# US 8,541,170 B2 · Methods and compositions of molecular profiling for disease diagnostics > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 8,541,170 B2 - Document ID: US8541170B2 - Status: Granted - Kind: B2 - Application number: 12/592,065 - Filed: 2009-11-17 - Published: 2010-05-27 - Granted: 2013-09-24 - Earliest claimed: 2008-11-17 - Assignees: Veracyte, Inc. - Applicants: Giulia C. Kennedy; Bonnie H. Anderson; Darya I. Chudova; Eric T. Wang; Hui Wang; Moraima Pagan; Nusrat Rabbee; Jonathan I. Wilde - Inventors: Giulia C. Kennedy; Bonnie H. Anderson; Darya I. Chudova; Eric T. Wang; Hui Wang; Moraima Pagan; Nusrat Rabbee; Jonathan I. Wilde - Web page: https://patentgrep.com/patent/US8541170B2 ## CPC - C12Q 1/6886: for cancer (immunoassay for cancer G01N33/575) - A61B 10/0096: {Casings for storing test samples (A61B10/0038, A61B10/0045 take precedence; preservation of living parts of the human or animal body A01N1/10; containers for retaining a material to be analysed B01L3/50; containers for enzymology or microbiology C12M1/16; swab-sampler being part of enzymology or microbiology container C12M1/30)} - A61B 50/30: Containers specially adapted for packaging, protecting, dispensing, collecting or disposing of surgical or diagnostic appliances or instruments (for wound clamps A61B17/10; for ligaturing clips A61B17/122; packages or dispensers for surgical gloves or finger-stalls A61B42/40) - G01N 33/575: for cancer - G01N 33/57557: {of other specific parts of the body, e.g. brain} - G06Q 99/00: Subject matter not provided for in other groups of this subclass - G16B 25/00: ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression - G16B 25/10: Gene or protein expression profiling; Expression-ratio estimation or normalisation - A61B 10/0283: {with vacuum aspiration, e.g. caused by retractable plunger or by connected syringe (A61B10/025, A61B10/0266 take precedence)} - C12Q 2600/112: Disease subtyping, staging or classification - C12Q 2600/158: Expression markers - C12Q 2600/178: miRNA, siRNA or ncRNA - C12Q: MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS (immunoassay G01N33/53); COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY ## 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. ## Claims ### Claim 1 A method of detecting a thyroid condition in a patient comprising the steps of: a. obtaining a sample of thyroid tissue from said patient; b. assaying a level of expression of two or more gene expression products in said sample of thyroid tissue; c. classifying said sample of thyroid tissue as benign or normal by applying an algorithm to the expression level data from step b, wherein said algorithm produces a negative predictive value (NPV) of at least 95%, wherein said classifying is based on a level of expression in said sample of thyroid tissue of at least two of the following genes or corresponding Transcript Cluster ID Nos: ARSG, FREM2, GIMAP2, PIGN, and PLA2G16; and d. treating said patient on the basis of step (c) 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. ### Claim 2 (depends on 1) The method of claim 1, wherein said algorithm is trained with a training set comprising a training sample obtained by fine needle aspiration. ### Claim 3 (depends on 1) 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. ### Claim 4 (depends on 1) The method of claim 1, wherein said sample of thyroid tissue has not previously received a definitive diagnosis. ### Claim 5 (depends on 1) The method of claim 1, wherein said sample of thyroid tissue is subjected to cytological testing that indicates the sample is ambiguous or suspicious. ### Claim 6 (depends on 1) The method of claim 1, wherein said sample is a formalin-fixed-paraffin-embedded sample. ### Claim 7 (depends on 1) The method of claim 1, wherein said pathology of said training sample is metastatic B cell lymphoma. ### Claim 8 (depends on 1) The method of claim 1, wherein said method has a specificity greater than 70%. ### Claim 9 (depends on 1) The method of claim 1, wherein an overall classification error rate of said method is less than 6%. ### Claim 10 (depends on 1) 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. ### Claim 11 (depends on 1) 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. ### Claim 12 (depends on 1) The method of claim 1, wherein said gene expression product is an RNA expression product. ### Claim 13 (depends on 12) The method of claim 12, wherein a level of said RNA expression product is measured by microarray, SAGE, blotting, RT-PCR, quantitative PCR, or sequencing. ### Claim 14 (depends on 1) The method of claim 1, wherein said algorithm is trained with at least three training samples, each of which exhibits a different malignant pathology. ### Claim 15 A method of evaluating a sample of thyroid tissue from a patient comprising the steps of: a. obtaining said sample of thyroid tissue from said patient; b. assaying an expression level for two or more gene expression products in said sample of thyroid tissue to generate test data c. correlating said test data with gene expression data obtained from at least 200 training samples of thyroid tissue; d. based on said correlating, (i) identifying said sample of thyroid tissue as malignant, (ii) identifying said sample of thyroid tissue as benign, (iii) identifying said sample of thyroid tissue as non-cancerous, (iv) identifying said sample of thyroid tissue as non-malignant, or (v) identifying said sample of thyroid tissue as normal, wherein said correlating uses gene expression data of at least two of the following genes or corresponding Transcript Cluster ID Nos: ARSG, FREM2, GIMAP2, PIGN, and PLA2G16; and e. treating said patient on the basis of step (d), wherein one or more of said at least 200 training samples of thyroid tissues have a pathology selected from the group consisting of: metastatic melanoma, metastatic renal carcinoma, metastatic breast carcinoma, and metastatic B cell lymphoma. ### Claim 16 (depends on 15) The method of claim 15, wherein said correlating comprises applying an algorithm that was trained with said gene expression data obtained from at least 200 training samples of thyroid tissue. ### Claim 17 (depends on 16) The method of claim 16, wherein said at least 200 training samples of thyroid tissue is at least 400 training samples of thyroid tissue. ### Claim 18 (depends on 16) The method of claim 16, 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. ### Claim 19 (depends on 15) The method of claim 15, 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. ### Claim 20 (depends on 15) The method of claim 15, wherein said sample of thyroid tissue is obtained by fine needle aspiration. ### Claim 21 (depends on 20) The method of claim 20, wherein said sample of thyroid tissue is subjected to cytological testing, a result of which is indeterminate. ### Claim 22 (depends on 15) The method of claim 15, wherein said gene expression product is an RNA gene expression product. ### Claim 23 (depends on 16) The method of claim 16, wherein said algorithm is trained with a training set comprising a training sample of thyroid tissue obtained by fine needle aspiration. ### Claim 24 (depends on 16) The method of claim 16, 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. ### Claim 25 (depends on 20) The method of claim 20, wherein said sample of thyroid tissue is subjected to cytological testing that indicates the sample is ambiguous or suspicious. ### Claim 26 A method of evaluating a sample of thyroid tissue from a patient comprising the steps of: a. obtaining said sample of thyroid tissue from said patient; b. assaying an expression level for two or more gene expression products in said sample of thyroid tissue; c. applying an algorithm to said expression level of step (b), wherein said algorithm correlates the expression level of step (b) with expression data from at least three samples of thyroid tissue, wherein each of said at least three samples of thyroid tissue 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; d. using said algorithm, (i) identifying said sample of thyroid tissue as malignant, (ii) identifying said sample of thyroid tissue as benign, (iii) identifying said sample of thyroid tissue as non-cancerous, (iv) identifying said sample of thyroid tissue as non-malignant, or (v) identifying said sample of thyroid tissue as normal, wherein said expression data include data of at least two of the following genes or corresponding Transcript Cluster ID Nos: ARSG, FREM2, GIMAP2, PIGN, and PLA2G16; and e. treating said patient on the basis of step (d), wherein said algorithm is a trained algorithm, trained by said expression data from at least three samples of thyroid tissue, wherein each of said samples of thyroid tissue 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, or metastatic B cell lymphoma, and further wherein said training samples of thyroid tissue comprise three or more samples of the following types: metastatic melanoma, metastatic renal carcinoma, metastatic breast carcinoma, metastatic B cell lymphoma, or parathyroid. ### Claim 27 (depends on 26) The method of claim 26, wherein said trained algorithm applies a classifier that was trained with gene expression data obtained from at least 200 training samples of thyroid tissue. ### Claim 28 (depends on 26) The method of claim 26, wherein said training samples of thyroid tissue further comprise three or more samples of the following types: follicular carcinoma, follicular variant papillary thyroid carcinoma, papillary thyroid carcinoma, medullary thyroid carcinoma, Hurthle cell carcinoma, or anaplastic thyroid carcinoma. ### Claim 29 (depends on 26) The method of claim 26, wherein said training samples of thyroid tissue further comprise three or more samples of the following types: parathyroid, lymphocytic thyroiditis, nodular hyperplasia, or Hurthle cell adenoma. ### Claim 30 (depends on 26) The method of claim 26, wherein said training samples of thyroid tissue comprise metastatic B cell lymphoma. ### Claim 31 (depends on 28) The method of claim 28, wherein said training samples of thyroid tissue comprise Hurthle cell carcinoma. ### Claim 32 (depends on 28) The method of claim 28, wherein said training samples of thyroid tissue comprise follicular carcinoma and papillary thyroid carcinoma. ### Claim 33 (depends on 29) The method of claim 29, wherein said training samples of thyroid tissue comprise Hurthle cell adenoma. ### Claim 34 (depends on 26) The method of claim 26, 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. ### Claim 35 (depends on 26) The method of claim 26, wherein said sample of thyroid tissue is obtained by fine needle aspiration. ### Claim 36 (depends on 35) The method of claim 35, wherein said sample of thyroid tissue is subjected to cytological testing, the results of which are indeterminate. ### Claim 37 (depends on 26) The method of claim 26, wherein the gene expression product is an RNA expression product. ### Claim 38 (depends on 26) The method of claim 26, wherein said algorithm is trained with a training set comprising a training sample of thyroid tissue obtained by fine needle aspiration. ### Claim 39 (depends on 26) The method of claim 26, wherein said algorithm is trained with a training set comprising a training sample of thyroid tissue obtained by fine needle aspiration and a training sample of thyroid tissue obtained by surgical biopsy. ### Claim 40 (depends on 26) The method of claim 26, wherein said trained algorithm is trained using gene expression data from training samples of normal thyroid tissue. ### Claim 41 (depends on 26) The method of claim 26, wherein said sample of thyroid tissue is subjected to cytological testing that indicates the sample is ambiguous or suspicious. ### Claim 42 A method of evaluating a sample of thyroid tissue from a patient comprising the steps of: a. obtaining said sample of thyroid tissue from said patient; b. assaying an expression level for two or more gene expression products in said sample of thyroid tissue to generate test data; c. applying an algorithm to said expression level of step (b), wherein said algorithm correlates the expression level of step (b) with expression data obtained from a plurality of samples, wherein said plurality of samples comprises a sample obtained by surgical biopsy of thyroid tissue and a sample obtained by fine needle aspiration of thyroid tissue; d. based on said correlating, (i) identifying said sample of thyroid tissue as malignant, (ii) identifying said sample of thyroid tissue as benign, (iii) identifying said sample of thyroid tissue as non-cancerous, (iv) identifying said sample of thyroid tissue as non-malignant, or (v) identifying said sample of thyroid tissue as normal, wherein said correlating uses gene expression data of at least two of the following genes or corresponding Transcript Cluster ID Nos: ARSG, FREM2, GIMAP2, PIGN, and PLA2G16; and e. treating said patient on the basis of step (d), wherein said algorithm is a trained algorithm trained by said expression data obtained from said plurality of samples, and further wherein one or more of said plurality of samples has a pathology selected from the group consisting of: metastatic melanoma, metastatic renal carcinoma, metastatic breast carcinoma, and metastatic B cell lymphoma. ### Claim 43 (depends on 42) The method of claim 42, wherein said plurality of samples comprises at least 200 training samples. ### Claim 44 (depends on 42) The method of claim 42, wherein said plurality of samples comprises at least 400 training samples. ### Claim 45 (depends on 42) The method of claim 42, wherein said trained 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. ### Claim 46 (depends on 42) The method of claim 42, 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. ### Claim 47 (depends on 42) The method of claim 42, wherein said sample of thyroid tissue is subjected to cytological testing, a result of which is indeterminate. ### Claim 48 (depends on 42) The method of claim 42, wherein said gene expression product is an RNA gene expression product. ### Claim 49 (depends on 42) The method of claim 42, wherein said sample of thyroid tissue is subjected to cytological testing that indicates the sample is ambiguous or suspicious. ### Claim 50 (depends on 15, 26) The method of any one of claims 15, 26, and 42, wherein results from said identifying are sent to a party via a communication medium. ### Claim 51 A method of detecting a thyroid condition in a patient comprising the steps of: a. obtaining a sample of thyroid tissue from said patient; b. assaying a level of expression of two or more gene expression products in said sample of thyroid tissue, wherein the two or more gene assayed include at least two of the following genes or corresponding Transcript Cluster ID Nos: ARSG, FREM2, GIMAP2, PIGN, and PLA2G 16; c. classifying said sample of thyroid tissue as benign by applying an algorithm to data from step b, wherein said algorithm correlates said data from step b with expression data obtained from a plurality of samples, wherein said plurality of samples comprises a sample with a pathology that is a metastatic cancer from a non-thyroid organ, wherein said expression data include data of at least two of the following genes or corresponding Transcript Cluster ID Nos: ARSG, FREM2, GIMAP2, PIGN, and PLA2G16; and d. treating said patient on the basis of step (c). ### Claim 52 (depends on 51) The method of claim 51, wherein said algorithm is a trained algorithm trained with expression data obtained from 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. ### Claim 53 (depends on 52) The method of claim 52, wherein said pathology of said training sample is metastatic B cell lymphoma. ### Claim 54 (depends on 1, 51) The method of any one of claim 1 and 51, wherein results from said classifying are sent to a party via a communication medium. ### Claim 55 A method of evaluating a sample of thyroid tissue from a patient comprising the steps of: a. obtaining said sample of thyroid tissue from said patient; b. assaying an expression level for two or more gene expression products in said sample of thyroid tissue; c. applying an algorithm to said expression level of step (b), wherein said algorithm correlates the expression level of step (b) with expression data from at least three samples of thyroid tissue, wherein each of said at least three samples of thyroid tissue 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; d. using said algorithm, (i) identifying said sample of thyroid tissue as malignant, (ii) identifying said sample of thyroid tissue as benign, (iii) identifying said sample of thyroid tissue as non-cancerous, (iv) identifying said sample of thyroid tissue as non-malignant, or (v) identifying said sample of thyroid tissue as normal, wherein said expression data include data of at least two of the following genes or corresponding Transcript Cluster ID Nos: ARSG, FREM2, GIMAP2, PIGN, and PLA2G16; and e. treating said patient on the basis of step (d), wherein the three samples of thyroid tissue are the following tissue types: follicular carcinoma, papillary thyroid carcinoma, and metastatic B cell lymphoma. ### Claim 56 (depends on 55) The method of claim 55, 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. ### Claim 57 (depends on 55) The method of claim 55, wherein said sample of thyroid tissue comprises thyroid cells and said sample of thyroid tissue is obtained by fine needle aspiration. ### Claim 58 (depends on 55) The method of claim 55, wherein said two or more gene expression products are RNA expression products. ### Claim 59 (depends on 55) The method of claim 55, wherein said method has a specificity greater than 70%. ### Claim 60 (depends on 55) The method of claim 55, wherein said method has a nominal negative predictive value (NPV) greater than 95%.