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.

What is claimed is: 
     
         1 . A method of detecting a thyroid condition in a subject comprising:
 (a) obtaining a sample of thyroid from said subject;   (b) assaying expression levels of three or more gene expression products in a sample of thyroid tissue obtained from said subject, which gene expression products correspond to at least three genes or corresponding Transcript Cluster ID Nos selected from AKT1, PIK3CA, PTEN, NTRK3 and ALK;   (c) using a computer to classify said sample of thyroid tissue as benign or normal by applying an algorithm to said expression levels from (a), wherein said classifying is based on an expression level in said sample of thyroid tissue of said at least three genes or corresponding Transcript Cluster ID Nos selected from AKT1, PIK3CA, PTEN, NTRK3 and ALK;   (d) generating an electronic report upon classifying said sample of thyroid tissue, which electronic report is indicative of said sample of thyroid tissue being benign or normal; and   (e) outputting said electronic report that is indicative of said sample of thyroid tissue being benign or normal.   
     
     
         2 . The method of  claim 1 , wherein said three or more gene expression products comprise four or more gene expression products, and wherein said at least three genes or corresponding Transcript Cluster ID Nos selected from AKT1, PIK3CA, PTEN, NTRK3 and ALK comprise at least four genes or corresponding Transcript Cluster ID Nos selected from AKT1, PIK3CA, PTEN, NTRK3 and ALK. 
     
     
         3 . The method of  claim 1 , further comprising assaying expression levels of BRAF, RET, RAS, CTNNB1, TSHR, PPARGC1A, MET, PTH, KRT7, CALCA, TP53, AKT1, PIK3CA, PTEN, NTRK3, ALK, EIF1AY, NTRK2, IGF2BP2, SLC5A8 and TTF1 in said sample of thyroid tissue obtained from said subject and wherein said at least three genes or corresponding Transcript Cluster ID Nos selected from AKT1, PIK3CA, PTEN, NTRK3 and ALK comprise BRAF, RET, RAS, CTNNB1, TSHR, PPARGC1A, MET, PTH, KRT7, CALCA, TP53, AKT1, PIK3CA, PTEN, NTRK3, ALK, EIF1AY, NTRK2, IGF2BP2, SLC5A8 and TTF1 or corresponding Transcript Cluster ID Nos. 
     
     
         4 . The method of  claim 1 , wherein said algorithm is trained with a training set comprising a training sample obtained by fine needle aspiration. 
     
     
         5 . The method of  claim 1 , wherein said algorithm is trained with a training set comprising a training sample obtained by fine needle aspiration and a training sample obtained by surgical biopsy. 
     
     
         6 . The method of  claim 1 , wherein said algorithm is trained with a training set comprising at least 200 training samples of thyroid tissue. 
     
     
         7 . The method of  claim 1 , wherein said algorithm is trained by correlating a gene expression profile of a first sub-type of thyroid tissue with a gene expression profile of at least six sub-types of thyroid tissue that are not of said first sub-type. 
     
     
         8 . The method of  claim 1 , wherein said algorithm is trained with a training set comprising a training sample with a pathology selected from the group consisting of: metastatic melanoma, metastatic renal carcinoma, metastatic breast carcinoma, and metastatic B cell lymphoma. 
     
     
         9 . The method of  claim 8 , wherein said pathology of said training sample is metastatic B cell lymphoma. 
     
     
         10 . The method of  claim 1 , wherein said sample of thyroid tissue has not previously received a definitive diagnosis. 
     
     
         11 . The method of  claim 1 , wherein said sample of thyroid tissue is subjected to cytological testing that indicates the sample is ambiguous or suspicious. 
     
     
         12 . The method of  claim 1 , wherein said sample of thyroid tissue is a formalin-fixed-paraffin-embedded sample. 
     
     
         13 . The method of  claim 1 , wherein said algorithm has a specificity greater than 70%. 
     
     
         14 . The method of  claim 1 , wherein said algorithm has a negative predictive value (NPV) of at least 95%. 
     
     
         15 . The method of  claim 1 , wherein said algorithm has an overall classification error rate of less than 6%. 
     
     
         16 . The method of  claim 1 , 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. 
     
     
         17 . The method of  claim 1 , wherein said sample of thyroid tissue comprises thyroid cells and said sample of thyroid tissue is obtained by fine needle aspiration. 
     
     
         18 . The method of  claim 1 , wherein said gene expression products comprise RNA expression products. 
     
     
         19 . The method of  claim 18 , wherein a level of said RNA expression products is measured by microarray, SAGE, blotting, RT-PCR, quantitative PCR, or sequencing. 
     
     
         20 . The method of  claim 1 , wherein said algorithm is trained with at least three training samples, each of which exhibits a different malignant pathology. 
     
     
         21 . The method of  claim 20 , wherein each of said at least three training samples is obtained from a different tissue type and wherein said different tissue type is selected from the following tissue types: follicular carcinoma, lymphocytic thyroiditis, follicular variant papillary thyroid carcinoma, papillary thyroid carcinoma, nodular hyperplasia, medullary thyroid carcinoma, Hurthle cell carcinoma, Hurthle cell adenoma, anaplastic thyroid carcinoma, metastatic melanoma, metastatic renal carcinoma, metastatic breast carcinoma, parathyroid, and metastatic B cell lymphoma. 
     
     
         22 . The method of  claim 1 , wherein said electronic report is sent to a party via a communication medium. 
     
     
         23 . The method of  claim 1 , wherein said electronic report is presented on a computer screen. 
     
     
         24 . The method of  claim 1 , wherein said electronic report is presented as a paper record.