Methods and compositions for classification of samples

Abstract

Disclosed herein are kits, compositions, and methods relating to the classification of samples. Methods disclosed herein can be used to identify sample mix-ups. Methods disclosed herein can also be used to diagnose conditions or to support treatment-related decisions.

1 . A method to predict a gender of a subject, the method comprising:
 (a) obtaining a biological sample from the subject;   (b) assaying an expression level of one or more gene expression products in the biological sample; and   (c) classifying the biological sample as from a male or a female by applying an algorithm to the expression level, thereby predicting the gender of the subject.   
     
     
         2 . The method of  claim 1 , further comprising comparing the predicted gender to a reported gender, wherein a sample mix-up is identified if the predicted gender and the reported gender are not the same. 
     
     
         3 . The method of  claim 1 , wherein the algorithm is a trained algorithm. 
     
     
         4 . The method of  claim 3 , wherein the trained algorithm comprises a linear SVM algorithm. 
     
     
         5 . The method of  claim 3 , wherein the trained algorithm comprises a sum of feature intensities according to the equation: Σ n=1   n F i S i  wherein F i  is the intensity of feature i and is S i  is 1 if the absolute value of the mean intensity difference between Male and Female training samples of feature F i  is greater than 1. 
     
     
         6 . The method of  claim 1 , wherein the biological sample is from thyroid tissue. 
     
     
         7 . The method of  claim 1 , wherein the biological sample is obtained by needle aspiration, fine needle aspiration, core needle biopsy, vacuum assisted biopsy, large core biopsy, incisional biopsy, excisional biopsy, punch biopsy, shave biopsy, or skin biopsy. 
     
     
         8 . The method of  claim 1 , wherein the biological sample is a fine needle aspiration of thyroid tissue. 
     
     
         9 . The method of  claim 1 , wherein the expression level is assayed by microarray, SAGE, blotting, RT-PCR, sequencing, and/or quantitative PCR. 
     
     
         10 . The method of  claim 1 , wherein the gene expression product is RNA. 
     
     
         11 . The method of  claim 10 , wherein the RNA is mRNA, rRNA, tRNA, or miRNA. 
     
     
         12 . The method of  claim 1 , wherein at least one of the gene expression products correspond a gene or TCID found in Table 1, Table 2, and/or Table 3. 
     
     
         13 . The method of  claim 1 , wherein at least one of the gene expression products corresponds to RPS4Y1, EIF1AY, UTY, USP9Y, CYorf15B, DDX3Y, or a combination thereof. 
     
     
         14 . The method of  claim 1 , wherein the gene expression products correspond to RPS4Y1, EIF1AY, UTY, USP9Y, CYorf15B, and DDX3Y. 
     
     
         15 . The method of  claim 1 , wherein the method is characterized by an error rate of less than about 3%. 
     
     
         16 . The method of  claim 1 , wherein the method is characterized by having a sensitivity of at least about 90% and a specificity of at least about 90%. 
     
     
         17 . The method of  claim 1 , wherein the method is characterized by having a sensitivity of about 100% and a specificity of about 99%. 
     
     
         18 . A method to identify lymphoma in a biological sample, the method comprising:
 (a) obtaining a biological sample from a subject;   (b) assaying an expression level of one or more gene expression products; and   (c) classifying the biological sample as containing or not containing lymphoma by applying an algorithm to the expression levels.   
     
     
         19 . The method of  claim 18 , further comprising analyzing the biological sample using one or more clinical classifiers if the sample does not contain lymphoma. 
     
     
         20 . The method of  claim 18 , wherein the algorithm is a trained algorithm. 
     
     
         21 .- 53 . (canceled)