Methods and compositions of molecular profiling for disease diagnostics

Abstract

The present invention relates to compositions, kits, and methods for molecular profiling and cancer diagnostics, including but not limited to gene expression product markers, alternative exon usage markers, and DNA polymorphisms associated with cancer. In particular, the present invention provides molecular profiles associated with thyroid cancer, methods of determining molecular profiles, and methods of analyzing results to provide a diagnosis.

1 . A computer system for providing a classification as to whether a sample of thyroid tissue is benign or malignant, comprising:
 (a) an input that accepts assay data comprising an expression level of one or more microRNAs in a sample of thyroid tissue, which sample of thyroid tissue is obtained from a subject suspected of having thyroid cancer;   (b) storage media operatively coupled to said input that stores said assay data comprising said expression level;   (c) a computer processor that is programmed to (i) access said assay data in said storage media, (ii) perform a comparison of said expression level in said sample of thyroid tissue to an expression level of said one or more microRNAs in one or more sets of reference samples to provide a differential expression level of said one or more microRNAs with respect to said one or more sets of reference samples, and (iii) based on said comparison, classify said sample of thyroid tissue as benign or malignant at a sensitivity greater than 90% and a specificity greater than 95%; and   (d) an output that provides said classification to a user.   
     
     
         2 . The computer system of  claim 1 , wherein said output is a computer screen or an electronic communication. 
     
     
         3 . The computer system of  claim 1 , wherein said storage media further comprises an indication that said sample of thyroid tissue is ambiguous or suspicious, which indication is associated with said assay data stored in said storage media. 
     
     
         4 . A method for diagnosing a subject suspected of having thyroid cancer, comprising:
 (a) obtaining a sample of thyroid tissue from said subject;   (b) assaying said sample of thyroid tissue for an expression level for one or more microRNAs in said sample of thyroid tissue; and   (c) classifying said sample of thyroid tissue as benign or malignant by applying an algorithm to said expression level from step (b), said algorithm comprising comparing said expression level in said sample of thyroid tissue to an expression level of said one or more microRNAs in one or more sets of reference samples to provide a differential expression level of said one or more microRNAs with respect to said one or more sets of reference samples, wherein said algorithm has a sensitivity greater than 90% and a specificity greater than 95%.   
     
     
         5 . The method of  claim 4 , wherein said sample of thyroid tissue is assayed for an expression for two or more microRNAs in said sample of thyroid tissue. 
     
     
         6 . The method of  claim 4 , wherein said sample of thyroid tissue is assayed for an expression for three or more microRNAs in said sample of thyroid tissue. 
     
     
         7 . The method of  claim 4 , wherein said one or more microRNAs are selected from said microRNAs listed in  FIG. 14 ,  FIG. 15 ,  FIG. 16  and  FIG. 17 . 
     
     
         8 . The method of  claim 4 , wherein said one or more microRNAs are selected from said microRNAs listed in  FIG. 16  and  FIG. 17 . 
     
     
         9 . The method of  claim 4 , wherein said one or more microRNAs comprise mir-222 and/or mir-197. 
     
     
         10 . The method of  claim 9 , wherein said one or more microRNAs comprise mir-222 and mir-197. 
     
     
         11 . The method of  claim 4 , wherein said sample of thyroid tissue 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. 
     
     
         12 . The method of  claim 11 , wherein said sample of thyroid tissue is obtained by fine needle aspiration (FNA). 
     
     
         13 . The method of  claim 4 , wherein said algorithm comprises comparing said expression level in said sample of thyroid tissue to an expression level of said one or more microRNAs in two or more sets of reference samples. 
     
     
         14 . The method of  claim 4 , wherein said one or more sets of reference samples comprise a set of normal thyroid tissue. 
     
     
         15 . The method of  claim 4 , wherein said one or more sets of reference samples comprise at least one set of benign thyroid tissue. 
     
     
         16 . The method of  claim 15 , wherein said at least one set of benign thyroid tissues has a pathology of benign nodule (BN), follicular neoplasm (FN), follicular adenoma (FA), lymphocytic thyroiditis (LCT), or nodular hyperplasia (NHP). 
     
     
         17 . The method of  claim 15 , wherein said at least one set of benign thyroid tissue has a pathology of Hurthle cell adenoma (HA). 
     
     
         18 . The method of  claim 4 , wherein said one or more sets of reference samples comprise at least one set of malignant thyroid tissue. 
     
     
         19 . The method of  claim 18 , wherein said at least one set of malignant thyroid tissue has a pathology of follicular carcinoma (FC), follicular variant of papillary thyroid carcinoma (FVPTC), papillary thyroid carcinoma (PTC), or medullary thyroid carcinoma (MTC). 
     
     
         20 . The method of  claim 18 , wherein said at least one set of malignant thyroid tissue has a pathology of Hurthle cell carcinoma (HC). 
     
     
         21 . The method of  claim 4 , further comprising subjecting said sample of thyroid tissue to cytological testing that indicates that said sample of thyroid tissue is ambiguous or suspicious. 
     
     
         22 . The method of  claim 4 , wherein said algorithm is a trained algorithm. 
     
     
         23 . The method of  claim 22 , wherein said algorithm is trained by at least 24 samples. 
     
     
         24 . The method of  claim 4 , wherein said algorithm has a specificity greater than 99%. 
     
     
         25 . The method of  claim 4 , wherein said algorithm has a sensitivity greater than 95%. 
     
     
         26 . The method of  claim 25 , wherein said algorithm has a sensitivity greater than 99%. 
     
     
         27 . The method of  claim 4 , wherein said algorithm has a negative predictive value greater than 95%. 
     
     
         28 . The method of  claim 4 , wherein said expression level of said one or more microRNAs is measured by microarray, SAGE, blotting, RT-PCR, quantitative PCR, or sequencing. 
     
     
         29 . The method of  claim 4 , wherein said classifying of (c) is performed by a computer processor. 
     
     
         30 . The method of  claim 4 , further comprising diagnosing and/or treating said subject based on a result of said classifying of (c).