor scores for a set of known samples belonging to said first lymphoma type and a set of known samples belonging to said second lymphoma type, wherein said first series of linear predictor scores are based on expression of said subset of genes G1 and said second series of linear predictor scores are based on expression of said subset of genes G2;
f) measuring expression of said subset of genes G1 and said subset of genes G2 in sample X;
g) generating a first linear predictor score for sample X based on the expression of the genes in said subset of genes G1 and a second linear predictor score for sample X based on the expression of the genes in said subset of genes G2;
h) calculating a first probability q1 that sample X belongs to said first lymphoma type by:
q
1
=
ϕ
⁡
(
LPS
⁡
(
X
)
1
;
μ
^
1
,
σ
^
1
)
ϕ
⁡
(
LPS
⁡
(
X
)
1
;
μ
^
1
,
σ
^
1
)
+
ϕ
⁡
(
LPS
⁡
(
X
)
1
;
μ
^
2
,
σ
^
2
)
wherein LPS(X)1 is the first linear predictor score for sample X, φ(x;
μ, σ) is the normal density function with mean μ and standard deviation σ, {circumflex over (μ)}1 and {circumflex over (σ)}1 are the mean and variance of the linear predictor scores for said set of known samples belonging to said first lymphoma type, and {circumflex over (μ)}2 and {circumflex over (σ)}2 are the mean and variance of the linear predictor scores for said known samples belonging to said second lymphoma type;
and
i) calculating a second probability q2 that sample X belongs to said first lymphoma type by:
q
2
=
ϕ
⁡
(
LPS
⁡
(
X
)
2
;
μ
^
1
,
σ
^
1
)
ϕ
⁡
(
LPS
⁡
(
X
)
2
;
μ
^
1
,
σ
^
1
)
+
ϕ
⁡
(
LPS
⁡
(
X
)
2
;
μ
^
2
,
σ
^
2
)
wherein LPS(X)2 is the second linear predictor score for sample X, φ(x;
μ, σ) is the normal density function with mean μ and standard deviation σ, {circumflex over (μ)}1 and {circumflex over (σ)}1 are the mean and variance of the linear predictor scores for said set of known samples belonging to said first lymphoma type, and {circumflex over (μ)}2 and {circumflex over (σ)}2 are the mean and variance of the linear predictor scores for said known samples belonging to said second lymphoma type;
wherein sample X is classified as BL if q1 and q2 are both greater than 90%.

128 more elements

Dependencies are inferred from the claim text.

Facts

Application: 11/493,387Filed 25 Jul 2006
Priority: 3 Sep 2003
Published: 10 May 2007
Term ends: Not estimatedThe source records don’t support an estimate.

Classification16 codes

G01N 33/6842
General methods of protein analysis not limited to specific proteins or families of proteins: proteomic analysis of subsets of protein mixtures…
C12Q 1/6886
For diseases caused by alterations of genetic material: for cancer
G16B 25/00
Ict specially adapted for hybridisation
G16B 25/10
ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression: gene or protein expression profiling;…

Family

Application60/500,377Us provisional application · filed 3 Sep 2003
Application10/934,930Continuation in part · filed 3 Sep 2004
Application11/493,387Continuation in part · filed 25 Jul 2006
US 2005/0164231 A1Published application
US 7,711,492 B2Related patent
US 8,131,475 B2Related patent
US 2007/0105136 A1This patent

From the US filing history. Foreign family members aren’t listed.