Methods for identifying, diagnosing, and predicting survival of lymphomas

A method of identifying a lymphoma sample as Burkitt lymphoma (BL), the method comprising:

• a) isolating gene expression product from lymphoma sample X, wherein the gene expression product is mRNA and sample X is from a patient;
• 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, and wherein said first lymphoma type is BL;
• c) 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, wherein the gene expression data is mRNA expression data;
• 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) 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;
• f) 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;
• g) contacting the gene expression product from sample X to a plurality of probes to thereby obtain gene expression data for said subset of genes G1 and said subset of genes G2 in sample X;
• h) 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;
• i) 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, {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 φ(LPS(X)1;
• {circumflex over (μ)}1,{circumflex over (σ)}1) is the normal density function evaluated at LPS(X)1 with mean {circumflex over (μ)}1 and variance {circumflex over (σ)}1, and φ(LPS(X)1;
• {circumflex over (μ)}2, {circumflex over (σ)}2), is the normal density function evaluated at LPS(X)1 with mean {circumflex over (μ)}2 and variance {circumflex over (σ)}2;
• and
• j) 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, {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 φ(LPS(X)2;
• {circumflex over (μ)}1,{circumflex over (σ)}1) is the normal density function evaluated at LPS(X)2 with mean {circumflex over (μ)}1 and variance {circumflex over (σ)}1, and φ(LPS(X)2;{circumflex over (μ)}2, {circumflex over (σ)}2) is the normal density function evaluated at LPS(X)2 with mean {circumflex over (μ)}2 and variance {circumflex over (σ)}2;
• wherein sample X is classified as BL if q1 and q2 are both greater than 90%.