# Patent

## Title
Compositions and methods for classifying thyroid nodule disease

## Abstract

A system for classifying thyroid nodule tissue as malignant or benign is provided that is based on the identification of sets of gene transcripts, which are characterized in that changes in expression of each gene transcript within a set of gene transcripts can be correlated to with either malignant or benign thyroid nodule disease. The thyroid classification system provides for sets of “thyroid classifying” target sequences and further provides for combinations of polynucleotide probes and primers derived there from. These combinations of polynucleotide probes can be provided in solution or as an array. The combination of probes and the arrays can be used for diagnosis. The invention further provides further methods of classifying thyroid nodule tissue.

## Background
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation application of U.S. patent application Ser. No. 13/254,571, filed Nov. 28, 2011, which is a National Stage Entry of International Application No. PCT/CA2010/000266, filed Mar. 3, 2010, which claims the benefit of U.S. Provisional Patent Application No. 61/157,552, filed Mar. 4, 2009, each of which is entirely incorporated herein by reference.
SEQUENCE LISTING
The instant application contains a Sequence Listing which has been submitted in ASCII format via EFS-Web and is hereby incorporated by reference in its entirety. Said ASCII copy, created on Sep. 1, 2011, is named 1004001U.txt and is 290,717 bytes in size.


FIELD OF THE INVENTION
This invention relates to the field of diagnostics and in particular to systems and methods for diagnosis of thyroid cancer.
BACKGROUND
Thyroid nodule disease is a common clinical problem, found in 4-7% of the living adult population in North America. The occurrence of thyroid nodules increases with age; autopsies reveal the presence of thyroid nodules in 50% of the population. It is estimated that, at 80 years old, 90% of the population will have at least one thyroid nodule. However, the vast majority of solitary thyroid nodules are benign in nature, and would require no further treatment if a correct diagnosis could be obtained without surgery.
A number of techniques can be used to diagnose thyroid conditions, including radioactive thyroid scans, ultrasound, thyroid hormone level and thyroglobulin measurements, and fine needle aspiration biopsy (FNAB). Thyroid scans do not effectively distinguish benign and malignant conditions, however, and are typically used in conjunction with other techniques. Similarly, ultrasound may provide information suggestive of either benign or malignant conditions, but cannot definitively diagnose thyroid status. Measurements of thyroid hormone level and thyroglobulin can be informative, but are nondiagnostic by themselves.
Thyroid FNAB is the only non-surgical method which can by itself differentiate malignant and benign nodules. More than 300,000 fine needle aspiration biopsies (FNAB) of the thyroid are performed annually in the US and evaluated using cytology. The primary purpose of FNAB is to distinguish thyroid nodules that require immediate surgical intervention (e.g., total thyroidectomy in the case of a diagnosis of malignant disease) from nodules that can be treated effectively with less aggressive clinical approaches.
In FNAB, samples of thyroid cells are obtained by inserting a needle into the thyroid and aspirating cells into a syringe. Usually, 2 to 4 aspirations are made from different sites in each nodule. The cells are mounted on a slide (for each aspiration, 2 to 4 slides are prepared), stained, and examined. The sample is then classified as nondiagnostic (indeterminate), benign, suspicious or malignant. Most samples are categorized as benign.
FNAB can be used to successfully diagnose papillary carcinoma, medullary carcinoma, anaplastic carcinoma, thyroid lymphoma and metastases to the thyroid from other sites. Papillary carcinoma accounts for  ̃60-70% and the follicular variant of papillary carcinoma accounts for  ̃6% of thyroid cancers. These well differentiated thyroid cancers are usually curable, but they must be found first.
Especially problematic are cases considered ‘suspicious’, ‘inadequate’ or ‘indeterminate’ by cytological diagnosis of FNAB samples. These patients are invariably triaged by invasive surgery, which has a significant morbidity. Overtreatment with total-thyroidectomy frequently occurs as a result; it is estimated that less than 25% of patients with such diagnoses in fact have cancer that warrants removal of the thyroid gland. Approximately 5-10% of samples are classified as nondiagnostic by FNAB. In those cases, FNAB can be repeated; however, only half of repeat biopsies yield a diagnostic result. For the remaining patients, further testing and surgery may be required. Due to the fear of cancer, invasive surgery is chosen, but in most cases is unnecessary. Approximately 10-20% of samples are classified as suspicious by FNAB. Of these, approximately 25% will ultimately prove to be malignant after surgery, typically exhibiting follicular or Hurthle cell cancers, which cannot be diagnosed by FNAB. Follicular carcinoma, which accounts for  ̃12-15% of all thyroid cancers and the less prevalent Hurthle cell carcinoma cannot be distinguished cytologically from benign follicular or Hurthle cell adenomas. Therefore, most patients with suspicious biopsies are typically subjected to surgery, when in fact  ̃75% of these patients do not have malignant disease.
A contributing factor to the difficulties with current FNAB cytology-based diagnoses is the variability between different pathologists and cytopathologists in diagnostic agreement between cytological analysis and final histological review, ranging from 40%-90%. The overall accuracy of diagnoses using only FNAB ranges from 60% to >90%, and is dependent on the expertise of the cytologist and whether or not ‘suspicious’ or ‘indeterminate’ diagnostic categories are included in the reported accuracy of the study. When factoring the cytology diagnostic categories of ‘suspicious’ or ‘indeterminate’, the literature shows that the overall specificity of FNAB cytology for diagnosis of malignant disease decreases dramatically to <60% with false-positive rates of .about.40%. Patients with malignant thyroid disease are invariably treated by total removal of the tumor and all of the thyroid gland followed by radioactive iodine treatment, whereas benign thyroid disease can be treated less aggressively with a near-total thyroidectomy, partial thyroidectomy (e.g., ‘lobectomy’) or a watchful-waiting approach (e.g., observation without surgical intervention). As FNAB and cytology cannot reliably distinguish malignant from benign disease in cases with ‘suspicious’ cytological findings, such as occurs in the case of follicular and Hurthle cell lesions, these patients are typically all treated as if they were diagnosed with malignant disease (i.e., with aggressive surgery). Since only a small fraction of these patients in fact have malignant disease, over-treatment of thyroid nodule disease patients occurs frequently, with significant consequences for patients. As such, many unnecessary thyroidectomies are therefore performed in patients with what ultimately proves to be benign or non-neoplastic thyroid nodule disease when an FNAB sample is deemed as ‘suspicious’ or ‘indeterminate.’ These deficiencies negatively impact patient outcomes, long-term well-being and healthcare efficiencies.
Use of molecular analyses has the potential to increase the sensitivity, specificity and/or overall accuracy of thyroid diagnoses as compared to FNAB cytology alone. In the pre-operative setting, such a result would likely reduce the number of unnecessary surgeries for patients without malignant disease and avoid inadvertent undertreatment of highly curable thyroid cancers resulting from misdiagnoses. In addition, an accurate molecular based diagnosis as an adjunct assay to established pathological review diagnosis of thyroidectomy specimens in the post-operative setting could be beneficial by increasing the confidence of pathologists in establishing a definitive diagnosis for cancer that would likely influence the course of treatment and management of definitive malignant disease. However, prior attempts at using gene expression profiling to develop diagnostic gene expression signatures and identify mRNA biomarkers useful for the differential diagnosis of thyroid nodule disease have not yet yielded new clinical tools to improve the diagnosis of malignant from benign thyroid nodule disease from clinical specimens. Most of these efforts and those of protein immunohistochemistry studies focused on the protein-encoding genome. However, the transcriptome is inherently more complex than this, given that <2% of the genome encodes for protein and recent studies that have shown that more than 90% of the genome undergoes transcription yielding millions of non-coding RNA transcripts that serve regulatory roles over the protein-encoding transcriptome. So, gene-level analysis may provide only a rough estimate of diagnosis as it cannot capture the full differences between the genomes of malignant and benign thyroid nodule disease (e.g., alternative gene splicing, non-coding and functional RNA expression). Recent efforts to validate a 3-gene signature for diagnosis of thyroid nodule disease FNAB with a QRT-PCR approach report a low diagnostic accuracy in a large validation study (see Sibru et al., citation #14). Other prior attempts using gene-biased microarrays showed similar performance characteristics with low diagnostic accuracy for gene-based signatures (see Jiang et al., US 2007/0037186 A1). For example, Jiang et al., (US 2007/0037186 A1) disclosed a 4-gene QRT-PCR panel with a sensitivity of 92% but a specificity of just 61%. As a result, diagnoses using these provide results little better than FNAB cytology. In addition, other prior attempts utilize samples which are generally not available in the clinical setting. In particular, in the majority of clinical settings fresh tissue is unavailable. Formalin fixation is an essential part of the routine processing of tissue samples because this fixative best preserves the architecture of the tissue and cellular morphology, allowing pathologists enough definition to ascertain a diagnosis. Fresh or frozen unfixed tissue is suboptimal for viewing key details that pathologists use to differentially diagnose disease (e.g., benign vs cancer). For example, US 2008/0145841 and WO2006/127537 describes a thyroid fine needle aspiration molecular assay using fresh frozen samples. WO 2006/127537 showed a best result of 92% specificity and 76% sensitivity (see Table 12, page 98) and US 2008/0145841 showed an accuracy of 87.1% (see para [0127]).
Another possible reason why previous efforts aimed to developing molecular based classification schemes for thyroid nodule disease have not led to routine clinical assays relates to the technical feasibility of administering a molecular test. Typically, thyroid nodule fine-needle aspirate biopsies provide only a small amount of cells and therefore only minute yields of extractable nucleic acids or proteins that may be insufficient for standard molecular assays. In addition, many FNABs are further processed to prepare cell blocks or cell pellets made by centrifuging a fine-needle aspirate, followed by fixation similar to an FFPE block. After surgical resection, standard pathology practices require detailed post-operative evaluation of thyroidectomy specimens; this is especially important to establish a definitive diagnosis of cancer in cases where the FNAB cytology results were only ‘suspicious’ or indeterminate for the presence of cancer. Both of these procedures involve formalin-fixation and paraffin embedding, as this procedure best preserves the morphology and definition of the cells (in comparison to fresh or frozen preparates) favored by pathologists for microscopic evaluation but problematic for many nucleic acid molecular assays due to fragmentation of nucleic acids by formalin-fixation and paraffin embedding. Therefore, small amounts of sample and the use of fixatives are two additional technical impediments that must be overcome in order to apply molecular analyses of nucleic acids in routine clinical settings.
This background information is provided for the purpose of making known information believed by the applicant to be of possible relevance to the present invention. No admission is necessarily intended, nor should be construed, that any of the preceding information constitutes prior art against the present invention.
SUMMARY OF THE INVENTION
An object of the present invention is to provide systems and methods for expression-based classification of thyroid nodule disease from patient specimens. In accordance with one aspect of the present invention, there is provided a system for expression-based classification of thyroid tissue as malignant or benign, said system comprising one or more polynucleotides, each of said polynucleotides capable of specifically hybridizing to a RNA transcript comprising the sequence as set forth in any one of SEQ ID NOs: 1 to 584 or the complement thereof.
In accordance with another aspect of the present invention, there is provided a nucleic acid array for expression-based classification of thyroid tissue as malignant or benign, said array comprising at least ten probes immobilized on a solid support, each of said probes being between about 15 and about 500 nucleotides in length, each of said probes being derived from a sequence corresponding to, or complementary to, a RNA transcript comprising the sequence as set forth in any one of SEQ ID NOs: 1 to 584, or a portion of said transcript.
In accordance with another aspect of the present invention, there is provided a method of classifying a thyroid nodule in a subject as malignant or benign, said method comprising: (a) determining the expression level of one or more transcripts in a test sample obtained from said subject to provide an expression pattern profile, each of said transcripts comprising a sequence as set forth in any one of SEQ ID NOs:. 1 to 584, and (c) comparing said expression pattern profile with a reference expression pattern profile.
In accordance with another aspect of the present invention, there is provided a kit for characterizing the expression of one or more nucleic acid sequences depicted in SEQ ID NOs: 1-584 comprising one or more nucleic acids selected from:

    
    
        (a) a nucleic acid depicted in any of SEQ ID NOs: 1-584;
        (b) an RNA form of any of the nucleic acids depicted in SEQ ID NOs: 1-584;
        (c) a peptide nucleic acid form of any of the nucleic acids depicted in SEQ ID NOs: 1-584;
        (d) a nucleic acid comprising at least 20 consecutive bases of any of (a-c);
        (e) a nucleic acid comprising at least 25 consecutive bases having at least 90% sequence identity to any of (a-c); or
        (f) a complement to any of (a-e); and
        optionally instructions for correlating the expression level of said one or more nucleic acid sequences with the disease state of thyroid tissue.
    
    


In accordance with another aspect of the present invention, there is provided an array of probe nucleic acids certified for use in classifying thyroid disease status, wherein said array comprises at least two different probe nucleic acids that specifically hybridize to corresponding different target nucleic acids depicted in any one of SEQ ID NOs: 1-584, an RNA form thereof, or a complement to either thereof.
In accordance with another aspect of the present invention, there is provided an array of probe nucleic acids certified for use in classifying thyroid disease status, wherein said array comprises at least two different probe nucleic acids that specifically hybridize to corresponding different target nucleic acids depicted in any one of SEQ ID NOs: 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10, an RNA form thereof, or a complement to either thereof.
In accordance with another aspect of the present invention, there is provided an array of probe nucleic acids certified for use in classifying thyroid disease status, wherein said array comprises at least two different probe nucleic acids that specifically hybridize to corresponding different target nucleic acids depicted in any one of SEQ ID NOs: 1, 10, 11, 12, 13, 14, and 15, an RNA form thereof, or a complement to either thereof.
In accordance with another aspect of the present invention, there is provided a device for classifying a biological sample from a thyroid gland as malignant or benign, the device comprising means for measuring the expression level of one or more transcripts, each of said transcripts comprising a sequence as set forth in any one of SEQ ID NOs: 1 to 584; means for correlating the expression level with a classification of thyroid disease status; and means for outputting the thyroid disease status.
In accordance with another aspect of the present invention, there is provided a computer-readable medium comprising one or more digitally-encoded expression pattern profiles representative of the level of expression of one or more transcripts, each of said transcripts comprising a sequence as set forth in any one of SEQ ID NOs: 1 to 584, each of said one or more expression pattern profiles being associated with a value wherein each of said values is correlated with the presence of malignant or benign tissue in a thyroid gland sample.
In accordance with another aspect of the present invention, there is provided a system for expression-based classification of thyroid tissue as malignant or benign, said system comprising one or more polynucleotides, each of said polynucleotides capable of specifically hybridizing to a RNA transcript comprising the non-coding sequence as set forth in any one of SEQ ID NOs: 1-4, 6-15, 17-31, 33-43, 47, 49-55, 57-62, 64, 65, 67-71, 73-78, 80, 84, 85, 88, 90-95, 101, 102, 104, 105, 107, 108, 111-113, 116-118, 122-125, 128, 129, 131-133, 135-137, 139, 140-144, 148-150, 152-156, 158, 162-164, 166-171, 173, 175, 176, 177, 179, 185-187, 189, 191-195, 197, 201, 204, 208-217, 220, 221, 224-229, 231-233, 235-241, 245, 247, 250-254, 256-259, 261, 263-267, 269-273, 276, 279, 283-293, 299, 301, 303, 304-306, 308, 309, 312, 313, 315-323, 325, 327, 328, 329, 331-335, 337, 343, 345-353, 355, 358, 360-363, 365, 367, 370-376, 378, 381-384, 389-392, 396, 399-402, 404, 405, 410-414, 418, 420-424, 426-431, 434, 435, 437, 438, 440, 444-449, 451-456, 458, 459, 460, 462, 463-473, 475, 476, 478, 480, 481, 485-488, 490-498, 500-503, 505, 507, 509, 511, 512, 515, 516, 519, 520, 522, 523, 525, 526, 528-532, 534, 535, 538, 541, 542, 544, 547-549, 550-553, 558, 561, 562, 564, 566, 567, 569, 571-573, 575, 576, 579 and 581-584.



BRIEF DESCRIPTION OF THE DRAWINGS
These and other features of the invention will become more apparent in the following detailed description in which reference is made to the appended drawings.
FIG. 1 is a pie chart that depicts the annotations of the 584 selected RNAs identified as differentially expressed in the training subset. Note that only 30% correspond to canonical exons of that overlap the translated coding sequences of genes while more than 67% correspond to non-canonical expressed transcripts (i.e., intronic, antisense, promoter and extra-genic RNA sequences) that are largely the non-coding sequences of the transcriptome. The labels in the pie chart indicate the position of the differentially expressed probes relative to the nearest annotated gene. CDS—RNA overlaps with the coding sequence translations of mRNAs in RefSeq or GenBank databases, NC—‘non-coding’ RNA does not overlap with the CDS.
FIG. 2A depicts box plots showing interquartile range and distribution of “POP” scores for thyroid nodule disease benign and malignant sample groups using a 10-RNA metagene to derive patient outcome predictor scores normalized on a data range of 0-100 points. FIG. 2B depicts box plots showing interquartile range and distribution of ‘POP’ scores for thyroid nodule disease benign and malignant sample groups using a 6-RNA metagene to derive patient outcome predictor scores normalized on a data range of 0-100 points. Box plots for specimens definitively diagnosed by original FNAB cytology and those where FNAB cytology was indeterminate are depicted separately. Differences in POP scores between pathology review diagnosed benign and malignant thyroid nodule disease groups were highly significant as evaluated by t-tests for significance as indicated.
FIG. 3A depicts a receiver-operator curve (ROC) of the POP scores derived from the 10-RNA metagene. FIG. 3B depicts a receiver-operator curve (ROC) of the POP scores derived from the 6-RNA metagene. The area-under-the curves and their confidence intervals are indicated below the ROC curve and show that the POP scores are excellent discriminators of malignant and benign disease.



DETAILED DESCRIPTION OF THE INVENTION
The present invention provides systems and methods for classifying thyroid tissue from a subject as malignant or benign, which allows for the diagnosis of thyroid cancer in the subject. The systems and methods are based on the identification of expressed transcripts that are differentially expressed in malignant thyroid nodule disease (i.e., cancer) relative to benign thyroid nodule disease conditions. These expressed transcripts can be considered as a library which can be used as a resource for the identification of sets of specific target sequences (“thyroid classification sets”), which may represent the entire library of expressed transcripts or a subset of the library and the detection of which is indicative of the status of the thyroid tissue (for example, malignant or benign). The invention further provides for probes capable of detecting these target sequences and primers that are capable of amplifying the target sequences.
In accordance with one embodiment of the invention, the target sequences comprised by the thyroid classification set are sequences based on or derived from the gene transcripts from the library, or a subset thereof. Such sequences are occasionally referred to herein as “probe selection regions” or “PSRs.” In another embodiment of the invention, the target sequences comprised by the thyroid classification set are sequences based on the gene transcripts from the library, or a subset thereof, and include both coding and non-coding sequences.
The methods employ molecular analysis of the expression levels of one or more transcripts corresponding to SEQ ID NOs:1 to 584. Increased relative expression of one or more transcripts in Group I corresponding to the expression products SEQ ID NOs:1-6, 11-13, 16-248 and/or decreased relative expression of one or more transcripts in Group II corresponding to the expression products of SEQ ID NOs: 7-10, 14, 15, 249-584 can be correlated with increased likelihood of malignant thyroid nodule disease. Conversely, increased relative expression of one or more transcripts in Group II and/or decreased relative expression of one or more transcripts in Group I can be correlated with an increased likelihood of benign thyroid nodule disease. Subsets and combinations of these transcripts may be used as described herein. In one embodiment, the systems and methods provide for the molecular analysis of the expression levels of one or more of the target sequences as set forth in SEQ ID NOs: 1-584. Subsets and combinations of these target sequences or probes complementary thereto may be used as described herein.
In one embodiment of the invention, the subset includes non-canonical expressed transcripts.
In one embodiment of the invention, the subset includes a plurality of transcripts, each of the transcripts comprising a non-coding sequence as set forth in any one of SEQ ID NOs: 1-4, 6-15, 17-31, 33-43, 47, 49-55, 57-62, 64, 65, 67-71, 73-78, 80, 84, 85, 88, 90-95, 101, 102, 104, 105, 107, 108, 111-113, 116-118, 122-125, 128, 129, 131-133, 135-137, 139, 140-144, 148-150, 152-156, 158, 162-164, 166-171, 173, 175, 176, 177, 179, 185-187, 189, 191-195, 197, 201, 204, 208-217, 220, 221, 224-229, 231-233, 235-241, 245, 247, 250-254, 256-259, 261, 263-267, 269-273, 276, 279, 283-293, 299, 301, 303, 304-306, 308, 309, 312, 313, 315-323, 325, 327, 328, 329, 331-335, 337, 343, 345-353, 355, 358, 360-363, 365, 367, 370-376, 378, 381-384, 389-392, 396, 399-402, 404, 405, 410-414, 418, 420-424, 426-431, 434, 435, 437, 438, 440, 444-449, 451-456, 458, 459, 460, 462, 463-473, 475, 476, 478, 480, 481, 485-488, 490-498, 500-503, 505, 507, 509, 511, 512, 515, 516, 519, 520, 522, 523, 525, 526, 528-532, 534, 535, 538, 541, 542, 544, 547-549, 550-553, 558, 561, 562, 564, 566, 567, 569, 571-573, 575, 576, 579 and 581-584.
In one embodiment of the invention, the subset includes intronic sequences.
In one embodiment of the invention, the systems and methods provide for the molecular analysis of the expression levels of one or more of the target sequences as set forth in SEQ ID NOs 1 to 10.
In one embodiment of the invention, the systems and methods provide for the molecular analysis of the expression levels of one or more of the target sequences as set forth in SEQ ID NOs: 1, 11, 12, 13, 14 and 15.
Before the present invention is described in further detail, it is to be understood that this invention is not limited to the particular methodology, compositions, articles or machines described, as such methods, compositions, articles or machines can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present invention.
Definitions
Unless defined otherwise or the context clearly dictates otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. In describing the present invention, the following terms will be employed, and are intended to be defined as indicated below.
The term “polynucleotide” as used herein refers to a polymer of greater than one nucleotide in length of ribonucleic acid (RNA), deoxyribonucleic acid (DNA), hybrid RNA/DNA, modified RNA or DNA, or RNA or DNA mimetics, including peptide nucleic acids (PNAs). The polynucleotides may be single- or double-stranded. The term includes polynucleotides composed of naturally-occurring nucleobases, sugars and covalent internucleoside (backbone) linkages as well as polynucleotides having non-naturally-occurring portions which function similarly. Such modified or substituted polynucleotides are well-known in the art and for the purposes of the present invention, are referred to as “analogues.”
“Complementary” or “substantially complementary” refers to the ability to hybridize or base pair between nucleotides or nucleic acids, such as, for instance, between a sensor peptide nucleic acid or polynucleotide and a target polynucleotide. Complementary nucleotides are, generally, A and T (or A and U), or C and G. Two single-stranded polynucleotides or PNAs are said to be substantially complementary when the bases of one strand, optimally aligned and compared and with appropriate insertions or deletions, pair with at least about 80% of the bases of the other strand, usually at least about 90% to 95%, and more preferably from about 98 to 100%.
Alternatively, substantial complementarity exists when a polynucleotide will hybridize under selective hybridization conditions to its complement. Typically, selective hybridization will occur when there is at least about 65% complementarity over a stretch of at least 14 to 25 bases, for example at least about 75%, or at least about 90% complementarity. See, M. Kanehisa Nucleic Acids Res. 12:203 (1984).
“Preferential binding” or “preferential hybridization” refers to the increased propensity of one polynucleotide to bind to its complement in a sample as compared to a noncomplementary polymer in the sample.
Hybridization conditions will typically include salt concentrations of less than about 1M, more usually less than about 500 mM, for example less than about 200 mM. In the case of hybridization between a peptide nucleic acid and a polynucleotide, the hybridization can be done in solutions containing little or no salt. Hybridization temperatures can be as low as 5° C., but are typically greater than 22° C., and more typically greater than about 30° C., for example in excess of about 37° C. Longer fragments may require higher hybridization temperatures for specific hybridization as is known in the art. Other factors may affect the stringency of hybridization, including base composition and length of the complementary strands, presence of organic solvents and extent of base mismatching, and the combination of parameters used is more important than the absolute measure of any one alone. Other hybridization conditions which may be controlled include buffer type and concentration, solution pH, presence and concentration of blocking reagents to decrease background binding such as repeat sequences or blocking protein solutions, detergent type(s) and concentrations, molecules such as polymers which increase the relative concentration of the polynucleotides, metal ion(s) and their concentration(s), chelator(s) and their concentrations, and other conditions known in the art.
“Multiplexing” herein refers to an assay or other analytical method in which multiple analytes can be assayed simultaneously.
A “target sequence” as used herein (also occasionally referred to as a “PSR” or “probe selection region”) refers to a region of the genome against which one or more probes can be designed. As used herein, a probe is any polynucleotide capable of selectively hybridizing to a target sequence or its complement, or to an RNA version of either. A probe may comprise ribonucleotides, deoxyribonucleotides, peptide nucleic acids, and combinations thereof. A probe may optionally comprise one or more labels. In some embodiments, a probe may be used to amplify one or both strands of a target sequence or an RNA form thereof, acting as a sole primer in an amplification reaction or as a member of a set of primers.
“Having” is an open ended phrase like “comprising” and “including,” and includes circumstances where additional elements are included and circumstances where they are not.
“Optional” or “optionally” means that the subsequently described event or circumstance may or may not occur, and that the description includes instances where the event or circumstance occurs and instances in which it does not.
The term “suspected of comprising thyroid cancer,” as used in reference to biological samples or purified fractions or components thereof or products derived therefrom, refers to any sample or product that is to analyzed for the expression of the target sequences described herein, and includes samples comprising normal thyroid tissue, as well as samples comprising thyroid tumors, whether benign or malignant. Such tissue may be obtained from the thyroid itself, from another location within a patient that is a suspected metastases, or from a known sample of malignant thyroid cancer or from a known thyroid cancer cell line. Samples known to be malignant can function as positive controls, while samples known to be noncancerous (or of non-thyroid origin) can function as negative controls, but are “suspected” of comprising thyroid cancer in that they are tested to determine whether the assay being performed produces false positives or other abnormal results, indicating a problem with a given assay.
As used herein, the term “about” refers to approximately a +/−10% variation from a given value. It is to be understood that such a variation is always included in any given value provided herein, whether or not it is specifically referred to.
Use of the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. Thus, for example, reference to “a polynucleotide” includes a plurality of polynucleotides, reference to “a target” includes a plurality of such targets, reference to “a normalization method” includes a plurality of such methods, and the like. Additionally, use of specific plural references, such as “two,” “three,” etc., read on larger numbers of the same subject, unless the context clearly dictates otherwise.
Terms such as “connected,” “attached,” “linked” and “conjugated” are used interchangeably herein and encompass direct as well as indirect connection, attachment, linkage or conjugation unless the context clearly dictates otherwise.
Where a range of values is recited, it is to be understood that each intervening integer value, and each fraction thereof, between the recited upper and lower limits of that range is also specifically disclosed, along with each subrange between such values. The upper and lower limits of any range can independently be included in or excluded from the range, and each range where either, neither or both limits are included is also encompassed within the invention. Where a value being discussed has inherent limits, for example where a component can be present at a concentration of from 0 to 100%, or where the pH of an aqueous solution can range from 1 to 14, those inherent limits are specifically disclosed. Where a value is explicitly recited, it is to be understood that values which are about the same quantity or amount as the recited value are also within the scope of the invention, as are ranges based thereon. Where a combination is disclosed, each subcombination of the elements of that combination is also specifically disclosed and is within the scope of the invention. Conversely, where different elements or groups of elements are disclosed, combinations thereof are also disclosed. Where any element of an invention is disclosed as having a plurality of alternatives, examples of that invention in which each alternative is excluded singly or in any combination with the other alternatives are also hereby disclosed; more than one element of an invention can have such exclusions, and all combinations of elements having such exclusions are hereby disclosed.
Thyroid Classification System
The system of the present invention is based on the identification of a library of gene transcripts that are differentially expressed in thyroid cancer relative to benign thyroid nodule disease and thus may be diagnostic for thyroid cancer. For example, relative over and/or under expression of one or more of the gene transcripts in a thyroid nodule sample compared to a reference sample or expression profile or signature there from may be indicative of a malignant condition. The reference sample can be, for example, from one or more benign thyroid nodules from one or more references subject(s). The reference expression profile or signature may optionally be normalized to one or more appropriate reference gene transcripts. Alternatively or in addition to, expression of one or more of the gene transcripts in a thyroid nodule sample may be compared to an expression profile or signature from one or more known thyroid cancer samples such that a substantially similar expression profile or signature may be used to validate a finding of cancer or may be compared to the expression profile or signature from normal thyroid tissue.
Expression profiles or signatures from diagnostic samples may be normalized to one or more house keeping gene transcripts such that normalized over and/or under expression of one or more of the gene transcripts in a thyroid nodule sample may be indicative of a malignant condition.
Thyroid Classification Library
The Thyroid Classification Library in accordance with the present invention comprises one or more gene transcripts whose relative and/or normalized expression is indicative of a thyroid malignancy or of benign thyroid nodule disease. Gene transcripts which show differential expression in benign and/or malignant thyroid tissue include transcripts comprising the sequences as set forth in SEQ ID NOs: 1 to 584. In one embodiment of the invention, the library comprises one or more of the gene transcripts, each of the transcripts comprising a sequence as set forth in any one of SEQ ID NOs: 1 to 584.
In one embodiment, the library comprises at least one transcript comprising a sequence as set forth in any one of SEQ ID NOs: 1 to 584. In one embodiment, the library comprises at least five transcripts, each of the at least five transcripts comprising a sequence as set forth in any one of SEQ ID NOs: 1 to 584. In another embodiment, the library comprises at least 10 transcripts, each of the at least 10 transcripts comprising a sequence as set forth in any one of SEQ ID NOs: 1 to 584. In a further embodiment, the library comprises at least 15 transcripts, each of the at least 15 transcripts comprising a sequence as set forth in any one of SEQ ID NOs: 1 to 584. In other embodiments, the library comprises at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 55, at least 60 and at least 65 transcripts, each of the at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 55, at least 60 and at least 65 transcripts comprising a sequence as set forth in any one of SEQ ID NOs: 1 to 584. In a further embodiment, the library comprises at least 584 transcripts, each of the at least 584 transcripts comprising a sequence as set forth in any one of SEQ ID NOs: 1 to 584.
In one embodiment, the library comprises a plurality of transcripts, each of the transcripts comprising a sequence as set forth in any one of SEQ ID NOs: 1 to 584, wherein the majority (e.g. 70%, 80%, 90%, 95% or 98%) of the target sequences are in non-coding regions.
In one embodiment, the library comprises a plurality of transcripts, each of the transcripts comprising a sequence as set forth in any one of SEQ ID NOs: 1-4, 6-15, 17-31, 33-43, 47, 49-55, 57-62, 64, 65, 67-71, 73-78, 80, 84, 85, 88, 90-95, 101, 102, 104, 105, 107, 108, 111-113, 116-118, 122-125, 128, 129, 131-133, 135-137, 139, 140-144, 148-150, 152-156, 158, 162-164, 166-171, 173, 175, 176, 177, 179, 185-187, 189, 191-195, 197, 201, 204, 208-217, 220, 221, 224-229, 231-233, 235-241, 245, 247, 250-254, 256-259, 261, 263-267, 269-273, 276, 279, 283-293, 299, 301, 303, 304-306, 308, 309, 312, 313, 315-323, 325, 327, 328, 329, 331-335, 337, 343, 345-353, 355, 358, 360-363, 365, 367, 370-376, 378, 381-384, 389-392, 396, 399-402, 404, 405, 410-414, 418, 420-424, 426-431, 434, 435, 437, 438, 440, 444-449, 451-456, 458, 459, 460, 462, 463-473, 475, 476, 478, 480, 481, 485-488, 490-498, 500-503, 505, 507, 509, 511, 512, 515, 516, 519, 520, 522, 523, 525, 526, 528-532, 534, 535, 538, 541, 542, 544, 547-549, 550-553, 558, 561, 562, 564, 566, 567, 569, 571-573, 575, 576, 579 and 581-584.
The invention also contemplates that alternative libraries may be designed that include in addition to transcripts comprising a sequence as set forth in any one of SEQ ID NOs: 1 to 584, additional gene transcripts that are identified as having differential expression in benign and/or malignant thyroid tissue (for example, see Table 2). As is known in the art, the publication and sequence databases can be mined using a variety of search strategies to identify appropriate candidates for inclusion in the library. For example, currently available scientific and medical publication databases such as Medline, Current Contents, OMIM (online Mendelian inheritance in man), various Biological and Chemical Abstracts, Journal indexes, and the like can be searched using term or key-word searches, or by author, title, or other relevant search parameters. Many such databases are publicly available, and strategies and procedures for identifying publications and their contents, for example, genes, other nucleotide sequences, descriptions, indications, expression pattern, etc, are well known to those skilled in the art. Numerous databases are available through the internet for free or by subscription, see, for example, the National Center Biotechnology Information (NCBI), Infotrieve, Thomson ISI, and Science Magazine (published by the AAAS) websites. Additional or alternative publication or citation databases are also available that provide identical or similar types of information, any of which can be employed in the context of the invention. These databases can be searched for publications describing altered gene expression between malignant thyroid nodule disease and benign thyroid nodule disease. Additional potential candidate genes may be identified by searching the above described databases for differentially expressed proteins and by identifying the nucleotide sequence encoding the differentially expressed proteins.
Thyroid Classification Sets
A Thyroid Classification Set comprises one or more target sequences identified within the gene transcripts in the thyroid classification library, or a subset of these gene transcripts. The target sequences may be within the coding and/or non-coding regions of the gene transcripts. The set can comprise one or a plurality of target sequences from each gene transcript in the library, or subset thereof. The relative and/or normalized level of these target sequences in a sample is indicative of the level of expression of the particular gene transcript and thus of a thyroid malignancy or of benign thyroid nodule disease. For example, the relative and/or normalized expression level of one or more of the target sequences may be indicative of a thyroid malignancy while the relative and/or normalized expression level of one or more other target sequences may be indicative of benign thyroid nodule disease.
Accordingly, one embodiment of the present invention provides for a library or catalog of candidate target sequences derived from the transcripts (both coding and non-coding regions) of at least one gene suitable for classifying thyroid nodules as being malignant or benign. In a further embodiment, the library or catalog of candidate target sequences comprise target sequences as set forth in SEQ ID NOs 1 to 584. The library or catalog in affect provides a resource list of transcripts from which target sequences appropriate for inclusion in a thyroid classification set can be derived. In one embodiment, an individual thyroid classification set may comprise target sequences derived from the transcripts of one or more genes exhibiting a positive correlation with thyroid cancer. In one embodiment, an individual thyroid classification set may comprise target sequences derived from the transcripts of one or more genes exhibiting a negative correlation with thyroid cancer. In one embodiment, an individual Thyroid Classification Set may comprise target sequences derived from the transcripts of from two or more genes, wherein at least one gene has a transcript that exhibits a positive correlation with thyroid cancer and at least one gene has a transcript that exhibits a negative correlation.
In one embodiment, the Thyroid Classification Set comprises target sequences derived from the transcripts of at least one gene. In one embodiment, the Thyroid Classification set comprises target sequences derived from the transcripts of at least 5 genes. In another embodiment, the Thyroid Classification set comprises target sequences derived from the transcripts of at least 10 genes. In a further embodiment, the Thyroid Classification set comprises target sequences derived from the transcripts of at least 15 genes. In other embodiments, the Thyroid Classification set comprises target sequences derived from the transcripts of at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 55, at least 60 and at least 65 genes.
Following the identification of candidate gene transcripts, appropriate target sequences can be identified by screening for target sequences that have been annotated to be associated with each specific gene locus from a number of annotation sources including GenBank, RefSeq, Ensembl, dbEST, GENSCAN, TWINSCAN, Exoniphy, Vega, microRNAs registry and others (see Affymetrix Exon Array design note).
As part of the target sequence selection process, target sequences can be further evaluated for potential cross-hybridization against other putative transcribed sequences in the design (but not the entire genome) to identify only those target sequences that are predicted to uniquely hybridize to a single target.
The set of target sequences that are predicted to uniquely hybridize to a single target can be further filtered using a variety of criteria including, for example, sequence length, for their mean expression levels across a wide selection of human tissues, as being representative of transcripts expressed either as novel alternative (i.e., non-consensus) exons, alternative retained introns, novel exons 5′ or 3′ of the gene's transcriptional start site or representing transcripts expressed in a manner antisense to the gene, amongst others.
In one embodiment, the Thyroid Classification Set comprises target sequences derived from the sequences as set forth in SEQ ID NOs: 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10.
In one embodiment, the Thyroid Classification Set comprises target sequences derived from the sequences as set forth in SEQ ID NOs: 1, 11, 12, 13, 14, and 15.
In one embodiment, the potential set of target sequences can be filtered for their expression levels using the multi-tissue expression data made publicly available by Affymetrix such that probes with, for example, expression across numerous tissues or no expression in thyroid tissue can be excluded.
In one embodiment, the thyroid classification set can be specifically designed to be indicative of malignant thyroid cancer in general or alternatively be indicative of one or more individual types of thyroid cancer.
Validation of Target Sequences
Following in silico selection of target sequences, each target sequence suitable for use in the thyroid classification set may be validated to confirm differential relative or normalized expression in thyroid cancer or benign thyroid nodule disease. Validation methods are known in the art and include hybridization techniques such as microarray analysis or Northern blotting using appropriate controls, and may include one or more additional steps, such as reverse transcription, transcription, PCR, RT-PCR and the like. The validation of the target sequences using these methods is well within the abilities of a worker skilled in the art.
Minimal Expression Signature
In one embodiment, individual thyroid classification sets provide for at least a determination of a minimal expression signature, capable of distinguishing malignant from benign thyroid nodule disease. Means for determining the appropriate number of target sequences necessary to obtain a minimal expression signature are known in the art and include the Nearest Shrunken Centroids (NSC) method.
In this method (see US 20070031873), a standardized centroid is computed for each class. This is the average gene expression for each gene in each class divided by the within-class standard deviation for that gene. Nearest centroid classification takes the gene expression profile of a new sample, and compares it to each of these class centroids. The class whose centroid that it is closest to, in squared distance, is the predicted class for that new sample. Nearest shrunken centroid classification “shrinks” each of the class centroids toward the overall centroid for all classes by an amount called the threshold. This shrinkage consists of moving the centroid towards zero by threshold, setting it equal to zero if it hits zero. For example if threshold was 2.0, a centroid of 3.2 would be shrunk to 1.2, a centroid of −3.4 would be shrunk to −1.4, and a centroid of 1.2 would be shrunk to zero. After shrinking the centroids, the new sample is classified by the usual nearest centroid rule, but using the shrunken class centroids. This shrinkage can make the classifier more accurate by reducing the effect of noisy genes and provides an automatic gene selection. In particular, if a gene is shrunk to zero for all classes, then it is eliminated from the prediction rule. Alternatively, it may be set to zero for all classes except one, and it can be learned that the high or low expression for that gene characterizes that class. The user decides on the value to use for threshold. Typically one examines a number of different choices. To guide in this choice, PAM does K-fold cross-validation for a range of threshold values. The samples are divided up at random into K roughly equally sized parts. For each part in turn, the classifier is built on the other K-1 parts then tested on the remaining part. This is done for a range of threshold values, and the cross-validated misclassification error rate is reported for each threshold value. Typically, the user would choose the threshold value giving the minimum cross-validated misclassification error rate.
Alternatively, minimal expression signatures can be established through the use of optimization algorithms such as the mean variance algorithm widely used in establishing stock portfolios. This method is described in detail in US patent publication number 20030194734. Essentially, the method calls for the establishment of a set of inputs (stocks in financial applications, expression as measured by intensity here) that will optimize the return (e.g., signal that is generated) one receives for using it while minimizing the variability of the return. In other words, the method calls for the establishment of a set of inputs (e.g., expression as measured by intensity) that will optimize the signal while minimizing variability. Many commercial software programs are available to conduct such operations. “Wagner Associates Mean-Variance Optimization Application,” referred to as “Wagner Software” throughout this specification, is preferred. This software uses functions from the “Wagner Associates Mean-Variance Optimization Library” to determine an efficient frontier and optimal portfolios in the Markowitz sense is preferred. Use of this type of software requires that microarray data be transformed so that it can be treated as an input in the way stock return and risk measurements are used when the software is used for its intended financial analysis purposes.
The process of selecting a minimal expression signature can also include the application of heuristic rules. Preferably, such rules are formulated based on biology and an understanding of the technology used to produce clinical results. More preferably, they are applied to output from the optimization method. For example, the mean variance method of portfolio selection can be applied to microarray data for a number of genes differentially expressed in subjects with cancer. Output from the method would be an optimized set of genes that could include some genes that are expressed in peripheral blood as well as in diseased tissue.
Other heuristic rules can be applied that are not necessarily related to the biology in question. For example, one can apply a rule that only a prescribed percentage of the portfolio can be represented by a particular gene or group of genes. Commercially available software such as the Wagner Software readily accommodates these types of heuristics. This can be useful, for example, when factors other than accuracy and precision (e.g., anticipated licensing fees) have an impact on the desirability of including one or more genes.
In one embodiment, the thyroid classification set for obtaining a minimal expression signature comprises at least one, two, three, four, five, six, eight, 10, 15, 20, 25 or more of target sequences shown to have a positive correlation with malignant thyroid disease, for example those depicted in SEQ ID NOs: 1-6, 11-13, and 16-248 or a subset thereof. In another embodiment, the thyroid classification set for obtaining a minimal expression signature comprises at least one, two, three, four, five, six, eight, 10, 15, 20, 25 or more of those target sequences shown to have a positive correlation with benign thyroid disease, for example those depicted in of SEQ ID NOs: 7-10, 14, 15, and 249-584, or a subset thereof. In yet another embodiment, the thyroid classification set for obtaining a minimal expression signature comprises at least one, two, three, four, five, six, eight, 10, 15, 20, 25 or more of target sequences shown to have a positive or negative correlation with malignant thyroid disease, for example those depicted in SEQ ID NOs:1-584 or a subset thereof.
In some embodiments, the thyroid classification set comprises target sequences for detecting expression products of SEQ ID NOs:1-584. In some embodiments, the thyroid classification set comprises probes for detecting expression levels of sequences exhibiting positive and negative correlation with a disease status of interest are employed. For example, a combination useful for identifying a sample as exhibiting malignant or benign disease comprises at least one, two, three, four, five, six, eight, 10, 15, 20, 25 or more of those target sequences shown to have a positive correlation with malignant thyroid disease, for example those depicted in SEQ ID NOs:1-6, 11-13, and 16-248 or a subset thereof; and at least one, two, three, four, five, six, eight, 10, 15, 20, 25 or more of those target sequences shown to have a positive correlation with benign thyroid disease, for example those depicted in of SEQ ID NOs: 7-10, 14, 15, and 249-584, or a subset thereof.
Exemplary subsets and combinations of interest also include at least one, two, three, four, five, six, 10, 15, 18, 20, 23, 25, 27, 30, 35, 40, 45, 50, 55, 60, 70, 80, 90, 100, 125, 150, 175, 200, 225, 250, 275, 300, 350, 400, 450, or 500 of the 584 of the target sequences set forth in SEQ ID NOs: 1 to 584; at least one, two, three, four, five, six, or ten of the target sequences set forth in SEQ ID NOs: 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10, or a combination comprising any or all thereof; at least one, two, three, four, five or six of the target sequences set forth in SEQ ID NOs: 1, 11, 12, 13, 14, and 15, or a combination comprising any or all thereof.
Of particular interest are those combinations utilizing at least one sequence exhibiting positive correlation with the trait of interest, as well as those combinations utilizing at least one sequence exhibiting negative correlation with the trait of interest. Also of interest are those combinations utilizing at least two, at least three, at least four, at least five or at least six of those sequences exhibiting such a positive correlation, in combination with at least two, at least three, at least four, at least five, or at least six of those sequences exhibiting such a negative correlation.
It is to be recognized that those sequences shown as having a positive correlation with malignant disease conversely also possess a negative correlation with benign disease. Correspondingly, those sequences shown as having a positive correlation with benign disease also possess a negative correlation with malignant disease.
The thyroid classification set can optionally include one or more target sequences specifically derived from the transcripts of one or more housekeeping genes and/or one or more internal control target sequences and/or one or more negative control target sequences. In one embodiment, these target sequences can, for example, be used to normalize expression data. Housekeeping genes from which target sequences for inclusion in a Thyroid Classification Set can be derived from are known in the art and include those genes in which are expressed at a constant level in normal, benign and malignant thyroid tissue.
The target sequences described herein may be used alone or in combination with each other or with other known or later identified disease markers.
Thyroid Classification Probes/Primers
The system of the present invention provides for combinations of polynucleotide probes that are capable of detecting the target sequences of the Thyroid Classification Sets. Individual polynucleotide probes comprise a nucleotide sequence derived from the nucleotide sequence of the target sequences or complementary sequences thereof. The nucleotide sequence of the polynucleotide probe is designed such that it corresponds to, or is complementary to the target sequences. The polynucleotide probe can specifically hybridize under either stringent or lowered stringency hybridization conditions to a region of the target sequences, to the complement thereof, or to a nucleic acid sequence (such as a cDNA) derived therefrom.
The selection of the polynucleotide probe sequences and determination of their uniqueness may be carried out in silico using techniques known in the art, for example, based on a BLASTN search of the polynucleotide sequence in question against gene sequence databases, such as the Human Genome Sequence, UniGene, dbEST or the non-redundant database at NCBI. In one embodiment of the invention, the polynucleotide probe is complementary to a region of a target mRNA derived from a PSR in the thyroid classification set. Computer programs can also be employed to select probe sequences that will not cross hybridize or will not hybridize non-specifically.
One skilled in the art will understand that the nucleotide sequence of the polynucleotide probe need not be identical to its target sequence in order to specifically hybridise thereto. The polynucleotide probes of the present invention, therefore, comprise a nucleotide sequence that is at least about 75% identical to a region of the target gene or mRNA. In another embodiment, the nucleotide sequence of the polynucleotide probe is at least about 90% identical a region of the target gene or mRNA. In a further embodiment, the nucleotide sequence of the polynucleotide probe is at least about 95% identical to a region of the target gene or mRNA. Methods of determining sequence identity are known in the art and can be determined, for example, by using the BLASTN program of the University of Wisconsin Computer Group (GCG) software or provided on the NCBI website. The nucleotide sequence of the polynucleotide probes of the present invention may exhibit variability by differing (e.g. by nucleotide substitution, including transition or transversion) at one, two, three, four or more nucleotides from the sequence of the target gene.
Other criteria known in the art may be employed in the design of the polynucleotide probes of the present invention. For example, the probes can be designed to have <50% G content and/or between about 25% and about 70% G+C content. Strategies to optimize probe hybridization to the target nucleic acid sequence can also be included in the process of probe selection. Hybridization under particular pH, salt, and temperature conditions can be optimized by taking into account melting temperatures and by using empirical rules that correlate with desired hybridization behaviours. Computer models may be used for predicting the intensity and concentration-dependence of probe hybridization.
As is known in the art, in order to represent a unique sequence in the human genome, a probe should be at least 15 nucleotides in length. Accordingly, the polynucleotide probes of the present invention range in length from about 15 nucleotides to the full length of the PSR or target mRNA. In one embodiment of the invention, the polynucleotide probes are at least about 15 nucleotides in length. In another embodiment, the polynucleotide probes are at least about 20 nucleotides in length. In a further embodiment, the polynucleotide probes are at least about 25 nucleotides in length. In another embodiment, the polynucleotide probes are between about 15 nucleotides and about 500 nucleotides in length. In other embodiments, the polynucleotide probes are between about 15 nucleotides and about 450 nucleotides, about 15 nucleotides and about 400 nucleotides, about 15 nucleotides and about 350 nucleotides, about 15 nucleotides and about 300 nucleotides in length.
The polynucleotide probes of a thyroid classification set can comprise RNA, DNA, RNA or DNA mimetics, or combinations thereof, and can be single-stranded or double-stranded. Thus the polynucleotide probes can be composed of naturally-occurring nucleobases, sugars and covalent internucleoside (backbone) linkages as well as polynucleotide probes having non-naturally-occurring portions which function similarly. Such modified or substituted polynucleotide probes may provide desirable properties such as, for example, enhanced affinity for a target gene and increased stability.
The system of the present invention further provides for primers and primer pairs capable of amplifying target sequences defined by the thyroid classification set, or fragments or subsequences or complements thereof. The nucleotide sequences of the thyroid classifying set may be provided in computer-readable media for in silico applications and as a basis for the design of appropriate primers for amplification of one or more target sequences of the thyroid classifying set.
Primers based on the nucleotide sequences of target sequences can be designed for use in amplification of the target sequences. For use in amplification reactions such as PCR, a pair of primers will be used. The exact composition of the primer sequences is not critical to the invention, but for most applications the primers will hybridize to specific sequences of the thyroid classification set under stringent conditions, particularly under conditions of high stringency, as known in the art. The pairs of primers are usually chosen so as to generate an amplification product of at least about 50 nucleotides, more usually at least about 100 nucleotides. Algorithms for the selection of primer sequences are generally known, and are available in commercial software packages. These primers may be used in standard quantitative or qualitative PCR-based assays to assess transcript expression levels of RNAs defined by the thyroid classification set. Alternatively, these primers may be used in combination with probes, such as molecular beacons in amplifications using real-time PCR.
In one embodiment, the primers or primer pairs, when used in an amplification reaction, specifically amplify at least a portion of a nucleic acid depicted in one of SEQ ID NOs: 1-584, an RNA form thereof, or a complement to either thereof. Optionally, when amplified, either stand produced by amplification may be provided in purified and/or isolated form.
In one embodiment, the primers or primer pairs, when used in an amplification reaction, specifically amplify at least a portion of a nucleic acid depicted in one of SEQ ID NOs: 1-10, an RNA form thereof, or a complement to either thereof.
In one embodiment, the primers or primer pairs, when used in an amplification reaction, specifically amplify at least a portion of a nucleic acid depicted in one of SEQ ID NOs: 1, 11, 12, 13, 14 and 15, an RNA form thereof, or a complement to either thereof.
As is known in the art, a nucleoside is a base-sugar combination and a nucleotide is a nucleoside that further includes a phosphate group covalently linked to the sugar portion of the nucleoside. In forming oligonucleotides, the phosphate groups covalently link adjacent nucleosides to one another to form a linear polymeric compound, with the normal linkage or backbone of RNA and DNA being a 3′ to 5′ phosphodiester linkage. Specific examples of polynucleotide probes or primers useful in this invention include oligonucleotides containing modified backbones or non-natural internucleoside linkages. As defined in this specification, oligonucleotides having modified backbones include both those that retain a phosphorus atom in the backbone and those that lack a phosphorus atom in the backbone. For the purposes of the present invention, and as sometimes referenced in the art, modified oligonucleotides that do not have a phosphorus atom in their internucleoside backbone can also be considered to be oligonucleotides.
Exemplary polynucleotide probes or primers having modified oligonucleotide backbones include, for example, those with one or more modified internucleotide linkages that are phosphorothioates, chiral phosphorothioates, phosphorodithioates, phosphotriesters, aminoalkylphosphotriesters, methyl and other alkyl phosphonates including 3′-alkylene phosphonates and chiral phosphonates, phosphinates, phosphoramidates including 3′amino phosphoramidate and aminoalkylphosphoramidates, thionophosphoramidates, thionoalkyl-phosphonates, thionoalkylphosphotriesters, and boranophosphates having normal 3′-5′ linkages, 2′-5′ linked analogs of these, and those having inverted polarity wherein the adjacent pairs of nucleoside units are linked 3′-5′ to 5′-3′ or 2′-5′ to 5′-2′. Various salts, mixed salts and free acid forms are also included.
Exemplary modified oligonucleotide backbones that do not include a phosphorus atom are formed by short chain alkyl or cycloalkyl internucleoside linkages, mixed heteroatom and alkyl or cycloalkyl internucleoside linkages, or one or more short chain heteroatomic or heterocyclic internucleoside linkages. Such backbones include morpholino linkages (formed in part from the sugar portion of a nucleoside); siloxane backbones; sulfide, sulfoxide and sulphone backbones; formacetyl and thioformacetyl backbones; methylene formacetyl and thioformacetyl backbones; alkene containing backbones; sulphamate backbones; methyleneimino and methylenehydrazino backbones; sulphonate and sulfonamide backbones; amide backbones; and others having mixed N, O, S and CH2 component parts.
The present invention also contemplates oligonucleotide mimetics in which both the sugar and the internucleoside linkage of the nucleotide units are replaced with novel groups. The base units are maintained for hybridization with an appropriate nucleic acid target compound. An example of such an oligonucleotide mimetic, which has been shown to have excellent hybridization properties, is a peptide nucleic acid (PNA) [Nielsen et al., Science, 254:1497-1500 (1991)]. In PNA compounds, the sugar-backbone of an oligonucleotide is replaced with an amide containing backbone, in particular an aminoethylglycine backbone. The nucleobases are retained and are bound directly or indirectly to aza-nitrogen atoms of the amide portion of the backbone.
The present invention also contemplates polynucleotide probes or primers comprising “locked nucleic acids” (LNAs), which are novel conformationally restricted oligonucleotide analogues containing a methylene bridge that connects the 2′-O of ribose with the 4′-C (see, Singh et al., Chem. Commun., 1998, 4:455-456). LNA and LNA analogues display very high duplex thermal stabilities with complementary DNA and RNA, stability towards 3′-exonuclease degradation, and good solubility properties. Synthesis of the LNA analogues of adenine, cytosine, guanine, 5-methylcytosine, thymine and uracil, their oligomerization, and nucleic acid recognition properties have been described (see Koshkin et al., Tetrahedron, 1998, 54:3607-3630). Studies of mis-matched sequences show that LNA obey the Watson-Crick base pairing rules with generally improved selectivity compared to the corresponding unmodified reference strands.
LNAs form duplexes with complementary DNA or RNA or with complementary LNA, with high thermal affinities. The universality of LNA-mediated hybridization has been emphasized by the formation of exceedingly stable LNA:LNA duplexes (Koshkin et al., J. Am. Chem. Soc., 1998, 120:13252-13253). LNA:LNA hybridization was shown to be the most thermally stable nucleic acid type duplex system, and the RNA-mimicking character of LNA was established at the duplex level. Introduction of three LNA monomers (T or A) resulted in significantly increased melting points toward DNA complements.
Synthesis of 2′-amino-LNA (Singh et al., J. Org. Chem., 1998, 63, 10035-10039) and 2′-methylamino-LNA has been described and thermal stability of their duplexes with complementary RNA and DNA strands reported. Preparation of phosphorothioate-LNA and 2′-thio-LNA have also been described (Kumar et al., Bioorg. Med. Chem. Lett., 1998, 8:2219-2222).
Modified polynucleotide probes or primers may also contain one or more substituted sugar moieties. For example, oligonucleotides may comprise sugars with one of the following substituents at the 2′ position: OH; F; O-, S, or N-alkyl; O, S, or N-alkenyl; O, S or N-alkynyl; or O-alkyl-O-alkyl, wherein the alkyl, alkenyl and alkynyl may be substituted or unsubstituted C1 to C10 alkyl or C2 to C10 alkenyl and alkynyl. Examples of such groups are: O[(CH2)nO]mCH3, O(CH2)nOCH3, O(CH2)n NH2, O(CH2)nCH3, O(CH2)nONH2, and O(CH2)nON[(CH2)nCH3)]2, where n and m are from 1 to about 10. Alternatively, the oligonucleotides may comprise one of the following substituents at the 2′ position: C1 to C10 lower alkyl, substituted lower alkyl, alkaryl, aralkyl, O-alkaryl or O-aralkyl, SH, SCH3, OCN, Cl, Br, CN, CF3, OCF3, SOCH3, SO2CH3, ONO2, NO2, N3, NH2, heterocycloalkyl, heterocycloalkaryl, aminoalkylamino, polyalkylamino, substituted silyl, an RNA cleaving group, a reporter group, an intercalator, a group for improving the pharmacokinetic properties of an oligonucleotide, or a group for improving the pharmacodynamic properties of an oligonucleotide, and other substituents having similar properties. Specific examples include 2′-methoxyethoxy (2′-O—CH2CH2OCH3, also known as 2′-O-(2-methoxyethyl) or 2′-MOE) [Martin et al., Helv. Chim. Acta, 78:486-504(1995)], 2′-dimethylaminooxyethoxy (O(CH2)2ON(CH3)2 group, also known as 2′-DMAOE), 2′-methoxy (2′-O—CH3), 2′-aminopropoxy (2′-OCH2CH2CH2NH2) and 2′-fluoro (2′-F).
Similar modifications may also be made at other positions on the polynucleotide probes or primers, particularly the 3′ position of the sugar on the 3′ terminal nucleotide or in 2′-5′ linked oligonucleotides and the 5′ position of 5′ terminal nucleotide. Polynucleotide probes or primers may also have sugar mimetics such as cyclobutyl moieties in place of the pentofuranosyl sugar.
Polynucleotide probes or primers may also include modifications or substitutions to the nucleobase. As used herein, “unmodified” or “natural” nucleobases include the purine bases adenine (A) and guanine (G), and the pyrimidine bases thymine (T), cytosine (C) and uracil (U). Modified nucleobases include other synthetic and natural nucleobases such as 5-methylcytosine (5-me-C), 5-hydroxymethyl cytosine, xanthine, hypoxanthine, 2-aminoadenine, 6-methyl and other alkyl derivatives of adenine and guanine, 2-propyl and other alkyl derivatives of adenine and guanine, 2-thiouracil, 2-thiothymine and 2-thiocytosine, 5-halouracil and cytosine, 5-propynyl uracil and cytosine, 6-azo uracil, cytosine and thymine, 5-uracil (pseudouracil), 4-thiouracil, 8-halo, 8-amino, 8-thiol, 8-thioalkyl, 8-hydroxyl and other 8-substituted adenines and guanines, 5-halo particularly 5-bromo, 5-trifluoromethyl and other 5-substituted uracils and cytosines, 7-methylguanine and 7-methyladenine, 8-azaguanine and 8-azaadenine, 7-deazaguanine and 7-deazaadenine and 3-deazaguanine and 3-deazaadenine. Further nucleobases include those disclosed in U.S. Pat. No. 3,687,808; The Concise Encyclopedia Of Polymer Science And Engineering, (1990) pp 858-859, Kroschwitz, J. I., ed. John Wiley & Sons; Englisch et al., Angewandte Chemie, Int. Ed., 30:613 (1991); and Sanghvi, Y. S., (1993) Antisense Research and Applications, pp 289-302, Crooke, S. T. and Lebleu, B., ed., CRC Press. Certain of these nucleobases are particularly useful for increasing the binding affinity of the polynucleotide probes of the invention. These include 5-substituted pyrimidines, 6-azapyrimidines and N-2, N-6 and O-6 substituted purines, including 2-aminopropyladenine, 5-propynyluracil and 5-propynylcytosine. 5-methylcytosine substitutions have been shown to increase nucleic acid duplex stability by 0.6-1.2° C. [Sanghvi, Y. S., (1993) Antisense Research and Applications, pp 276-278, Crooke, S. T. and Lebleu, B., ed., CRC Press, Boca Raton].
One skilled in the art will recognize that it is not necessary for all positions in a given polynucleotide probe or primer to be uniformly modified. The present invention, therefore, contemplates the incorporation of more than one of the aforementioned modifications into a single polynucleotide probe or even at a single nucleoside within the probe or primer.
One skilled in the art will also appreciate that the nucleotide sequence of the entire length of the polynucleotide probe or primer does not need to be derived from the target sequence. Thus, for example, the polynucleotide probe may comprise nucleotide sequences at the 5′ and/or 3′ to the transcription start and stop sites, respectively that are not derived from the target sequences. Nucleotide sequences which are not derived from the nucleotide sequence of the target sequence may provide additional functionality to the polynucleotide probe. For example, they may provide a restriction enzyme recognition sequence or a “tag” that facilitates detection, isolation, purification or immobilisation onto a solid support. Alternatively, the additional nucleotides may provide a self-complementary sequence that allows the primer/probe to adopt a hairpin configuration. Such configurations are necessary for certain probes, for example, molecular beacon and Scorpion probes, which can be used in solution hybridization techniques.
The polynucleotide probes or primers can incorporate moieties useful in detection, isolation, purification, or immobilisation, if desired. Such moieties are well-known in the art (see, for example, Ausubel et al., (1997 & updates) Current Protocols in Molecular Biology, Wiley & Sons, New York) and are chosen such that the ability of the probe to hybridize with its target sequence is not affected.
Examples of suitable moieties are detectable labels, such as radioisotopes, fluorophores, chemiluminophores, enzymes, colloidal particles, and fluorescent microparticles, as well as antigens, antibodies, haptens, avidin/streptavidin, biotin, haptens, enzyme cofactors/substrates, enzymes, and the like.
A label can optionally be attached to or incorporated into a probe or primer polynucleotide to allow detection and/or quantitation of a target polynucleotide representing the target sequence of interest. The target polynucleotide may be the expressed target sequence RNA itself, a cDNA copy thereof, or an amplification product derived therefrom, and may be the positive or negative strand, so long as it can be specifically detected in the assay being used. Similarly, an antibody may be labeled.
In certain multiplex formats, labels used for detecting different targets may be distinguishable. The label can be attached directly (e.g., via covalent linkage) or indirectly, e.g., via a bridging molecule or series of molecules (e.g., a molecule or complex that can bind to an assay component, or via members of a binding pair that can be incorporated into assay components, e.g. biotin-avidin or streptavidin). Many labels are commercially available in activated forms which can readily be used for such conjugation (for example through amine acylation), or labels may be attached through known or determinable conjugation schemes, many of which are known in the art.
Labels useful in the invention described herein include any substance which can be detected when bound to or incorporated into the biomolecule of interest. Any effective detection method can be used, including optical, spectroscopic, electrical, piezoelectrical, magnetic, Raman scattering, surface plasmon resonance, colorimetric, calorimetric, etc. A label is typically selected from a chromophore, a lumiphore, a fluorophore, one member of a quenching system, a chromogen, a hapten, an antigen, a magnetic particle, a material exhibiting nonlinear optics, a semiconductor nanocrystal, a metal nanoparticle, an enzyme, an antibody or binding portion or equivalent thereof, an aptamer, and one member of a binding pair, and combinations thereof. Quenching schemes may be used, wherein a quencher and a fluorophore as members of a quenching pair may be used on a probe, such that a change in optical parameters occurs upon binding to the target introduce or quench the signal from the fluorophore. One example of such a system is a molecular beacon. Suitable quencher/fluorophore systems are known in the art. The label may be bound through a variety of intermediate linkages. For example, a polynucleotide may comprise a biotin-binding species, and an optically detectable label may be conjugated to biotin and then bound to the labeled polynucleotide. Similarly, a polynucleotide sensor may comprise an immunological species such as an antibody or fragment, and a secondary antibody containing an optically detectable label may be added.
Chromophores useful in the methods described herein include any substance which can absorb energy and emit light. For multiplexed assays, a plurality of different signaling chromophores can be used with detectably different emission spectra. The chromophore can be a lumophore or a fluorophore. Typical fluorophores include fluorescent dyes, semiconductor nanocrystals, lanthanide chelates, polynucleotide-specific dyes and green fluorescent protein.
Coding schemes may optionally be used, comprising encoded particles and/or encoded tags associated with different polynucleotides of the invention. A variety of different coding schemes are known in the art, including fluorophores, including SCNCs, deposited metals, and RF tags.
Polynucleotides from the described target sequences may be employed as probes for detecting target sequences expression, for ligation amplification schemes, or may be used as primers for amplification schemes of all or a portion of a target sequences. When amplified, either strand produced by amplification may be provided in purified and/or isolated form.
In one embodiment, polynucleotides of the invention include a nucleic acid depicted in (a) any of SEQ ID NOs: 1-584; (b) an RNA form of any of the nucleic acids depicted in SEQ ID NOs: 1-584; (c) a peptide nucleic acid form of any of the nucleic acids depicted in SEQ ID NOs: 1-584; (d) a nucleic acid comprising at least 20 consecutive bases of any of (a-c); (e) a nucleic acid comprising at least 25 consecutive bases having at least 90% sequence identity to any of (a-c); and a complement to any of (a-e).
Complements may take any polymeric form capable of base pairing to the species recited in (a)-(e), including nucleic acid such as RNA or DNA, or may be a neutral polymer such as a peptide nucleic acid. Polynucleotides of the invention can be selected from the subsets of the recited nucleic acids described herein, as well as their complements.
In some embodiments, polynucleotides of the invention comprise at least 20 consecutive bases as depicted in SEQ ID NOs:1-584, or a complement thereto. The polynucleotides may comprise at least 21, 22, 23, 24, 25, 27, 30, 32, 35 or more consecutive bases as depicted in SEQ ID NOs:1-584.
The polynucleotides may be provided in a variety of formats, including as solids, in solution, or in an array. The polynucleotides may optionally comprise one or more labels, which may be chemically and/or enzymatically incorporated into the polynucleotide.
In one embodiment, solutions comprising polynucleotide and a solvent are also provided. In some embodiments, the solvent may be water or may be predominantly aqueous. In some embodiments, the solution may comprise at least two, three, four, five, six, seven, eight, nine, ten, twelve, fifteen, seventeen, twenty or more different polynucleotides, including primers and primer pairs, of the invention. Additional substances may be included in the solution, alone or in combination, including one or more labels, additional solvents, buffers, biomolecules, polynucleotides, and one or more enzymes useful for performing methods described herein, including polymerases and ligases. The solution may further comprise a primer or primer pair capable of amplifying a polynucleotide of the invention present in the solution.
In some embodiments, one or more polynucleotides provided herein can be provided on a substrate. The substrate can comprise a wide range of material, either biological, nonbiological, organic, inorganic, or a combination of any of these. For example, the substrate may be a polymerized Langmuir Blodgett film, functionalized glass, Si, Ge, GaAs, GaP, SiO2, SiN4, modified silicon, or any one of a wide variety of gels or polymers such as (poly)tetrafluoroethylene, (poly)vinylidenedifluoride, polystyrene, cross-linked polystyrene, polyacrylic, polylactic acid, polyglycolic acid, poly(lactide coglycolide), polyanhydrides, poly(methyl methacrylate), poly(ethylene-co-vinyl acetate), polysiloxanes, polymeric silica, latexes, dextran polymers, epoxies, polycarbonates, or combinations thereof. Conducting polymers and photoconductive materials can be used.
Substrates can be planar crystalline substrates such as silica based substrates (e.g. glass, quartz, or the like), or crystalline substrates used in, e.g., the semiconductor and microprocessor industries, such as silicon, gallium arsenide, indium doped GaN and the like, and includes semiconductor nanocrystals.
The substrate can take the form of an array, a photodiode, an optoelectronic sensor such as an optoelectronic semiconductor chip or optoelectronic thin-film semiconductor, or a biochip. The location(s) of probe(s) on the substrate can be addressable; this can be done in highly dense formats, and the location(s) can be microaddressable or nanoaddressable.
Silica aerogels can also be used as substrates, and can be prepared by methods known in the art. Aerogel substrates may be used as free standing substrates or as a surface coating for another substrate material.
The substrate can take any form and typically is a plate, slide, bead, pellet, disk, particle, microparticle, nanoparticle, strand, precipitate, optionally porous gel, sheets, tube, sphere, container, capillary, pad, slice, film, chip, multiwell plate or dish, optical fiber, etc. The substrate can be any form that is rigid or semi-rigid. The substrate may contain raised or depressed regions on which an assay component is located. The surface of the substrate can be etched using known techniques to provide for desired surface features, for example trenches, v-grooves, mesa structures, or the like.
Surfaces on the substrate can be composed of the same material as the substrate or can be made from a different material, and can be coupled to the substrate by chemical or physical means. Such coupled surfaces may be composed of any of a wide variety of materials, for example, polymers, plastics, resins, polysaccharides, silica or silica-based materials, carbon, metals, inorganic glasses, membranes, or any of the above-listed substrate materials. The surface can be optically transparent and can have surface Si—OH functionalities, such as those found on silica surfaces.
The substrate and/or its optional surface can be chosen to provide appropriate characteristics for the synthetic and/or detection methods used. The substrate and/or surface can be transparent to allow the exposure of the substrate by light applied from multiple directions. The substrate and/or surface may be provided with reflective “mirror” structures to increase the recovery of light.
The substrate and/or its surface is generally resistant to, or is treated to resist, the conditions to which it is to be exposed in use, and can be optionally treated to remove any resistant material after exposure to such conditions.
The substrate or a region thereof may be encoded so that the identity of the sensor located in the substrate or region being queried may be determined. Any suitable coding scheme can be used, for example optical codes, RFID tags, magnetic codes, physical codes, fluorescent codes, and combinations of codes.
Preparation of Probes and Primers
The polynucleotide probes or primers of the present invention can be prepared by conventional techniques well-known to those skilled in the art. For example, the polynucleotide probes can be prepared using solid-phase synthesis using commercially available equipment. As is well-known in the art, modified oligonucleotides can also be readily prepared by similar methods. The polynucleotide probes can also be synthesized directly on a solid support according to methods standard in the art. This method of synthesizing polynucleotides is particularly useful when the polynucleotide probes are part of a nucleic acid array.
Polynucleotide probes or primers can be fabricated on or attached to the substrate by any suitable method, for example the methods described in U.S. Pat. No. 5,143,854, PCT Publ. No. WO 92/10092, U.S. patent application Ser. No. 07/624,120, filed Dec. 6, 1990 (now abandoned), Fodor et al., Science, 251: 767-777 (1991), and PCT Publ. No. WO 90/15070). Techniques for the synthesis of these arrays using mechanical synthesis strategies are described in, e.g., PCT Publication No. WO 93/09668 and U.S. Pat. No. 5,384,261. Still further techniques include bead based techniques such as those described in PCT Appl. No. PCT/US93/04145 and pin based methods such as those described in U.S. Pat. No. 5,288,514. Additional flow channel or spotting methods applicable to attachment of sensor polynucleotides to a substrate are described in U.S. patent application Ser. No. 07/980,523, filed Nov. 20, 1992, and U.S. Pat. No. 5,384,261.
Alternatively, the polynucleotide probes of the present invention can be prepared by enzymatic digestion of the naturally occurring target gene, or mRNA or cDNA derived therefrom, by methods known in the art.
Thyroid Classification Methods
The present invention further provides methods for characterizing thyroid samples for the presence of malignant or benign thyroid nodule disease. The methods use the thyroid classification sets, probes and primers described herein to provide expression signatures or profiles from a test sample derived from a subject having or suspected of having thyroid cancer. In some embodiments, such methods involve contacting a test sample with thyroid classifying probes (either in solution or immobilized) under conditions that permit hybridization of the probe(s) to any target nucleic acid(s) present in the test sample and then detecting any probe:target duplexes formed as an indication of the presence of the target nucleic acid in the sample. Expression patterns thus determined are then compared to one or more reference profiles or signatures. Optionally, the expression pattern can be normalized. The methods use the thyroid classification sets, probes and primers described herein to provide expression signatures or profiles from a test sample derived from a subject to classify thyroid nodule tissue as malignant or benign.
The assay/method is capable of discriminating malignant and benign samples with good accuracy even in samples classified as “indeterminate” by FNAB cytology and that can only otherwise be diagnosed by pathology review diagnosis.
In some embodiments, such methods involve the specific amplification of target sequences nucleic acid(s) present in the test sample using methods known in the art to generate an expression profile or signature which is then compared to a reference profile or signature.
In some embodiments, the invention further provides for diagnosing thyroid cancer, for prognosing patient outcome, and/or for designating treatment modalities.
In one embodiment, the methods generate expression profiles or signatures detailing the expression of the 584 target sequences having altered relative expression in malignant and benign thyroid disease disclosed herein. In one embodiment, the methods generate expression profiles or signatures detailing the expression of the subsets of these target sequences having 10 or 6 target sequences as described in the examples.
In some embodiments, the methods detect increased relative expression of one or more target sequences in Group I corresponding to the expression products of SEQ ID NOs:1-6, 11-13, and 16-248, and/or decreased relative expression of one or more target sequences in Group II corresponding to the expression products of SEQ ID NOs: 7-10, 14, 15, and 249-584, and thereby designate a sample as comprising malignant thyroid nodule disease. In some embodiments, increased relative expression of one or more target sequences in Group II and/or decreased relative expression of one or more target sequences in Group I and thereby designate a sample as comprising benign thyroid nodule disease.
In some embodiments, the methods detect combinations of expression levels of sequences exhibiting positive and negative correlation with a disease status. In one embodiment, the methods detect a minimal expression signature.
Any method of detecting and/or quantitating the expression of the encoded target sequences can in principle be used in the invention. Such methods can include Northern blotting, array or microarray hybridization, by enzymatic cleavage of specific structures (e.g., an Invader® assay, Third Wave Technologies, e.g. as described in U.S. Pat. Nos. 5,846,717, 6,090,543; 6,001,567; 5,985,557; and 5,994,069) and amplification methods, e.g. RT-PCR, including in a TaqMan® assay (PE Biosystems, Foster City, Calif., e.g. as described in U.S. Pat. Nos. 5,962,233 and 5,538,848), and may be quantitative or semi-quantitative, and may vary depending on the origin, amount and condition of the available biological sample. Combinations of these methods may also be used. For example, nucleic acids may be amplified, labeled and subjected to microarray analysis. Single-molecule sequencing (e.g., Illumina, Helicos, PacBio, ABI SOLID), in situ hybridization, bead-array technologies (e.g., Luminex xMAP, Illumina BeadChips), branched DNA technology (e.g., Panomics, Genisphere).
The expressed target sequences can be directly detected and/or quantitated, or may be copied and/or amplified to allow detection of amplified copies of the expressed target sequences or its complement. In some embodiments, degraded and/or fragmented RNA can be usefully analyzed for expression levels of target sequences, for example RNA having an RNA integrity number of less than 8.
In some embodiments, quantitative RT-PCR assays are used to measure the expression level of target sequences depicted in SEQ ID NOs: 1-584. In other embodiments, a GeneChip or microarray can be used to measure the expression of one or more of the target sequences.
Molecular assays measure the relative expression levels of the target sequences, which can be normalized to the expression levels of one or more control sequences, for example array control sequences and/or one or more housekeeping genes, for example GAPDH. Increased (or decreased) relative expression of the target sequences as described herein, including any of SEQ ID NOs:1-584, may thus be used alone or in any combination with each other in the methods described herein. In addition, negative control probes may be included.
Diagnostic Samples
Diagnostic samples for use with the systems and in the methods of the present invention comprise nucleic acids suitable for providing RNAs expression information. In principle, the biological sample from which the expressed RNA is obtained and analyzed for target sequence expression can be any material suspected of comprising thyroid cancer. The diagnostic sample can be a biological sample used directly in a method of the invention. Alternatively, the diagnostic sample can be a sample prepared from a biological sample.
In one embodiments, the sample or portion of the sample comprising or suspected of comprising thyroid cancer can be any source of biological material, including cells, tissue or fluid, including bodily fluids. Non-limiting examples of the source of the sample include an aspirate, a needle biopsy, a liquid-based preparation (e.g., ThinPrep®) cytology pellet, a bulk tissue preparation or a section thereof obtained for example by surgery or autopsy, lymph fluid, blood, plasma, serum, tumors, and organs. The assay and methods are broadly applicable to FFPE samples.
The samples may be archival samples, having a known and documented medical outcome, or may be samples from current patients whose ultimate medical outcome is not yet known. Samples to be analyzed for thyroid cancer are typically obtained as fine needle aspirates, a cytology smear, a cytology pellet, or as bulk samples obtained, for example, from a thyroidectomy. Where samples of a bodily fluid are obtained, cells or cell types may be isolated and/or purified therefrom. For example, circulating epithelial cells can be obtained from peripheral blood and analyzed as described herein. In some embodiments, magnetic separation can be used to obtain circulating epithelial cells (U.S. Pat. No. 6,136,182).
In some embodiments, the sample may be dissected prior to molecular analysis. The sample may be prepared via macrodissection of a bulk tumor specimen or portion thereof, or may be treated via microdissection, for example via Laser Capture Microdis section (LCM).
The sample may initially be provided in a variety of states, as fresh tissue, fresh frozen tissue, fine needle aspirates, and may be fixed or unfixed. Frequently, medical laboratories routinely prepare medical samples in a fixed state, which facilitates tissue storage. A variety of fixatives can be used to fix tissue to stabilize the morphology of cells, and may be used alone or in combination with other agents. Exemplary fixatives include crosslinking agents, alcohols, acetone, Bouin's solution, Zenker solution, Hely solution, osmic acid solution and Carnoy solution.
Crosslinking fixatives can comprise any agent suitable for forming two or more covalent bonds, for example an aldehyde. Sources of aldehydes typically used for fixation include formaldehyde, paraformaldehyde, glutaraldehyde or formalin. Preferably, the crosslinking agent comprises formaldehyde, which may be included in its native form or in the form of paraformaldehyde or formalin. One of skill in the art would appreciate that for samples in which crosslinking fixatives have been used special preparatory steps may be necessary including for example heating steps and proteinase-k digestion; see methods
One or more alcohols may be used to fix tissue, alone or in combination with other fixatives. Exemplary alcohols used for fixation include methanol, ethanol and isopropanol.
Formalin fixation is frequently used in medical laboratories. Formalin comprises both an alcohol, typically methanol, and formaldehyde, both of which can act to fix a biological sample.
Whether fixed or unfixed, the biological sample may optionally be embedded in an embedding medium. Exemplary embedding media used in histology including paraffin, Tissue-Tek® Paramat, Paramat Extra, Paraplast, Paraplast X-tra, Paraplast Plus, Peel Away Paraffin Embedding Wax, Polyester Wax, Carbowax Polyethylene Glycol, PolyfinTM, Tissue Freezing Medium TFMTM, Cryo-GelTM, and OCT Compound (Electron Microscopy Sciences, Hatfield, Pa.). Prior to molecular analysis, the embedding material may be removed via any suitable techniques, as known in the art. For example, where the sample is embedded in wax, the embedding material may be removed by extraction with organic solvent(s), for example xylenes. Kits are commercially available for removing embedding media from tissues. Samples or sections thereof may be subjected to further processing steps as needed, for example serial hydration or dehydration steps.
In some embodiments, the sample is a fixed, wax-embedded biological sample. Frequently, samples from medical laboratories are provided as fixed, wax-embedded samples, most commonly as formalin-fixed, paraffin embedded (FFPE) tissues.
Whatever the source of the biological sample, the target polynucleotide that is ultimately assayed can be prepared synthetically (in the case of control sequences), but typically is purified from the biological source and subjected to one or more preparative steps. The RNA may be purified to remove or diminish one or more undesired components from the biological sample or to concentrate it. Conversely, where the RNA is too concentrated for the particular assay, it may be diluted.
RNA Extraction
RNA can be extracted and purified from biological samples using any suitable technique. A number of techniques are known in the art, and several are commercially available (e.g., FormaPureTM nucleic acid extraction kit, Agencourt Biosciences, Beverly Mass., High Pure FFPE RNA Micro KitTM, Roche Applied Science, Indianapolis, Ind.). RNA can be extracted from frozen tissue sections using TRIzol (Invitrogen, Carlsbad, Calif.) and purified using RNeasy Protect kit (Qiagen, Valencia, Calif.). RNA can be further purified using DNAse I treatment (Ambion, Austin, Tex.) to eliminate any contaminating DNA. RNA concentrations can be made using a Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies, Rockland, Del.). RNA integrity can be evaluated by running electropherograms, and RNA integrity number (RIN, a correlative measure that indicates intactness of mRNA) can be determined using the RNA 6000 PicoAssay for the Bioanalyzer 2100 (Agilent Technologies, Santa Clara, Calif.).
Amplification and Hybridization
Following sample collection and nucleic acid extraction, the nucleic acid portion of the sample comprising RNA that is or can be used to prepare the target polynucleotide(s) of interest can be subjected to one or more preparative reactions. These preparative reactions can include in vitro transcription (IVT), labeling, fragmentation, amplification and other reactions. mRNA can first be treated with reverse transcriptase and a primer to create cDNA prior to detection, quantitation and/or amplification; this can be done in vitro with purified mRNA or in situ, e.g., in cells or tissues affixed to a slide.
By “amplification” is meant any process of producing at least one copy of a nucleic acid, in this case an expressed RNA, and in many cases produces multiple copies. An amplification product can be RNA or DNA, and may include a complementary strand to the expressed target sequence. DNA amplification products can be produced initially through reverse translation and then optionally from further amplification reactions. The amplification product may include all or a portion of a PSR, and may optionally be labeled. A variety of amplification methods are suitable for use, including polymerase-based methods and ligation-based methods. Exemplary amplification techniques include the polymerase chain reaction method (PCR), the ligase chain reaction (LCR), ribozyme-based methods, self sustained sequence replication (3SR), nucleic acid sequence-based amplification (NASBA), the use of Q Beta replicase, reverse transcription, nick translation, and the like.
Asymmetric amplification reactions may be used to preferentially amplify one strand representing the PSR that is used for detection as the target polynucleotide. In some cases, the presence and/or amount of the amplification product itself may be used to determine the expression level of a given PSR. In other instances, the amplification product may be used to hybridize to an array or other substrate comprising sensor polynucleotides which are used to detect and/or quantitate PSR expression.
The first cycle of amplification in polymerase-based methods typically forms a primer extension product complementary to the template strand. If the template is single-stranded RNA, a polymerase with reverse transcriptase activity is used in the first amplification to reverse transcribe the RNA to DNA, and additional amplification cycles can be performed to copy the primer extension products. The primers for a PCR must, of course, be designed to hybridize to regions in their corresponding template that will produce an amplifiable segment; thus, each primer must hybridize so that its 3′ nucleotide is paired to a nucleotide in its complementary template strand that is located 3′ from the 3′ nucleotide of the primer used to replicate that complementary template strand in the PCR.
The target polynucleotide can be amplified by contacting one or more strands of the target polynucleotide with a primer and a polymerase having suitable activity to extend the primer and copy the target polynucleotide to produce a full-length complementary polynucleotide or a smaller portion thereof. Any enzyme having a polymerase activity that can copy the target polynucleotide can be used, including DNA polymerases, RNA polymerases, reverse transcriptases, enzymes having more than one type of polymerase or enzyme activity. The enzyme can be thermolabile or thermostable. Mixtures of enzymes can also be used. Exemplary enzymes include: DNA polymerases such as DNA Polymerase I (“Pol I”), the Klenow fragment of Pol I, T4, T7, Sequenase® T7, Sequenase® Version 2.0 T7, Tub, Taq, Tth, Pfx, Pfu, Tsp, Tfl, Tli and Pyrococcus sp GB-D DNA polymerases; RNA polymerases such as E. coli, SP6, T3 and T7 RNA polymerases; and reverse transcriptases such as AMV, M-MuLV, MMLV, RNAse H MMLV (SuperScript®), SuperScript® II, ThermoScript®, HIV-1, and RAV2 reverse transcriptases. All of these enzymes are commercially available. Exemplary polymerases with multiple specificities include RAV2 and Tli (exo-) polymerases. Exemplary thermostable polymerases include Tub, Taq, Tth, Pfx, Pfu, Tsp, Tfl, Tli and Pyrococcus sp. GB-D DNA polymerases.
Suitable reaction conditions are chosen to permit amplification of the target polynucleotide, including pH, buffer, ionic strength, presence and concentration of one or more salts, presence and concentration of reactants and cofactors such as nucleotides and magnesium and/or other metal ions (e.g., manganese), optional cosolvents, temperature, thermal cycling profile for amplification schemes comprising a polymerase chain reaction, and may depend in part on the polymerase being used as well as the nature of the sample. Cosolvents include formamide (typically at from about 2 to about 10%), glycerol (typically at from about 5 to about 10%), and DMSO (typically at from about 0.9 to about 10%). Techniques may be used in the amplification scheme in order to minimize the production of false positives or artifacts produced during amplification. These include “touchdown” PCR, hot-start techniques, use of nested primers, or designing PCR primers so that they form stem-loop structures in the event of primer-dimer formation and thus are not amplified. Techniques to accelerate PCR can be used, for example centrifugal PCR, which allows for greater convection within the sample, and comprising infrared heating steps for rapid heating and cooling of the sample. One or more cycles of amplification can be performed. An excess of one primer can be used to produce an excess of one primer extension product during PCR; preferably, the primer extension product produced in excess is the amplification product to be detected. A plurality of different primers may be used to amplify different target polynucleotides or different regions of a particular target polynucleotide within the sample.
An amplification reaction can be performed under conditions which allow an optionally labeled sensor polynucleotide to hybridize to the amplification product during at least part of an amplification cycle. When the assay is performed in this manner, real-time detection of this hybridization event can take place by monitoring for light emission or fluorescence during amplification, as known in the art.
Where the amplification product is to be used for hybridization to an array or microarray, a number of suitable commercially available amplification products are available. These include amplification kits available from NuGEN, Inc. (San Carlos, Calif.), including the WT-OvationTM System, WT-OvationTM System v2, WT-OvationTM Pico System, WT-OvationTM FFPE Exon Module, WT-OvationTM FFPE Exon Module RiboAmp and RiboAmpPlus RNA Amplification Kits (MDS Analytical Technologies (formerly Arcturus) (Mountain View, Calif.), Genisphere, Inc. (Hatfield, Pa.), including the RampUp PlusTM and SenseAmpTM RNA Amplification kits, alone or in combination. Amplified nucleic acids may be subjected to one or more purification reactions after amplification and labeling, for example using magnetic beads (e.g., RNAClean magnetic beads, Agencourt Biosciences).
Multiple RNA biomarkers can be analyzed using real-time quantitative multiplex RT-PCR platforms and other multiplexing technologies such as GenomeLab GeXP Genetic Analysis System (Beckman Coulter, Foster City, Calif.), SmartCycler® 9600 or GeneXpert(R) Systems (Cepheid, Sunnyvale, Calif.), ABI 7900 HT Fast Real Time PCR system (Applied Biosystems, Foster City, Calif.), LightCycler® 480 System (Roche Molecular Systems, Pleasanton, Calif.), xMAP 100 System (Luminex, Austin, Tex.) Solexa Genome Analysis System (Illumina, Hayward, Calif.), OpenArray Real Time qPCR (BioTrove, Woburn, Mass.) and BeadXpress System (Illumina, Hayward, Calif.).
Thyroid Classification Arrays
The present invention contemplates that a thyroid classification set or probes derived therefrom may be provided in an array format. In the context of the present invention, an “array” is a spatially or logically organized collection of polynucleotide probes. Any array comprising sensor probes specific for two or more of the target sequences depicted in SEQ ID NOs: 1-584 or a product derived from the target sequences depicted therein can be used. Desirably, an array will be specific for 5, 10, 15, 20, 25, 30, 50, 75, 100, 150, 200, 250, 300, 350, 400, 450, 500, 600, 700 or more of SEQ ID NOs: 1-584. Expression of these sequences may be detected alone or in combination with other transcripts. In some embodiments, an array is used which comprises a wide range of sensor probes for thyroid-specific expression products, along with appropriate control sequences. An array of interest is the Human Exon 1.0 ST Array (HuEx 1.0 ST, Affymetrix, Inc., Santa Clara, Calif.).
Typically the polynucleotide probes are attached to a solid substrate and are ordered so that the location (on the substrate) and the identity of each are known. The polynucleotide probes can be attached to one of a variety of solid substrates capable of withstanding the reagents and conditions necessary for use of the array. Examples include, but are not limited to, polymers, such as (poly)tetrafluoroethylene, (poly)vinylidenedifluoride, polystyrene, polycarbonate, polypropylene and polystyrene; ceramic; silicon; silicon dioxide; modified silicon; (fused) silica, quartz or glass; functionalized glass; paper, such as filter paper; diazotized cellulose; nitrocellulose filter; nylon membrane; and polyacrylamide gel pad. Substrates that are transparent to light are useful for arrays that will be used in an assay that involves optical detection.
Examples of array formats include membrane or filter arrays (for example, nitrocellulose, nylon arrays), plate arrays (for example, multiwell, such as a 24-, 96-, 256-, 384-, 864- or 1536-well, microtitre plate arrays), pin arrays, and bead arrays (for example, in a liquid “slurry”). Arrays on substrates such as glass or ceramic slides are often referred to as chip arrays or “chips.” Such arrays are well known in the art. In one embodiment of the present invention, the thyroid classification array is a chip.
Data Analysis
Array data can be managed and analyzed using techniques known in the art. The Genetrix suite of tools can be used for microarray analysis (Epicenter Software, Pasadena, Calif.). Probe set modeling and data pre-processing can be derived using the Robust Multi-Array (RMA) algorithm or variant GC-RMA, Probe Logarithmic Intensity Error (PLIER) algorithm or variant iterPLIER. Variance or intensity filters can be applied to pre-process data using the RMA algorithm, for example by removing target sequences with a standard deviation of <10 or a mean intensity of <100 intensity units of a normalized data range, respectively.
In some embodiments, one or more pattern recognition methods can be used in analyzing the expression level of target sequences. The pattern recognition method can comprise a linear combination of expression levels, or a nonlinear combination of expression levels. In some embodiments, expression measurements for RNA transcripts or combinations of RNA transcript levels are formulated into linear or non-linear models or algorithms (i.e., an ‘expression signature’) and converted into a likelihood score. This likelihood score indicates the probability that a biological sample is from malignant thyroid nodule disease or benign disease. The likelihood score can be used to distinguish malignant from benign thyroid nodule disease. The models and/or algorithms can be provided in machine readable format, and may be used to correlate expression levels or an expression profile with a disease state, and/or to designate a treatment modality for a patient or class of patients.
Thus, results of the expression level analysis can be used to correlate increased expression of one or more target sequences in group I (or a subset thereof) and/or decreased expression of one or more target sequences in group II (or a subset thereof) with thyroid cancer, and to designate a treatment modality selected from total thyroidectomy, radioactive iodine treatment, and a combination thereof. Patients with benign disease would be candidates for watchful waiting (careful monitoring at regular intervals), thyroid hormone suppression therapy (treating with levothyroxine or other synthetic forms of thyroxine) to shrink the nodule, radioactive iodine to treat hyperfunctioning adenomas or multinodular goiters and surgery. For benign patients surgical management is much more limited to either cosmetic debulking procedures or only partial thyroidectomy leaving thyroid function largely intact. In contrast, the usual treatment for malignant nodules is surgical removal using more aggressive approaches such as near-total or total thyroidectomy followed by radioactive iodine ablation therapy and permanent thyroid hormone replacement therapy. Results of the expression level analysis can be used to correlate increased expression of one or more target sequences in group II (or a subset thereof) and/or decreased expression of one or more target sequences in group I (or a subset thereof) with benign disease, and to designate a treatment modality selected from near-total thyroidectomy, partial thyroidectomy, or watchful-waiting. The preferred treatment regimen for benign or non-neoplastic disease is observation.
Factors known in the art for diagnosing and/or suggesting, selecting, designating, recommending or otherwise determining a course of treatment for a patient or class of patients suspected of having thyroid disease can be employed in combination with measurements of the target sequence expression. These techniques include FNAB cytology and classification, ultrasound analysis, MRI results, CT scan results, thyroid scans, and measurements of thyroid hormone levels.
For example, factors which may be used to indicate a benign condition include a family history of Hashimoto's thyroiditis, of benign thyroid nodule, or of goiter, symptoms of hyper- or hypothyroidism, pain or tenderness associated with a nodule, a nodule that is soft, smooth and mobile, a multinodular goiter without a predominant nodule, a nodule that is “warm” on a thyroid scan, or an ultrasound indication of a simple cyst structure.
Factors which may be used to indicate a malignant thyroid condition include patient age less than 20 or greater than seventy, male gender, new onset of swallowing difficulties or hoarseness, a history of external neck irradiation, a nodule that is firm, irregular and fixed, cervical lymphadenopathy, a history of thyroid cancer, a nodule that is “cold” on a thyroid scan, and a solid or complex morphology seen on ultrasound.
Certified tests for classifying thyroid disease status and/or designating treatment modalities are also provided. A certified test comprises a means for characterizing the expression levels of one or more of the target sequences of interest, and a certification from a government regulatory agency endorsing use of the test for classifying the thyroid disease status of a biological sample.
In some embodiments, the certified test may comprise reagents for amplification reactions used to detect and/or quantitate expression of the target sequences to be characterized in the test. An array of probe nucleic acids can be used, with or without prior target amplification, for use in measuring target sequence expression.
The test is submitted to an agency having authority to certify the test for use in distinguishing benign from malignant thyroid tissues. Results of detection of expression levels of the target sequences used in the test and correlation with disease status and/or outcome are submitted to the agency. A certification authorizing the diagnostic and/or prognostic use of the test is obtained.
Also provided are portfolios of expression levels comprising a plurality of normalized expression levels of the target sequences described herein, including SEQ ID NOs:1-584. Such portfolios may be provided by performing the methods described herein to obtain expression levels from an individual patient or from a group of patients. The expression levels can be normalized by any method known in the art; exemplary normalization methods that can be used in various embodiments include Robust Multichip Average (RMA), probe logarithmic intensity error estimation (PLIER), non-linear fit (NLFIT) quantile-based and nonlinear normalization, and combinations thereof. Background correction can also be performed on the expression data; exemplary techniques useful for background correction include mode of intensities, normalized using median polish probe modeling and sketch-normalization.
In some embodiments, portfolios are established such that the combination of genes in the portfolio exhibit improved sensitivity and specificity relative to known methods. In considering a group of genes for inclusion in a portfolio, a small standard deviation in expression measurements correlates with greater specificity. Other measurements of variation such as correlation coefficients can also be used in this capacity. The invention also encompasses the above methods where the specificity is at least about 50% and at least about 60%. The invention also encompasses the above methods where the sensitivity is at least about 90%.
The gene expression profiles of each of the target sequences comprising the portfolio can fixed in a medium such as a computer readable medium. This can take a number of forms. For example, a table can be established into which the range of signals (e.g., intensity measurements) indicative of disease is input. Actual patient data can then be compared to the values in the table to determine whether the patient samples are normal, benign or diseased. In a more sophisticated embodiment, patterns of the expression signals (e.g., fluorescent intensity) are recorded digitally or graphically.
Comparisons can also be used to determine whether the patient is not likely to experience the disease. The expression profiles of the samples are then compared to a control portfolio. If the sample expression patterns are consistent with the expression pattern for cancer then (in the absence of countervailing medical considerations) the patient is treated as one would treat a thyroid cancer patient. If the sample expression patterns are consistent with the expression pattern from the normal/control cell then the patient is diagnosed negative for cancer.
Genes can be grouped so that information obtained about the set of genes in the group can be used to make or assist in making a clinically relevant judgment such as a diagnosis, prognosis, or treatment choice.
A patient report is also provided comprising a representation of measured expression levels of a plurality of target sequences in a biological sample from the patient, wherein the representation comprises expression levels of target sequences corresponding to any one, two, three, four, five, six, eight, ten, twenty, thirty, fifty or more of the target sequences depicted in SEQ ID NOs: 1-584, or of the subsets described herein, or of a combination thereof. In some embodiments, the representation of the measured expression level(s) may take the form of a linear or nonlinear combination of expression levels of the target sequences of interest. The patient report may be provided in a machine (e.g., a computer) readable format and/or in a hard (paper) copy. The report can also include standard measurements of expression levels of said plurality of target sequences from one or more sets of patients with known thyroid status and/or outcome. The report can be used to inform the patient and/or treating physician of the expression levels of the expressed target sequences, the likely medical diagnosis and/or implications, and optionally may recommend a treatment modality for the patient.
Also provided are representations of the gene expression profiles useful for treating, diagnosing, prognosticating, and otherwise assessing disease. In some embodiments, these profile representations are reduced to a medium that can be automatically read by a machine such as computer readable media (magnetic, optical, and the like). The articles can also include instructions for assessing the gene expression profiles in such media. For example, the articles may comprise a readable storage form having computer instructions for comparing gene expression profiles of the portfolios of genes described above. The articles may also have gene expression profiles digitally recorded therein so that they may be compared with gene expression data from patient samples. Alternatively, the profiles can be recorded in different representational format. A graphical recordation is one such format. Clustering algorithms can assist in the visualization of such data.
Kits
Kits for performing the desired method(s) are also provided, and comprise a container or housing for holding the components of the kit, one or more vessels containing one or more nucleic acid(s), and optionally one or more vessels containing one or more reagents. The reagents include those described in the composition of matter section above, and those reagents useful for performing the methods described, including amplification reagents, and may include one or more probes, primers or primer pairs, enzymes (including polymerases and ligases), intercalating dyes, labeled probes, and labels that can be incorporated into amplification products.
In some embodiments, the kit comprises primers or primer pairs specific for those subsets and combinations of target sequences described herein. At least two, three, four or five primers or pairs of primers suitable for selectively amplifying the same number of target sequence-specific polynucleotides can be provided in kit form. In some embodiments, the kit comprises from five to fifty primers or pairs of primers suitable for amplifying the same number of target sequence-representative polynucleotides of interest.
The reagents may independently be in liquid or solid form. The reagents may be provided in mixtures. Control samples and/or nucleic acids may optionally be provided in the kit. Control samples may include tissue and/or nucleic acids obtained from or representative of benign thyroid tissue, as well as tissue and/or nucleic acids obtained from or representative of malignant thyroid tissue.
The nucleic acids may be provided in an array format, and thus an array or microarray may be included in the kit. The kit optionally may be certified by a government agency for use in classifying the disease status of thyroid tissue and/or for designating a treatment modality.
Instructions for using the kit to perform one or more methods of the invention can be provided with the container, and can be provided in any fixed medium. The instructions may be located inside or outside the container or housing, and/or may be printed on the interior or exterior of any surface thereof. A kit may be in multiplex form for concurrently detecting and/or quantitating one or more different target polynucleotides representing the expressed target sequences.
Devices
Devices useful for performing methods of the invention are also provided. The devices can comprise means for characterizing the expression level of a target sequence of the invention, for example components for performing one or more methods of nucleic acid extraction, amplification, and/or detection. Such components may include one or more of an amplification chamber (for example a thermal cycler), a plate reader, a spectrophotometer, capillary electrophoresis apparatus, a chip reader, and or robotic sample handling components. These components ultimately can obtain data that reflects the expression level of the target sequences used in the assay being employed.
The devices may include an excitation and/or a detection means. Any instrument that provides a wavelength that can excite a species of interest and is shorter than the emission wavelength(s) to be detected can be used for excitation. Commercially available devices can provide suitable excitation wavelengths as well as suitable detection components.
Exemplary excitation sources include a broadband UV light source such as a deuterium lamp with an appropriate filter, the output of a white light source such as a xenon lamp or a deuterium lamp after passing through a monochromator to extract out the desired wavelength(s), a continuous wave (cw) gas laser, a solid state diode laser, or any of the pulsed lasers. Emitted light can be detected through any suitable device or technique; many suitable approaches are known in the art. For example, a fluorimeter or spectrophotometer may be used to detect whether the test sample emits light of a wavelength characteristic of a label used in an assay.
The devices typically comprise a means for identifying a given sample, and of linking the results obtained to that sample. Such means can include manual labels, barcodes, and other indicators which can be linked to a sample vessel, and/or may optionally be included in the sample itself, for example where an encoded particle is added to the sample. The results may be linked to the sample, for example in a computer memory that contains a sample designation and a record of expression levels obtained from the sample. Linkage of the results to the sample can also include a linkage to a particular sample receptacle in the device, which is also linked to the sample identity.
The devices also comprise a means for correlating the expression levels of the target sequences being studied with a classification of thyroid disease. Such means may comprise one or more of a variety of correlative techniques, including lookup tables, algorithms, multivariate models, and linear or nonlinear combinations of expression models or algorithms. The expression levels may be converted to one or more likelihood scores, reflecting the likelihood that the sample comprises malignant tissue and/or the likelihood that the sample comprises benign tissue. The models and/or algorithms can be provided in machine readable format, and can optionally further designate a treatment modality for a patient or class of patients
The device also comprises output means for outputting the thyroid disease status and/or a treatment modality. Such output means can take any form which transmits the results to a patient and/or a healthcare provider, and may include a monitor, a printed format, or both. The device may use a computer system for performing one or more of the steps provided.
CITATIONS


    1: Griffith O L, et al., “Meta-analysis and meta-review of thyroid cancer gene expression profiling studies identifies important diagnostic biomarkers,” J Clin Oncol. 2006 Nov. 1, 24(31):5043-51.
    2: Puskas L G, et al., “Gene profiling identifies genes specific for well-differentiated epithelial thyroid tumors,” Cell Mol Biol (Noisy-le-grand), 2005 Sep. 5, 51(2):177-86.
    3: Fujarewicz K, et al., “A multi-gene approach to differentiate papillary thyroid carcinoma from benign lesions: gene selection using support vector machines with bootstrapping,” Endocr Relat Cancer. 2007 September, 14(3):809-26.
    4: Kebebew E, et al., “Diagnostic and extent of disease multigene assay for malignant thyroid neoplasms,” Cancer. 2006 Jun. 15, 106(12):2592-7.
    5: Finley D J, et al., “Discrimination of benign and malignant thyroid nodules by molecular profiling,” Ann Surg. 2004 September, 240(3):425-36; discussion 436-7.
    6: Mazzanti C, et al., “Using gene expression profiling to differentiate benign versus malignant thyroid tumors,” Cancer Res. 2004 Apr. 15; 64(8):2898-903. Erratum in: Cancer Res. 2004 Jul. 15, 64(14):5028.
    7: Finley D J, et al., “Advancing the molecular diagnosis of thyroid nodules: defining benign lesions by molecular profiling,” Thyroid. 2005 June; 15(6):562-8.
    8: Cerutti J M, et al., “Diagnosis of suspicious thyroid nodules using four protein biomarkers,” Clin Cancer Res. 2006 Jun. 1; 12(11 Pt 1):3311-8.
    9: Fryknäs M, et al., “Molecular markers for discrimination of benign and malignant follicular thyroid tumors,” Tumour Biol. 2006; 27(4):211-20.
    10: Hamada A, et al., “Diagnostic usefulness of PCR profiling of the differentially expressed marker genes in thyroid papillary carcinomas,” Cancer Lett. 2005 Jun. 28, 224(2):289-301.
    11: Yukinawa N, et al., “A multi-class predictor based on a probabilistic model: application to gene expression profiling-based diagnosis of thyroid tumors,” BMC Genomics. 2006 Jul. 27, 7:190.
    12: Griffiths O L, et al., “Biomarker panel diagnosis of thyroid cancer: a critical review,” Expert Rev. Anticancer Therapy. 2008 September, 8(9): 1399-1413.
    13. Prasad N B, et al., “Identification of Genes Differentially Expressed in Benign versus Malignant Thyroid Tumors,” Clinical Cancer Res. 2008 Jun. 1, 14(11):3327-37.
    14. Shibru D, et al., “Does the 3-gene diagnostic assay accurately distinguish benign from malignant thyroid neoplasms?” Cancer. 2008 Sep. 1; 113(5):930-5.


To gain a better understanding of the invention described herein, the following examples are set forth. It will be understood that these examples are intended to describe illustrative embodiments of the invention and are not intended to limit the scope of the invention in any way. Efforts have been made to ensure accuracy with respect to numbers used (e.g., amounts, temperature, etc.) but some experimental error and deviation should be accounted for. Unless otherwise indicated, parts are parts by weight, temperature is degree centigrade and pressure is at or near atmospheric, and all materials are commercially available.
EXAMPLES
Materials and Methods:
Tissue Samples.
Sixty (60) formalin-fixed paraffin embedded (FFPE) surgical specimens of human thyroid nodule disease were collected from patients at the Department of Surgery, St. Paul's Hospital (Vancouver, BC, Canada) according to an institutional review board-approved protocol. For a subset of 13 surgical specimens, fine-needle aspirate cell blocks were also available. For surgical specimens, a tissue microarrayer (Beecher Instruments, Silver Spring, Md.) was used to core each FFPE surgical resected specimen once with either a 0.6 mm or 1.0 diameter cylinder (‘FFPE TMA’). Surgical resected samples from 60 patients were evaluated. These samples were divided into three subsets consisting of training (n=30) and testing (n=20) subsets used to select for differentially expressed RNA probe sets and a follicular testing subset (n=10) consisting of difficult to diagnose follicular pattern lesions from patients with an fine needle aspiration biopsy (FNAB) diagnosis of suspicious for cancer.
Extraction of RNA.
RNA was extracted and purified from the FFPE TMA cores using a modified protocol for the commercially available Formapure nucleic acid extraction kit (Agencourt Biosciences, Beverly Mass.) adopted to process small amounts of input tissue. Principal modifications to the kit protocol included preheating the lysis buffer to 70° C. before immersing the FFPE sections in a reduced amount of lysis buffer (to increase concentration of lysate) and then subjecting FFPE lysates to incubation at 99° C. for 1 min. In addition, FFPE samples were incubated with Proteinase K (20 ul of 40 mg/mL) for an extended 16 hrs in a water bath at 55° C. RNA was further purified using DNAse I treatment (Ambion, Austin, Tex.) to eliminate any contaminating DNA. RNA was eluted with 300 ul of RNAse-free water and subsequently concentrated and purified using sodium acetate precipitation and a series of ethanol washes and resuspended in 15 ul of water. RNA concentrations were calculated using a Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies, Rockland, Del.). These additional purification steps significantly improved the yield of amplified material in subsequent steps described below. RNA integrity was evaluated by running electropherograms and RNA integrity number, RIN (a correlative measure that indicates intactness of mRNA) was determined using the RNA 6000 PicoAssay for the Bioanalyzer 2100 (Agilent Technologies, Santa Clara, Calif.). Sufficient RNA (75 ng) was obtained using these procedures from all 60 FFPE TMA.
Nucleic Acid Amplification and GeneChip Hybridization.
Purified RNA was subjected to whole-transcriptome amplification using the WT-Ovation FFPE system including the WT-Ovation Exon and FL-Ovation Biotin V2 labeling modules, with the following modifications. Seventy-five (75) nanograms of RNA extracted from FFPE TMA cores or FNAB cell blocks was used to generate amplified Ribo-SPIA product. For the WT-Ovation Exon sense-target strand conversion kit 4 ug of Ribo-SPIA product were used. Between 2.5 and 5 micrograms of WT-Ovation Exon product were used to fragment and label using the FL-Ovation Biotin V2 labeling module and labeled product was hybridized to Affymetrix Human Exon 1.0 ST GeneChips following manufacturer's recommendations (Affymetrix, Santa Clara, Calif.).
Microarray Analysis.
All data management and analysis was conducted using the Genetrix suite of tools for microarray analysis (Epicenter Software, Pasadena, Calif.). Probe set modeling and data pre-processing were derived using the iterPlier algorithm (Affymetrix, Santa Clarita, Calif.). The mode of intensity values was used for background correction and sketch was used for normalization and probe modeling used a median polish routine. Outlier samples were identified by evaluating the median absolute deviation of the normalized expression values in each sample and 25th percentile outlier samples. Two samples from the testing cohort were removed from further analysis because they were clearly outliers using both quality control metrics described above. Table 1 shows the composition of the subsets used in the analysis (for samples that passed microarray QC) and segregates specimens by the results of the original FNAB cytology diagnosis as well as the ‘gold-standard’ pathology review diagnosis (obtained from careful dissection and histopathological analysis of specimens after thyroidetomy procedures). A variance filter was applied to data pre-processed using the iterPlier algorithm, by removing probe set regions (PSRs) with a mean intensity of <10 intensity units of a normalized data range. PSRs are comprised of an average of four individual probes that interrogate the expression of RNA transcripts or portions thereof. PSR annotations and the sequences (RNAs) that they interrogate were downloaded from the Affymetrix website. An additional filter employed was to remove PSRs with known cross-hybridization properties (i.e., significant homology to more than one transcript from different genes or loci), leaving 1,134,588 PSRs for further analysis. Cross-hybridization properties of PSRs were downloaded from the Affymetrix website.
Example 1. Identification of PSRs Differentially Expressed in Benign and Malignant Thyroid in the Training Subset
Supervised expression profiling was performed using t-tests and mean-fold difference criteria to determine differential expression of RNAs in the training cohort of 30 specimens between samples classified as malignant and benign thyroid nodule disease by review pathology. In the training cohort, 10 of the specimens were definitively diagnosed by FNAB cytology as benign disease (e.g., goiter), 8 of the specimens were definitively diagnosed by FNAB cytology as malignant disease (e.g., papillary carcinoma) and all 18 of these specimens were confirmed the same upon histological review pathology of surgical specimens after thyroidectomy. The remaining 12 samples evaluated in the training cohort were indeterminate by FNAB cytology, but 7 were confirmed malignant and 5 confirmed benign thyroid nodule disease upon histological review diagnosis of the surgical specimens. The final histological review diagnosis of the FFPE surgical specimen was the variable used to select for differentially expressed target sequences.
Using supervised selection criteria of at least 3-fold mean difference in expression (between malignant and benign groups) and t-test p value cut-off of p<0.0001, 242 RNAs were found at increased expression in malignant samples as compared to benign samples, while 342 found at increased expression in benign samples as compared to malignant samples (RNA forms of the sequences are depicted in SEQ ID NOs: 1-584).
A detailed literature review was conducted and identified 68 genes differentially expressed between malignant and benign thyroid tissue (see citations #1-15 supra). On the Affymetrix Human Exon 1.0 microarray these 68 genes are represented by 766 exonic target sequences. Analysis of the overlap between target sequences from the literature review and the 584 identified as having at least 3-fold difference in expression in the training subset indicated an overlap of only 67 PSRs (SEQ ID NOs: 44, 46, 48, 56, 63, 79, 81, 83, 86, 87, 89, 96, 98, 99, 103, 106, 109, 114, 115, 119, 121, 126, 127, 130, 138, 145, 146, 151, 157, 159-161, 165, 172, 174, 178, 181, 183, 188, 190, 196, 198, 202, 203, 205, 206, 219, 223, 230, 234, 244, 249, 255, 260, 274, 275, 300, 302, 314, 324, 326, 368, 369, 393, 403, 514 and 517). This relatively small overlap indicates that most of the differentially expressed target sequences characterized in the training subset analysis have not be previously characterized as being differentially expressed between benign and malignant thyroid nodule disease samples.
Previous reports have demonstrated genome-wide expression profiling using primarily fresh or frozen specimens, which are not routinely available in the clinic and are logistically difficult to transport to external laboratories for analysis (i.e., require flash freezing with liquid nitrogen and transport on dry ice). In contrast, the approach used in this Example allows for genome-wide expression profiling of more widely available FFPE thyroid nodule disease surgical specimens and FNAB cell blocks and demonstrates that this approach can successfully generate high-resolution whole-transcriptome expression data from the more fragmented RNA extracted from these routine clinical specimens. Moreover, this approach has identified target sequences that can be detected in such routine clinical specimens thereby providing for a diagnostic method that is broadly applicable and is not dependent on the availability of fresh or frozen specimens.
In addition to robust profiling from FFPE specimens, the use of Human Exon microarrays, which report relative expression of genes on the exon level, provided a higher resolution view of the transcriptome and allowed detection of differentially expressed RNA species that can not be detected with 3′ biased gene-level microarrays (e.g., U133 Plus 2.0 GeneChips). FIG. 1 shows a pie chart of the types of RNA species comprised by the 584 RNAs selected in the present Example as differentially expressed in the training subset. These species can be seen to include not only exonic RNA species, but also intronic, promoter and antisense RNA species. In fact, a minority of the RNAs selected are from protein-encoding exons of genes that are represented in gene-level microarrays and the majority (70%) of the RNAs selected represent RNA sequences that are not profiled with gene-level microarray technology. This data demonstrates that gene-level analysis (e.g., using 3′ biased microarrays such as U133 Plus 2.0) can miss important differences in transcription such as intron retention, alternative splicing or exon usage and non-coding (i.e., translated into protein) RNA expression or strand-specific expression observed in this type of whole-transcriptome analysis. In particular, non-coding RNA—the predominant RNA species (over 90% of the transcription in the genome)—represent functional RNA molecules that could convey key differences between pathological conditions through regulatory roles of protein-encoding gene expression. This is a potentially rich source of diagnostic information that cannot be captured by solely observing differences in protein-encoding gene expression or protein biomarker expression and may facilitate the diagnosis of specific pathological conditions of clinical importance, such as malignant vs. benign in thyroid nodule disease.
Example 2. Validation of Selected RNAs and Identification of Minimal Diagnostic Expression Signatures
In order to identify a minimal expression signature capable of distinguishing malignant from benign thyroid nodule disease, the Nearest Shrunken Centroids (NSC) algorithm was employed as previously described (Davicioni et al., Molecular Classification of Rhabdomyosarcoma: Genotypic and Phenotypic Determinants of Diagnosis, American Journal of Pathology, 2009) on the 584 RNAs selected in the training subset (n=30) analysis. Using the NSC algorithm, a 10-RNA expression signature (Table 3) was identified in the testing subset (n=18) that independently discriminates the benign and malignant samples. A separate NSC algorithm analysis was implemented on the follicular lesion testing subset (n=10) and identified a 6-RNA minimal expression signature (Table 4) for discriminating benign and malignant disease (as definitively diagnosed by the surgical pathology review) from these follicular pattern lesions which in the clinic all fall into the ‘indeterminate’ diagnostic or ‘suspicious for cancer’ categories. Of note, out of these 16 RNAs, only one of these sequences (SEQ ID NO:5) is known to overlap with the protein-coding mRNA of a gene, it however is transcribed antisense to the gene.
Next, the expression levels of these 10- and 6-RNA signatures were summarized (for each of the 58 patients evaluated in the three subsets) into a ‘metagene’ by taking the expression level and multiplying it by a weighting factor for each PSR in the metagene signature and combining these values into a single variable. Weighting factors were derived from the signed log of the p value from the test statistic coefficients from a t-test for significance of differential expression in the training subset (Tables 3 and 4). Patient Outcome Predictor (‘POP’) scores were then generated from the metagene values for each patient by scaling and normalizing the metagene scores within a range of 0 to 100. The interquartile range of POP scores generated from metagenes for benign and malignant specimens is shown separately for specimens definitively diagnosed by FNAB cytology and those that were indeterminate or suspicious for cancer (FIGS. 3A and B).
In FIG. 3A, it can be observed that POP scores generated from the 10-RNA metagene did not overlap at all between benign and malignant thyroid nodule disease specimens (p<6e-18). The performance of this metagene in terms of distinguishing indeterminate diagnoses was also highly significant, although diminished in comparison (p<1.3e-5). In contrast, the POP scores generated from the “follicular” 6-RNA metagene separated indeterminate diagnoses slightly better as the interquartile ranges for true malignant specimens was tighter (p<6.9e-6) (FIG. 3B).
Using an arbitrary POP score cut-off value of <50 to indicate a patient with benign thyroid nodule disease and a cut-off value of ≧50 points to indicate a patient with malignant thyroid nodule disease, 2×2 contingency (‘truth tables’) reveal that both these expression signatures are highly accurate discriminators when compared to the ‘gold-standard’ histological review pathology diagnosis post-thryoidectomy. The sensitivity (82%) and specificity (100%) were equivalent for both the 10- and 6-RNA metagenes when evaluating POP scores for all specimens (Table 5). The specificity was maintained at 100% when evaluating only the FNAB indeterminate cytology specimens as a separate group but the sensitivity (64%) decreased and was less than that observed in the combined analysis of all specimens (Table 6). The high specificity shown by both the 10- and 6-RNA metagenes is significant in that it provides for a diagnostic assay with a very low false-positive rate. The overall accuracy in both groups, however, was significant: 91% in all specimens and 83% in indeterminate cytology specimens. Receiver-operator curves (ROC) for the 10- and 6-RNA metagene POP scores are depicted in FIGS. 3A and B, respectively, and show that the area-under-the curve was above 95% in both cases. These ROC results are significant because they demonstrate in the absence of a specified cut-off value for POP scores that they are performing extremely well as a diagnostic test for malignant thyroid nodule disease.
Although the invention has been described with reference to certain specific embodiments, various modifications thereof will be apparent to those skilled in the art without departing from the spirit and scope of the invention. All such modifications as would be apparent to one skilled in the art are intended to be included within the scope of the following claims.







TABLE 1







The composition of specimens used in the training and testing subsets


for the microarray analyses. The specimens are separated into the


major diagnostic categories relevant to clinical practice. Thyroid


nodule disease specimens that were indeterminate or suspicious


for cancer on the original FNAB and the definitive diagnosis


as determined by surgical pathology review after thyroidectomy.











FNAB
Surgical Pathology




Cytology Diagnostic
Dx












Category
Benign
Malignant










Training Subset











Benign
10
0



Indeterminate/Suspicious
5
7



Cancer
0
8







Testing Subset











Benign
5
0



Indeterminate/Suspicious
5
2



Cancer
0
6







Follicular Lesion Testing Subset











Benign
0
0



Indeterminate/Suspicious
5
5



Cancer
0
0
















TABLE 2







Examples of Suitable Genes for Inclusion in a


Thyroid Classification Library








Gene



Symbol
Gene










Thyroid Malignant - Increased Expression








ADORA1
adenosine A1 receptor


CCL18
chemokine (C-C motif) ligand 18 (pulmonary and



activation-regulated)


CCND1
cyclin D1


CD44
CD44 molecule (Indian blood group)


CDH3
cadherin 3, type 1, P-cadherin (placental)


CITED1
Cbp/p300-interacting transactivator, with Glu/Asp-rich



carboxy-terminal domain, 1


DPP4
dipeptidyl-peptidase 4 (CD26, adenosine deaminase



complexing protein 2)


DUSP6
dual specificity phosphatase 6


ENTPD1
ectonucleoside triphosphate diphosphohydrolase 1


EPS8
epidermal growth factor receptor pathway substrate 8


ETV5
Ets variant gene 5 (ets-related molecule)


MPZL2
myelin protein zero-like 2


FN1
fibronectin 1


GJB3
gap junction protein, beta 3, 31 kDa


GABBR2
gamma-aminobutyric acid (GABA) B receptor, 2


HBB
hemoglobin, beta


HLA-
major histocompatibility complex, class II, DM alpha


DMA



HLA-
major histocompatibility complex, class II, DQ alpha 1


DQA1



HMGA2
high mobility group AT-hook 2


ICAM1
intercellular adhesion molecule 1 (CD54), human rhinovirus



receptor


IGSF1
immunoglobulin superfamily, member 1


IL13RA1
interleukin 13 receptor, alpha 1


ENDOD1
endonuclease domain containing 1


KRT19
keratin 19


LGALS3
lectin, galactoside-binding, soluble, 3


LRP4
low density lipoprotein receptor-related protein 4


MET
met proto-oncogene (hepatocyte growth factor receptor)


MKRN2
makorin, ring finger protein, 2


MRC2
mannose receptor, C type 2


MTMR4
myotubularin related protein 4


P4HA2
procollagen-proline, 2-oxoglutarate 4-dioxygenase (proline



4-hydroxylase) alpha polypeptide II


PHLDA2
pleckstrin homology-like domain, family A, member 2


PROS1
protein S (alpha)


PRSS23
protease, serine, 23


PSD3
pleckstrin and Sec7 domain containing 3


QPCT
glutaminyl-peptide cyclotransferase (glutaminyl cyclase)


RXRG
retinoid X receptor, gamma


SCG5
secretogranin V (7B2 protein)


SDC4
syndecan 4


SERPINA1
serpin peptidase inhibitor, clade A (alpha-1 antiproteinase,



antitrypsin), member 1


SFTPB
surfactant, pulmonary-associated protein B


SPOCK1
sparc/osteonectin, cwcv and kazal-like domains



proteoglycan (testican) 1


ST14
suppression of tumorigenicity 14 (colon carcinoma)


SYN1
synapsin I


TGFA
transforming growth factor, alpha


TIMP1
TIMP metallopeptidase inhibitor 1


TUSC3
tumor suppressor candidate 3







Thyroid Benign - Increased Expression








BCL2
BCL2-antagonist of cell death


CDH16
cadherin 16, KSP-cadherin


COL9A3
collagen, type IX, alpha 3


CRABP1
cellular retinoic acid binding protein 1


CSNK1G2
casein kinase 1, gamma 2


DIO1
deiodinase, iodothyronine, type I


FABP4
fatty acid binding protein 4, adipocyte


FCGBP
Fc fragment of IgG binding protein


FCGRT
Fc fragment of IgG, receptor, transporter, alpha


HBA2
hemoglobin, alpha 2


ID4
inhibitor of DNA binding 4, dominant negative helix-loop-



helix protein


ITPR1
inositol 1,4,5-triphosphate receptor, type 1


KCNAB1
potassium voltage-gated channel, shaker-related subfamily,



beta member 1


MPPED2
metallophosphoesterase domain containing 2


MT1G
metallothionein 1G


MT1X
metallothionein 1X


MTF1
metal-regulatory transcription factor 1


RAB23
RAB23, member RAS oncogene family


RGS16
regulator of G-protein signaling 16


TFF3
trefoil factor 3 (intestinal)


TPO
thyroid peroxidase
















TABLE 3







A subset of PSRs used to generate a 10-RNA metagene. These PSRs were identified based on differentially expression in a


training subset of thyroid nodule disease specimens (Table 1), selected using an independent testing subset of thyroid


nodule diseasespecimens with the Nearest Shrunken Centroids algorithm. Indicated are the Affymetrix PSR ID, mean-


fold difference in expression (FD), positive and negative weighting factors, which indicate increased expression in


malignant and benign samples, respectively. Also noted are the location of the PSRs in the genome and their location


relative to the closest annotated gene as well as whether or not the RNA sequence targeted by the PSR overlaps with


the protein-coding sequence of the gene. These PSRs were used to derive the 10-RNA metagenes by taking a linear


combination of expression measurements multiplied by weighting factors and generate POP scores as


depicted in FIG. 3A.























Probeset



Affymetrix






Overlaps


SEQ ID
ID
FD
Weights
CHR
Strand
Location
Proximal Gene
CDS


















1
3536736
8.8
5.0
14
+
In INTRON
Lectin, galactoside-binding, soluble, 3
FALSE








#4


2
3460518
19.0
7.4
12
−
In INTRON
High mobility group AT-hook 2
FALSE








#3


3
2526817
11.1
6.5
2
+
In INTRON
Fibronectin 1
FALSE








#40


4
3420374
10.4
6.0
12
+
In INTRON
High mobility group AT-hook 2
FALSE








#3


5
3976358
10.3
6.9
X
+
In INTRON
Synapsin I
TRUE








#5


6
2828473
7.7
8.9
5
+
In EXON #7
PDZ and LIM domain 4
FALSE


7
3693001
−12.5
−6.3
16
−
In EXON #3
Metallothionein 1G
FALSE


8
2508453
−8.3
−6.9
2
+
In INTRON
Low density lipoprotein-related protein
FALSE








#1
1B (deleted in tumors)


9
2537610
−6.7
−8.5
2
−
In INTRON
Thyroid peroxidase
FALSE








#15


10
2573597
−6.3
−7.4
2
−
4,295 3′
Transcription factor CP2-like 1
FALSE
















TABLE 4







A subset of PSRs used to generate a 6-RNA metagene. These PSRs were identified based on differentially


expression in a training subset of thyroid nodule disease specimens (Table 1), selected using an independent


testing subset of thyroid nodule disease specimens with the Nearest Shrunken Centroids algorithm. Indicated


are the Affymetrix PSR ID, mean-fold difference in expression (FD), positive and negative weighting factors,


which indicate increased expression in malignant and benign samples, respectively. Also noted are the


location of the PSRs in the genome and their location relative to the closest annotated gene as well as


whether or not the RNA sequence targeted by the PSR overlaps with the protein-coding sequence of the


gene. These PSRs were used to derive the 6-RNA metagenes by taking a linear combination of


expression measurements multiplied by weighting factors and generate POP scores as depicted


in FIG. 3B.























Probeset


SEQ
Affymetrix






Overlaps


ID
ID
FD
Weights
CHR
Strand
Location
Proximal Gene
CDS


















1
3536736
8.8
5.0
14
+
In INTRON
Lectin, galactoside-
FALSE








#4
binding, soluble, 3


11
2830183
10.4
3.6
5
+
In EXON
Sparc/osteonectin, cwcv
FALSE








#11
and kazal-like domains









proteoglycan (testican) 1


12
2491744
9.1
4.0
2
+
In INTRON
Surfactant, pulmonary-
FALSE








#10
associated protein B


13
3329532
4.0
4.3
11
+
In INTRON
Low density lipoprotein
FALSE








#2
receptor-related protein 4


14
3104982
−20.0
−5.3
8
+
In EXON #4
Fatty acid binding protein
FALSE









4, adipocyte


15
3913484
−20.0
−4.1
20
−
In EXON
Collagen, type IX, alpha 3
FALSE








#32
















TABLE 5





2 × 2 contigency table comparing the ‘gold-standard’ pathology review


diagnosis of 58 thyroid nodule disease specimens with that of the POP


scores using a cut-off ≧50 score for malignant disease and <50 score for


benign disease classification. Note that both the 10-and 6-RNA metagene


derived scores produced identical results using these cut-off criteria.


















Pathology Diagnosis















Malignant
Benign






POP
≧50
23
 0



Scores
<50
 5
30















 
95% CI






Sensitivity
82
(63-93)



Specificity
100
 (88-100)



Positive
100
 (85-100)



Predictive





Value





Negative
86
(69-95)



Predictive





Value





Accuracy
91




Likelihood
NaN
—



Ratio





Positive





Test





Likelihood
0.2
(0.08-0.39)



Ratio





Negative





Test
















TABLE 6





2 × 2 contigency table comparing the ‘gold-standard’ pathology review


diagnosis for 29 thyroid nodule disease specimens with that of the POP


scores using a cut-off ≧50 score for malignant disease and <50 score


for benign disease classification. This subset of specimens were all


‘indeterminate’ or ‘suspicious for cancer’ by FNAB cytology and could


not be definitively diagnosed until review pathology was performed on the


surgical specimens. Note that both the 10- and 6-RNA metagene derived


scores produced identical results using these cut-off criteria.


















Pathology Diagnosis















Malignant
Benign






POP
≧50
9
0



Scores
<50
5
15 















 
95% CI






Sensitivity
64
(35-87)



Specificity
100
 (78-100)



Positive
100
 (66-100)



Predictive





Value





Negative
75
(50-91)



Predictive





Value





Accuracy
83




Likelihood
NaN
—



Ratio





Positive





Test





Likelihood
0.36
(0.18-0.72)



Ratio





Negative





Test
















TABLE 7







Differentially expressed RNA transcripts identified from comparison tests described in


Example 2. CDS-RNA overlaps with the coding sequence translations of mRNAs in


RefSeq or GenBank databases, NC—‘non-coding’ RNA does not


overlap with the CDS.














SEQ
Fold





Overlaps


ID
Difference
Weights
CHR
Strand
Position
Proximal Gene
CDS

















1
8.8
5.0
14
+
intron
Lectin, galactoside-binding, soluble, 3
NC


2
19.0
7.4
12
−
intron/
High mobility group AT-hook 2
NC







antisense


3
11.1
6.5
2
+
intron/
Fibronectin 1
NC







antisense


4
10.4
6.0
12
+
intron
High mobility group AT-hook 2
NC


5
10.3
6.9
X
+
intron/
Synapsin I
CDS







antisense


6
7.7
8.9
5
+
exon
PDZ and LIM domain 4
NC


7
−12.5
−6.3
16
−
exon
Metallothionein 1G
NC


8
−8.3
−6.9
2
+
intron/
Low density lipoprotein-related protein
NC







antisense
1B (deleted in tumors)


9
−6.7
−8.5
2
−
intron/
Thyroid peroxidase
NC







antisense


10
−6.3
−7.4
2
−
extra-
Transcription factor CP2-like 1
NC







genic


11
10.4
3.6
5
+
exon
Sparc/osteonectin, cwcv and kazal-like
NC








domains proteoglycan (testican) 1


12
9.1
4.0
2
+
intron
Surfactant, pulmonary-associated protein B
NC


13
4.0
4.3
11
+
intron/
Low density lipoprotein receptor-related
NC







antisense
protein 4


14
−20.0
−5.3
8
+
exon
Fatty acid binding protein 4, adipocyte
NC


15
−20.0
−4.1
20
−
exon
Collagen, type IX, alpha 3
NC


16
65.6
6.5
5
+
exon
Solute carrier family 27 (fatty acid
CDS








transporter), member 6


17
61.3
7.8
13
+
extra-
Cysteinyl leukotriene receptor 2
NC







genic


18
46.1
6.4
1
+
exon
Chitinase 3-like 1 (cartilage glycoprotein-
NC








39)


19
34.0
7.2
19
−
exon/
Apolipoprotein C-I
NC







antisense


20
28.6
6.5
1
+
exon
Tumor-associated calcium signal
NC








transducer 2


21
27.7
6.7
1
+
intron
ST6 (alpha-N-acetyl-neuraminyl-2,3-beta-
NC








galactosyl-1,3)-N-acetylgalactosaminide








alpha-2,6-sialyltransferase 5


22
27.6
5.1
2
−
intron
Surfactant, pulmonary-associated protein B
NC


23
26.3
4.7
5
+
exon
Solute carrier family 27 (fatty acid
NC








transporter), member 6


24
26.0
5.6
2
−
intron
Fibronectin 1
NC


25
25.9
4.2
5
+
exon
Chemokine (C—X—C motif) ligand 14
NC


26
24.9
5.4
2
−
intron
Fibronectin 1
NC


27
23.4
5.6
3
−
extra-
Lipase, member H
NC







genic


28
22.6
7.2
2
−
exon
Surfactant, pulmonary-associated protein B
NC


29
20.9
4.4
2
−
intron
Surfactant, pulmonary-associated protein B
NC


30
20.7
7.0
3
+
extra-
Ecotropic viral integration site 1
NC







genic


31
20.6
6.5
1
−
extra-
Retinoid X receptor, gamma
NC







genic


32
20.4
4.3
5
−
exon
Chemokine (C—X—C motif) ligand 14
CDS


33
19.4
6.0
2
−
intron
Fibronectin 1
NC


34
18.9
6.2
1
−
exon
Tumor-associated calcium signal
NC








transducer 2


35
18.8
5.0
2
−
intron
Surfactant, pulmonary-associated protein B
NC


36
18.5
5.4
2
−
exon
Cytochrome P450, family 1, subfamily B,
NC








polypeptide 1


37
18.3
8.9
12
+
intron
High mobility group AT-hook 2
NC


38
18.2
6.1
2
−
intron
Fibronectin 1
NC


39
18.0
4.7
10
+
extra-
CUE domain containing 2
NC







genic


40
17.8
4.7
1
−
exon
Chitinase 3-like 1 (cartilage glycoprotein-
NC








39)


41
17.8
5.4
12
−
intron/
High mobility group AT-hook 2
NC







antisense


42
17.5
4.7
2
−
exon
Fibronectin 1
NC


43
17.2
6.0
1
+
intron
ST6 (alpha-N-acetyl-neuraminyl-2,3-beta-
NC








galactosyl-1,3)-N-acetylgalactosaminide








alpha-2,6-sialyltransferase 5


44
16.5
4.4
2
−
exon
Fibronectin 1
CDS


45
16.2
4.9
4
+
exon
Solute carrier family 34 (sodium
CDS








phosphate), member 2


46
15.8
5.3
2
−
exon
Fibronectin 1
CDS


47
15.7
5.3
2
−
exon
Fibronectin 1
NC


48
15.6
6.4
2
−
exon
Fibronectin 1
CDS


49
15.5
6.4
7
−
exon
Putative binding protein 7a5
NC


50
14.4
4.0
2
+
intron
Interleukin 1 receptor-like 1
NC


51
14.0
5.4
3
−
exon
Lipase, member H
NC


52
13.9
6.8
2
+
exon
Cytochrome P450, family 1, subfamily B,
NC








polypeptide 1


53
13.8
4.8
1
−
extra-
Dehydrogenase/reductase (SDR family)
NC







genic
member 3


54
13.2
5.8
12
+
intron
High mobility group AT-hook 2
NC


55
13.2
6.4
2
+
exon/
Fibronectin 1
NC







antisense


56
13.0
6.4
2
−
exon
Fibronectin 1
CDS


57
12.6
5.0
7
−
intron/
Sidekick homolog 1, cell adhesion
NC







antisense
molecule (chicken)


58
12.6
5.4
14
+
intron
Lectin, galactoside-binding, soluble, 3
NC


59
12.4
6.8
2
+
exon
Cytochrome P450, family 1, subfamily B,
NC








polypeptide 1


60
12.2
4.7
19
+
exon
Cytochrome P450, family 2, subfamily S,
NC








polypeptide 1


61
12.1
4.0
5
−
exon
Chemokine (C—X—C motif) ligand 14
NC


62
12.1
4.5
8
+
exon
Transmembrane 7 superfamily member 4
NC


63
12.0
4.9
1
−
exon
Retinoid X receptor, gamma
CDS


64
11.8
6.8
16
−
exon
Cadherin 3, type 1, P-cadherin (placental)
NC


65
11.7
5.5
1
+
extra-
E74-like factor 3 (ets domain transcription
NC







genic
factor, epithelial-specific)


66
11.6
4.8
19
−
exon
[NM_000064]
CDS


67
11.6
4.5
2
+
intron/
Fibronectin 1
NC







antisense


68
11.6
5.3
3
+
extra-
Golgi integral membrane protein 4
NC







genic


69
11.6
5.4
3
+
extra-
Ecotropic viral integration site 1
NC







genic


70
11.5
5.8
2
−
intron
Fibronectin 1
NC


71
11.4
4.2
4
−
exon/
Chemokine (C—X—C motif) ligand 2
NC







promoter


72
11.2
5.1
12
+
exon
Plexin C1
CDS


73
11.2
4.9
19
+
exon
Apolipoprotein E
NC


74
11.1
6.1
2
−
intron
Fibronectin 1
NC


75
11.0
5.2
1
−
exon
Chitinase 3-like 1 (cartilage glycoprotein-
NC








39)


76
10.5
3.8
2
−
intron
Ornithine decarboxylase 1
NC


77
10.4
4.1
9
−
intron
Tenascin C (hexabrachion)
NC


78
10.3
4.4
1
+
exon
Stratifin
NC


79
10.3
5.9
2
−
exon
Fibronectin 1
CDS


80
10.3
5.6
2
+
exon/
Fibronectin 1
NC







antisense


81
10.2
5.5
2
−
exon
Fibronectin 1
CDS


82
10.1
4.9
3
−
exon
Claudin 1
CDS


83
10.1
5.7
14
−
exon
Serpin peptidase inhibitor, clade A (alpha-
CDS








1 antiproteinase, antitrypsin), member 1


84
10.0
4.0
2
+
intron/
Fibronectin 1
NC







antisense


85
9.9
4.1
12
+
intron
High mobility group AT-hook 2
NC


86
9.8
4.3
2
−
exon
Fibronectin 1
CDS


87
9.7
5.6
2
−
exon
Fibronectin 1
CDS


88
9.6
5.4
14
+
intron
Lectin, galactoside-binding, soluble, 3
NC


89
9.6
5.9
2
−
exon
Fibronectin 1
CDS


90
9.5
4.6
3
+
extra-
Golgi integral membrane protein 4
NC







genic


91
9.5
5.2
2
−
intron
Fibronectin 1
NC


92
9.5
5.4
20
−
exon
R-spondin family, member 4
NC


93
9.4
5.5
14
+
exon/
Serpin peptidase inhibitor, clade A (alpha-
NC







antisense
1 antiproteinase, antitrypsin), member 1


94
9.4
4.1
1
+
exon
Regulator of G-protein signaling 1
NC


95
9.3
4.3
10
+
intron
Protein tyrosine phosphatase, receptor
NC








type, E


96
9.2
4.6
2
−
exon
Fibronectin 1
CDS


97
9.2
4.9
12
−
exon
NEL-like 2 (chicken)
CDS


98
9.1
5.6
2
−
exon
Fibronectin 1
CDS


99
9.1
5.5
2
−
exon
Fibronectin 1
CDS


100
9.1
4.3
3
+
exon/
Growth associated protein 43
CDS







promoter


101
9.1
4.6
3
+
extra-
Ecotropic viral integration site 1
NC







genic


102
9.0
4.1
2
−
intron
Fibronectin 1
NC


103
9.0
4.5
2
−
exon
Fibronectin 1
CDS


104
9.0
6.4
10
+
intron
Protein tyrosine phosphatase, receptor
NC








type, E


105
9.0
3.8
12
−
intron/
High mobility group AT-hook 2
NC







antisense


106
8.9
4.8
2
−
exon
Fibronectin 1
CDS


107
8.8
5.2
1
−
exon
Collagen, type VIII, alpha 2
NC


108
8.8
5.1
1
−
exon
Calcium/calmodulin-dependent protein
NC








kinase II inhibitor 1


109
8.8
4.5
2
−
intron
Fibronectin 1
CDS


110
8.8
5.2
4
+
exon
Solute carrier family 34 (sodium
CDS








phosphate), member 2


111
8.8
4.7
12
+
intron
High mobility group AT-hook 2
NC


112
8.7
4.0
12
−
exon/
NEL-like 2 (chicken)
NC







promoter


113
8.7
4.2
1
−
extra-
Kin of IRRE like (Drosophila)
NC







genic


114
8.7
5.4
2
−
exon
Fibronectin 1
CDS


115
8.6
5.8
2
−
exon
Fibronectin 1
CDS


116
8.5
3.9
11
−
intron
Ankyrin repeat and BTB (POZ) domain
NC








containing 2


117
8.5
4.0
6
+
intron/
Dystonin
NC







antisense


118
8.4
4.1
4
+
exon
Solute carrier family 34 (sodium
NC








phosphate), member 2


119
8.3
5.0
14
−
exon
Serpin peptidase inhibitor, clade A (alpha-
CDS








1 antiproteinase, antitrypsin), member 1


120
8.1
3.9
13
+
exon
Sciellin
CDS


121
8.1
4.3
2
−
exon
Fibronectin 1
CDS


122
8.1
5.0
5
+
intron
PDZ and LIM domain 4
NC


123
8.1
6.4
1
−
exon
Calcium/calmodulin-dependent protein
NC








kinase II inhibitor 1


124
8.1
5.1
21
−
intron
T-cell lymphoma invasion and metastasis 1
NC


125
8.1
4.8
4
+
exon
Solute carrier family 34 (sodium
NC








phosphate), member 2


126
8.0
5.4
2
−
exon
Fibronectin 1
CDS


127
8.0
4.8
2
−
exon
Fibronectin 1
CDS


128
8.0
6.1
19
+
exon
Kallikrein-related peptidase 7
NC


129
7.9
5.3
2
+
intron/
Fibronectin 1
NC







antisense


130
7.9
5.0
2
−
exon
Fibronectin 1
CDS


131
7.9
4.1
2
−
intron
Fibronectin 1
NC


132
7.9
4.3
3
+
exon
Transmembrane 4 L six family member 4
NC


133
7.9
4.6
2
−
intron
Fibronectin 1
NC


134
7.9
4.1
16
+
exon
Tumor necrosis factor receptor
CDS








superfamily, member 12A


135
7.8
5.0
2
−
exon
Transmembrane protein 166
NC


136
7.8
4.8
2
−
exon
Fibronectin 1
NC


137
7.6
5.3
14
+
exon/
Serpin peptidase inhibitor, clade A (alpha-
NC







antisense
1 antiproteinase, antitrypsin), member 1


138
7.6
5.5
2
−
exon
Fibronectin 1
CDS


139
7.6
5.7
2
−
intron
Fibronectin 1
NC


140
7.6
4.2
4
+
exon
Solute carrier family 34 (sodium
NC








phosphate), member 2


141
7.6
4.2
13
+
exon
Cysteinyl leukotriene receptor 2
NC


142
7.5
6.4
12
+
intron
Prickle homolog 1 (Drosophila)
NC


143
7.5
3.7
15
−
exon
Aldehyde dehydrogenase 1 family,
NC








member A3


144
7.5
4.7
7
+
extra-
Putative binding protein 7a5
NC







genic


145
7.4
6.3
2
−
exon
Fibronectin 1
CDS


146
7.4
5.1
2
−
exon
Fibronectin 1
CDS


147
7.4
3.8
4
+
exon
Solute carrier family 34 (sodium
CDS








phosphate), member 2


148
7.4
6.3
2
+
intron/
Fibronectin 1
NC







antisense


149
7.3
3.8
6
−
exon
Triggering receptor expressed on myeloid
NC








cells 2


150
7.3
5.1
2
+
exon
Fibronectin 1
NC


151
7.3
4.5
2
−
exon
Fibronectin 1
CDS


152
7.2
3.7
10
+
extra-
Inositol polyphosphate multikinase
NC







genic


153
7.2
4.3
1
+
extra-
Solute carrier family 6 (neurotransmitter
NC







genic
transporter, glycine), member 9


154
7.2
4.1
12
−
exon
Oxidized low density lipoprotein (lectin-
NC








like) receptor 1


155
7.2
5.1
11
+
exon
Cystatin E/M
NC


156
7.2
3.9
12
−
exon
High mobility group AT-hook 2
NC


157
7.1
4.4
2
−
exon
Fibronectin 1
CDS


158
7.1
4.3
16
+
exon
Cadherin 3, type 1, P-cadherin (placental)
NC


159
7.0
5.2
2
−
exon
Fibronectin 1
CDS


160
7.0
5.1
2
−
exon
Fibronectin 1
CDS


161
7.0
5.3
2
−
exon
Fibronectin 1
CDS


162
7.0
4.1
11
−
extra-
[NM_001004729]
NC







genic


163
6.9
3.9
4
+
exon
Solute carrier family 34 (sodium
NC








phosphate), member 2


164
6.9
4.3
10
+
intron
Protein tyrosine phosphatase, receptor
NC








type, E


165
6.9
5.5
2
−
exon
Fibronectin 1
CDS


166
6.8
4.0
3
−
intron
Insulin-like growth factor 2 mRNA
NC








binding protein 2


167
6.8
3.9
22
−
exon
Leukemia inhibitory factor (cholinergic
NC








differentiation factor)


168
6.8
4.4
X
+
extra-
Mastermind-like domain containing 1
NC







genic


169
6.8
5.2
4
+
exon
Secreted phosphoprotein 1 (osteopontin,
NC








bone sialoprotein I, early T-lymphocyte








activation 1)


170
6.8
5.5
2
−
intron
Fibronectin 1
NC


171
6.7
4.3
5
+
intron
Integrin, alpha 2 (CD49B, alpha 2 subunit
NC








of VLA-2 receptor)


172
6.7
5.0
2
−
exon
Fibronectin 1
CDS


173
6.7
3.8
4
−
exon
Phosphodiesterase 5A, cGMP-specific
NC


174
6.7
3.8
2
−
exon
Fibronectin 1
CDS


175
6.7
7.0
17
−
exon
Phospholipase C, delta 3
NC


176
6.6
3.7
2
−
intron
LON peptidase N-terminal domain and
NC








ring finger 2


177
6.5
5.0
7
+
intron
Sidekick homolog 1, cell adhesion
NC








molecule (chicken)


178
6.5
5.6
2
−
exon
Fibronectin 1
CDS


179
6.4
4.2
1
+
exon
Microfibrillar-associated protein 2
NC


180
6.3
6.8
1
−
exon
Calcium/calmodulin-dependent protein
CDS








kinase II inhibitor 1


181
6.3
5.2
2
−
exon
Fibronectin 1
CDS


182
6.3
3.9
4
+
exon
Solute carrier family 34 (sodium
CDS








phosphate), member 2


183
6.2
5.7
2
−
exon
Fibronectin 1
CDS


184
6.2
4.3
4
+
exon
Solute carrier family 34 (sodium
CDS








phosphate), member 2


185
6.2
3.7
17
−
extra-
IKAROS family zinc finger 3 (Aiolos)
NC







genic


186
6.2
4.9
2
−
intron
Fibronectin 1
NC


187
6.2
4.6
17
−
intron
Family with sequence similarity 20,
NC








member A


188
6.2
5.4
14
−
exon
Serpin peptidase inhibitor, clade A (alpha-
CDS








1 antiproteinase, antitrypsin), member 1


189
6.1
4.1
4
+
exon
Complement factor I
NC


190
6.1
5.5
2
−
exon
Fibronectin 1
CDS


191
6.1
4.3
2
+
intron/
Fibronectin 1
NC







antisense


192
6.1
5.7
10
+
extra-
CUE domain containing 2
NC







genic


193
6.1
3.7
12
+
extra-
[NM_001013690]
NC







genic


194
6.1
5.6
2
+
exon/
Fibronectin 1
NC







antisense


195
6.1
3.9
7
−
intron
Putative binding protein 7a5
NC


196
6.1
4.8
2
−
exon
Fibronectin 1
CDS


197
6.0
5.0
2
−
exon
Fibronectin 1
NC


198
6.0
5.6
2
−
exon
Fibronectin 1
CDS


199
6.0
3.7
5
+
exon
Solute carrier family 27 (fatty acid
CDS








transporter), member 6


200
6.0
3.9
11
+
exon
Ets homologous factor
CDS


201
6.0
5.5
7
−
extra-
Putative binding protein 7a5
NC







genic


202
6.0
5.0
2
−
exon
Fibronectin 1
CDS


203
5.9
5.3
2
−
exon
Fibronectin 1
CDS


204
5.9
4.1
12
+
intron
High mobility group AT-hook 2
NC


205
5.8
4.1
2
−
exon
Fibronectin 1
CDS


206
5.8
5.6
2
−
exon
Fibronectin 1
CDS


207
5.8
4.0
12
+
exon
Beta-1,4-N-acetyl-galactosaminyl
CDS








transferase 3


208
5.7
4.9
4
−
exon
Chemokine (C—X—C motif) ligand 2
NC


209
5.7
6.3
12
−
intron
Prickle homolog 1 (Drosophila)
NC


210
5.7
4.4
13
−
extra-
Cysteinyl leukotriene receptor 2
NC







genic


211
5.7
3.9
2
+
exon/
Fibronectin 1
NC







antisense


212
5.7
4.5
19
−
exon
Leucine-rich alpha-2-glycoprotein 1
NC


213
5.6
4.6
2
−
intron
Fibronectin 1
NC


214
5.6
4.8
12
+
exon
High mobility group AT-hook 2
NC


215
5.6
4.8
3
+
extra-
Golgi integral membrane protein 4
NC







genic


216
5.4
3.7
6
−
exon
Runt-related transcription factor 2
NC


217
5.4
4.0
4
+
exon
Solute carrier family 34 (sodium
NC








phosphate), member 2


218
5.4
4.3
12
−
exon
NEL-like 2 (chicken)
CDS


219
5.4
4.7
2
−
exon
Fibronectin 1
CDS


220
5.3
4.3
3
+
exon
Claudin 1
NC


221
5.3
3.7
3
−
exon
Claudin 1
NC


222
5.3
3.7
4
+
exon
Solute carrier family 34 (sodium
CDS








phosphate), member 2


223
5.3
4.7
2
−
exon
Fibronectin 1
CDS


224
5.2
5.3
4
+
intron
Hypothetical protein FLJ20184
NC


225
5.1
4.4
2
−
intron
Fibronectin 1
NC


226
5.1
4.5
2
−
extra-
Pellino homolog 1 (Drosophila)
NC







genic


227
5.1
5.7
2
+
intron/
Fibronectin 1
NC







antisense


228
5.1
3.9
12
−
intron/
High mobility group AT-hook 2
NC







antisense


229
5.1
4.6
10
+
exon
Protein tyrosine phosphatase, receptor
NC








type, E


230
5.1
4.2
2
−
exon
Fibronectin 1
CDS


231
5.1
4.1
3
+
exon
Claudin 1
NC


232
5.0
3.7
2
+
exon
Dipeptidyl-peptidase 4 (CD26, adenosine
NC








deaminase complexing protein 2)


233
5.0
4.5
2
−
exon
Cytochrome P450, family 1, subfamily B,
NC








polypeptide 1


234
5.0
4.5
14
+
exon
Lectin, galactoside-binding, soluble, 3
CDS


235
4.9
7.0
1
+
extra-
Vang-like 1 (van gogh, Drosophila)
NC







genic


236
4.8
4.9
17
+
exon
ATP-binding cassette, sub-family C
NC








(CFTR/MRP), member 3


237
4.7
4.3
17
−
extra-
Trinucleotide repeat containing 6C
NC







genic


238
4.6
4.0
2
+
intron/
Fibronectin 1
NC







antisense


239
4.5
3.9
3
+
intron
Interleukin 1 receptor accessory protein
NC


240
4.4
4.1
3
−
intron
Protein S (alpha)
NC


241
4.4
4.3
2
+
exon/
Fibronectin 1
NC







antisense


242
4.4
3.8
4
+
exon
Solute carrier family 34 (sodium
CDS








phosphate), member 2


243
4.3
5.0
11
+
exon
Cystatin E/M
CDS


244
4.3
4.9
14
+
exon
Lectin, galactoside-binding, soluble, 3
CDS


245
3.9
3.9
8
−
exon
Dual specificity phosphatase 4
NC


246
3.9
4.3
4
+
exon
Solute carrier family 34 (sodium
CDS








phosphate), member 2


247
3.8
4.2
2
+
exon
Neuropilin 2
NC


248
3.4
4.1
2
−
intron
Fibronectin 1
CDS


249
−50.0
−6.3
2
+
exon
Thyroid peroxidase
CDS


250
−33.3
−6.5
2
+
intron
Thyroid peroxidase
NC


251
−33.3
−6.8
2
+
extra-
Thyroid peroxidase
NC







genic


252
−33.3
−6.4
11
−
extra-
Recombination activating gene 2
NC







genic


253
−25.0
−5.7
1
−
extra-
KIAA1324
NC







genic


254
−25.0
−6.5
2
+
exon/
Thyroid peroxidase
NC







promoter


255
−25.0
−5.4
2
+
exon
Thyroid peroxidase
CDS


256
−25.0
−6.0
2
−
intron
Low density lipoprotein-related protein
NC








1B (deleted in tumors)


257
−25.0
−4.8
8
−
exon
Fatty acid binding protein 4, adipocyte
NC


258
−25.0
−5.4
11
−
intron
Metallophosphoesterase domain
NC








containing 2


259
−25.0
−5.2
11
−
exon/
Metallophosphoesterase domain
NC







promoter
containing 2


260
−20.0
−8.8
2
+
exon
Thyroid peroxidase
CDS


261
−20.0
−7.5
2
+
intron
Thyroid peroxidase
NC


262
−20.0
−5.3
8
+
exon
Polycystic kidney and hepatic disease 1
CDS








(autosomal recessive)-like 1


263
−20.0
−5.6
8
−
intron/
Polycystic kidney and hepatic disease 1
NC







antisense
(autosomal recessive)-like 1


264
−20.0
−5.3
12
−
intron
Solute carrier family 5 (iodide
NC








transporter), member 8


265
−20.0
−5.6
15
+
extra-
Interferon stimulated exonuclease gene
NC







genic
20 kDa-like 1


266
−16.7
−7.5
2
−
exon/
Thyroid peroxidase
NC







antisense


267
−16.7
−5.3
2
−
intron
Low density lipoprotein-related protein
NC








1B (deleted in tumors)


268
−16.7
−4.3
8
+
exon
Polycystic kidney and hepatic disease 1
CDS








(autosomal recessive)-like 1


269
−16.7
−6.1
8
−
extra-
Zinc finger, matrin type 4
NC







genic


270
−16.7
−5.4
8
−
extra-
Polycystic kidney and hepatic disease 1
NC







genic
(autosomal recessive)-like 1


271
−16.7
−6.6
11
+
extra-
Chromosome 11 open reading frame 74
NC







genic


272
−16.7
−5.2
11
−
intron
Metallophosphoesterase domain
NC








containing 2


273
−16.7
−5.7
21
−
exon
Trefoil factor 3 (intestinal)
NC


274
−14.3
−6.8
2
+
exon
Thyroid peroxidase
CDS


275
−14.3
−4.8
2
+
exon
Thyroid peroxidase
CDS


276
−14.3
−6.1
2
−
intron
Low density lipoprotein-related protein
NC








1B (deleted in tumors)


277
−14.3
−5.4
2
−
exon
Low density lipoprotein-related protein 2
CDS


278
−14.3
−6.9
6
−
intron/
Opioid receptor, mu 1
CDS







antisense


279
−14.3
−6.0
6
−
intron/
Opioid receptor, mu 1
NC







antisense


280
−14.3
−4.4
7
−
exon
Sema domain, immunoglobulin domain
CDS








(Ig), short basic domain, secreted,








(semaphorin) 3D


281
−14.3
−4.6
8
+
exon
Polycystic kidney and hepatic disease 1
CDS








(autosomal recessive)-like 1


282
−14.3
−4.6
8
+
exon
Polycystic kidney and hepatic disease 1
CDS








(autosomal recessive)-like 1


283
−14.3
−5.7
11
−
exon
Metallophosphoesterase domain
NC








containing 2


284
−14.3
−6.7
21
−
exon
Trefoil factor 3 (intestinal)
NC


285
−12.5
−6.2
2
+
extra-
Thyroid peroxidase
NC







genic


286
−12.5
−8.5
2
−
exon/
Thyroid peroxidase
NC







antisense


287
−12.5
−3.7
2
−
extra-
ST6 beta-galactosamide alpha-2,6-
NC







genic
sialyltranferase 2


288
−12.5
−4.9
2
−
exon
Nebulin
CDS


289
−12.5
−5.7
4
+
intron
V-kit Hardy-Zuckerman 4 feline sarcoma
NC








viral oncogene homolog


290
−12.5
−6.6
4
+
extra-
[NM_152620]
NC







genic


291
−12.5
−5.5
5
+
intron/
Sparc/osteonectin, cwcv and kazal-like
NC







antisense
domains proteoglycan (testican) 1


292
−12.5
−4.4
7
−
extra-
Solute carrier family 26, member 4
NC







genic


293
−12.5
−8.0
8
+
intron
EF-hand domain family, member A2
NC


294
−12.5
−4.5
8
+
exon
Matrilin 2
CDS


295
−12.5
−4.0
8
+
exon
Polycystic kidney and hepatic disease 1
CDS








(autosomal recessive)-like 1


296
−12.5
−4.2
8
+
exon
Polycystic kidney and hepatic disease 1
CDS








(autosomal recessive)-like 1


297
−12.5
−4.2
8
+
exon
Polycystic kidney and hepatic disease 1
CDS








(autosomal recessive)-like 1


298
−12.5
−3.7
8
+
exon
Polycystic kidney and hepatic disease 1
CDS








(autosomal recessive)-like 1


299
−12.5
−4.9
10
+
extra-
BCL2-associated athanogene 3
NC







genic


300
−12.5
−5.5
11
−
exon
Metallophosphoesterase domain
CDS








containing 2


301
−12.5
−4.1
16
+
exon
Metallothionein 1H
NC


302
−12.5
−4.0
20
+
exon
Collagen, type IX, alpha 3
CDS


303
−11.1
−4.9
2
+
exon
Thyroid peroxidase
NC


304
−11.1
−4.9
2
−
intron
Low density lipoprotein-related protein
NC








1B (deleted in tumors)


305
−11.1
−4.6
4
+
intron
Sorbin and SH3 domain containing 2
NC


306
−11.1
−5.5
5
+
intron
Transmembrane protein 171
NC


307
−11.1
−5.0
7
+
exon
Solute carrier family 26, member 4
CDS


308
−11.1
−4.7
7
−
extra-
Solute carrier family 26, member 4
NC







genic


309
−11.1
−4.1
8
+
intron
Solute carrier family 26, member 7
NC


310
−11.1
−4.5
8
+
exon
Polycystic kidney and hepatic disease 1
CDS








(autosomal recessive)-like 1


311
−11.1
−4.8
8
+
exon
Polycystic kidney and hepatic disease 1
CDS








(autosomal recessive)-like 1


312
−11.1
−7.4
8
−
extra-
[NM_054028]
NC







genic


313
−11.1
−5.4
8
−
exon
Chromosome 8 open reading frame 13
NC


314
−11.1
−5.2
8
−
exon
Fatty acid binding protein 4, adipocyte
CDS


315
−11.1
−4.0
8
−
intron/
Solute carrier family 26, member 7
NC







antisense


316
−11.1
−5.7
9
−
extra-
Insulin-like growth factor binding protein-
NC







genic
like 1


317
−11.1
−6.5
11
+
intron
Chromosome 11 open reading frame 74
NC


318
−11.1
−7.0
12
+
intron
Mitochondrial ribosomal protein S35
NC


319
−11.1
−5.5
13
+
intron/
[NM_130785]
NC







antisense


320
−11.1
−5.2
14
+
exon
Tudor domain containing 9
NC


321
−11.1
−6.0
16
+
exon
Metallothionein 1G
NC


322
−11.1
−5.3
16
−
exon
Cadherin 16, KSP-cadherin
NC


323
−11.1
−6.2
18
−
extra-
Maestro
NC







genic


324
−11.1
−4.0
20
+
exon
Collagen, type IX, alpha 3
CDS


325
−10.0
−4.5
1
+
extra-
Enoyl Coenzyme A hydratase domain
NC







genic
containing 2


326
−10.0
−5.5
2
+
exon
Thyroid peroxidase
CDS


327
−10.0
−5.0
2
+
intron
Thyroid peroxidase
NC


328
−10.0
−5.9
2
+
intron
Thyroid peroxidase
NC


329
−10.0
−4.7
2
+
extra-
Solute carrier family 5 (choline
NC







genic
transporter), member 7


330
−10.0
−4.7
2
−
exon
[NM_001002036]
CDS


331
−10.0
−4.2
3
−
intron/
Zinc finger protein 167
NC







antisense


332
−10.0
−5.7
4
−
intron
Sorbin and SH3 domain containing 2
NC


333
−10.0
−5.7
4
−
intron
Sorbin and SH3 domain containing 2
NC


334
−10.0
−6.0
5
−
intron
Protein phosphatase 2 (formerly 2A),
NC








regulatory subunit B, beta isoform


335
−10.0
−6.0
6
+
intron
Opioid receptor, mu 1
NC


336
−10.0
−4.7
7
+
exon
Solute carrier family 26, member 4
CDS


337
−10.0
−3.8
8
+
intron
Solute carrier family 26, member 7
NC


338
−10.0
−4.3
8
+
exon
Solute carrier family 26, member 7
CDS


339
−10.0
−4.5
8
+
exon
Solute carrier family 26, member 7
CDS


340
−10.0
−5.6
8
+
exon
Matrilin 2
CDS


341
−10.0
−5.3
8
+
exon
Polycystic kidney and hepatic disease 1
CDS








(autosomal recessive)-like 1


342
−10.0
−4.4
8
+
exon
Polycystic kidney and hepatic disease 1
CDS








(autosomal recessive)-like 1


343
−10.0
−5.2
8
+
extra-
Estrogen receptor binding site associated,
NC







genic
antigen, 9


344
−10.0
−6.7
11
+
exon
Chromosome 11 open reading frame 74
CDS


345
−10.0
−5.0
11
−
intron
Metallophosphoesterase domain
NC








containing 2


346
−10.0
−4.2
15
+
exon
Cellular retinoic acid binding protein 1
NC


347
−10.0
−4.2
15
−
intron
Integrin, alpha 11
NC


348
−10.0
−5.5
21
+
extra-
U2 small nuclear RNA auxiliary factor 1
NC







genic


349
−9.1
−6.3
1
+
intron
Glutathione S-transferase M4
NC


350
−9.1
−5.9
2
+
intron/
Low density lipoprotein-related protein
NC







antisense
1B (deleted in tumors)


351
−9.1
−4.0
2
+
extra-
Insulin receptor substrate 1
NC







genic


352
−9.1
−4.6
2
−
intron/
Thyroid peroxidase
NC







antisense


353
−9.1
−6.2
2
−
intron/
Thyroid peroxidase
NC







antisense


354
−9.1
−5.4
2
−
exon
Low density lipoprotein-related protein
CDS








1B (deleted in tumors)


355
−9.1
−4.3
4
+
exon
Sorbin and SH3 domain containing 2
NC


356
−9.1
−3.8
8
+
exon
Polycystic kidney and hepatic disease 1
CDS








(autosomal recessive)-like 1


357
−9.1
−4.0
8
+
exon
Polycystic kidney and hepatic disease 1
CDS








(autosomal recessive)-like 1


358
−9.1
−4.6
8
−
intron/
Thyroglobulin
NC







antisense


359
−9.1
−4.9
9
−
exon
Aldehyde dehydrogenase 1 family,
CDS








member A1


360
−9.1
−6.6
11
+
extra-
Chromosome 11 open reading frame 74
NC







genic


361
−9.1
−6.1
11
−
intron
Metallophosphoesterase domain
NC








containing 2


362
−9.1
−4.3
11
−
intron
Metallophosphoesterase domain
NC








containing 2


363
−9.1
−5.2
12
+
extra-
Chromosome 12 open reading frame 39
NC







genic


364
−9.1
−5.5
12
−
exon
Solute carrier family 5 (iodide
CDS








transporter), member 8


365
−9.1
−4.8
20
+
exon
Chromosome 20 open reading frame 39
NC


366
−8.3
−4.1
1
+
exon
KIAA1324
CDS


367
−8.3
−5.1
2
+
intron/
Thyroid peroxidase
NC







promoter


368
−8.3
−6.3
2
+
exon
Thyroid peroxidase
CDS


369
−8.3
−5.7
2
+
exon
Thyroid peroxidase
CDS


370
−8.3
−4.9
2
+
intron/
Low density lipoprotein-related protein
NC







antisense
1B (deleted in tumors)


371
−8.3
−5.9
2
−
intron
Low density lipoprotein-related protein
NC








1B (deleted in tumors)


372
−8.3
−5.3
2
−
intron
Low density lipoprotein-related protein 2
NC


373
−8.3
−7.0
4
+
exon/
Peroxisome proliferator-activated receptor
NC







antisense
gamma, coactivator 1 alpha


374
−8.3
−5.3
4
−
exon
Peroxisome proliferator-activated receptor
NC








gamma, coactivator 1 alpha


375
−8.3
−4.8
4
−
exon
Peroxisome proliferator-activated receptor
NC








gamma, coactivator 1 alpha


376
−8.3
−6.0
4
−
intron
Sorbin and SH3 domain containing 2
NC


377
−8.3
−5.5
4
−
exon
Sorbin and SH3 domain containing 2
CDS


378
−8.3
−4.3
4
−
intron
Sorbin and SH3 domain containing 2
NC


379
−8.3
−4.9
7
+
exon
Solute carrier family 26, member 4
CDS


380
−8.3
−5.2
7
+
exon
Solute carrier family 26, member 4
CDS


381
−8.3
−5.3
7
+
exon
Solute carrier family 26, member 4
NC


382
−8.3
−5.6
8
+
extra-
Chromosome 8 open reading frame 79
NC







genic


383
−8.3
−7.1
8
+
exon
Zinc finger, matrin type 4
NC


384
−8.3
−3.8
8
+
intron
Solute carrier family 26, member 7
NC


385
−8.3
−4.2
8
+
exon
Polycystic kidney and hepatic disease 1
CDS








(autosomal recessive)-like 1


386
−8.3
−4.1
8
+
exon
Polycystic kidney and hepatic disease 1
CDS








(autosomal recessive)-like 1


387
−8.3
−5.5
8
+
exon
Polycystic kidney and hepatic disease 1
CDS








(autosomal recessive)-like 1


388
−8.3
−3.9
8
+
exon
Polycystic kidney and hepatic disease 1
CDS








(autosomal recessive)-like 1


389
−8.3
−3.8
8
+
extra-
Polycystic kidney and hepatic disease 1
NC







genic
(autosomal recessive)-like 1


390
−8.3
−4.1
8
+
intron
Thyroglobulin
NC


391
−8.3
−4.6
9
−
intron
Leucine rich repeat and Ig domain
NC








containing 2


392
−8.3
−6.1
11
+
extra-
Zinc finger, DHHC-type containing 13
NC







genic


393
−8.3
−4.3
11
−
exon
Metallophosphoesterase domain
CDS








containing 2


394
−8.3
−6.6
12
−
exon
Solute carrier family 5 (iodide
CDS








transporter), member 8


395
−8.3
−4.7
13
−
exon
Centromere protein J
CDS


396
−8.3
−7.4
14
+
extra-
Tumor necrosis factor, alpha-induced
NC







genic
protein 2


397
−8.3
−4.8
14
+
exon
Tudor domain containing 9
CDS


398
−8.3
−3.8
14
−
exon
Deiodinase, iodothyronine, type II
CDS


399
−8.3
−6.3
16
+
extra-
Chromodomain helicase DNA binding
NC







genic
protein 9


400
−7.7
−3.9
1
+
exon
Bone morphogenetic protein 8a
NC


401
−7.7
−5.3
1
+
intron
6-phosphofructo-2-kinase/fructose-2,6-
NC








biphosphatase 2


402
−7.7
−5.0
1
+
extra-
RAB4A, member RAS oncogene family
NC







genic


403
−7.7
−8.5
2
+
exon
Thyroid peroxidase
CDS


404
−7.7
−3.8
2
+
extra-
ST6 beta-galactosamide alpha-2,6-
NC







genic
sialyltranferase 2


405
−7.7
−4.8
2
−
intron
Thyroid peroxidase
NC


406
−7.7
−4.0
2
−
exon
Low density lipoprotein-related protein
CDS








1B (deleted in tumors)


407
−7.7
−6.7
2
−
exon
Low density lipoprotein-related protein 2
CDS


408
−7.7
−4.2
2
−
exon
Low density lipoprotein-related protein 2
CDS


409
−7.7
−7.2
2
−
exon
Low density lipoprotein-related protein 2
CDS


410
−7.7
−5.7
3
+
intron
Inositol 1,4,5-triphosphate receptor, type 1
NC


411
−7.7
−4.8
3
+
extra-
RING1 and YY1 binding protein
NC







genic


412
−7.7
−5.2
4
+
exon
Solute carrier family 4, sodium
NC








bicarbonate cotransporter, member 4


413
−7.7
−4.7
4
−
exon
Sorbin and SH3 domain containing 2
NC


414
−7.7
−6.4
5
+
exon
Orthopedia homeobox
NC


415
−7.7
−5.4
5
+
exon
G protein-coupled receptor 98
CDS


416
−7.7
−4.9
7
+
exon
Solute carrier family 26, member 4
CDS


417
−7.7
−5.6
7
+
exon
Solute carrier family 26, member 4
CDS


418
−7.7
−4.3
8
+
intron
Solute carrier family 26, member 7
NC


419
−7.7
−5.3
8
+
exon
Polycystic kidney and hepatic disease 1
CDS








(autosomal recessive)-like 1


420
−7.7
−7.9
9
+
intron
Ubiquitin-conjugating enzyme E2R 2
NC


421
−7.7
−5.5
10
−
exon
Oxoglutarate dehydrogenase-like
NC


422
−7.7
−7.0
11
−
extra-
Metallophosphoesterase domain
NC







genic
containing 2


423
−7.7
−5.6
11
−
intron
Metallophosphoesterase domain
NC








containing 2


424
−7.7
−5.3
11
−
exon
Neural cell adhesion molecule 1
NC


425
−7.7
−5.2
12
−
exon
Ankyrin repeat and sterile alpha motif
CDS








domain containing 1B


426
−7.7
−5.6
15
+
extra-
Interferon stimulated exonuclease gene
NC







genic
20 kDa-like 1


427
−7.7
−4.6
17
+
intron/
Solute carrier family 39 (metal ion
NC







antisense
transporter), member 11


428
−7.7
−5.3
22
+
exon
Myo-inositol oxygenase
NC


429
−7.1
−4.0
2
+
extra-
Insulin receptor substrate 1
NC







genic


430
−7.1
−6.2
2
−
intron/
Thyroid peroxidase
NC







antisense


431
−7.1
−8.3
2
−
exon
Lymphocyte antigen 75
NC


432
−7.1
−5.4
2
−
exon
Low density lipoprotein-related protein 2
CDS


433
−7.1
−5.2
2
−
exon
Low density lipoprotein-related protein 2
CDS


434
−7.1
−5.8
3
−
intron
Sodium channel, voltage-gated, type V,
NC








alpha subunit


435
−7.1
−6.1
4
+
exon
Peroxisome proliferator-activated receptor
NC








gamma, coactivator 1 alpha


436
−7.1
−5.1
4
+
extra-
Solute carrier family 4, sodium
CDS







genic
bicarbonate cotransporter, member 4


437
−7.1
−4.5
4
+
intron/
Sorbin and SH3 domain containing 2
NC







antisense


438
−7.1
−5.6
4
−
exon
Peroxisome proliferator-activated receptor
NC








gamma, coactivator 1 alpha


439
−7.1
−4.5
4
−
exon
Sorbin and SH3 domain containing 2
CDS


440
−7.1
−4.6
7
−
intron
Engulfment and cell motility 1
NC


441
−7.1
−4.1
8
+
exon
Polycystic kidney and hepatic disease 1
CDS








(autosomal recessive)-like 1


442
−7.1
−4.6
8
+
exon
Polycystic kidney and hepatic disease 1
CDS








(autosomal recessive)-like 1


443
−7.1
−5.0
8
−
exon
RecQ protein-like 4
CDS


444
−7.1
−4.5
9
−
intron
Guanine nucleotide binding protein (G
NC








protein), alpha 14


445
−7.1
−5.5
11
+
intron/
Metallophosphoesterase domain
NC







antisense
containing 2


446
−7.1
−4.5
11
−
intron
Metallophosphoesterase domain
NC








containing 2


447
−7.1
−5.2
11
−
intron
Metallophosphoesterase domain
NC








containing 2


448
−7.1
−4.2
12
+
intron
Zinc finger protein 664
NC


449
−7.1
−5.4
12
−
extra-
Arginine vasopressin receptor 1A
NC







genic


450
−7.1
−5.7
15
−
exon
WD repeat domain 72
CDS


451
−7.1
−4.4
17
+
exon
Hepatic leukemia factor
NC


452
−6.7
−4.7
2
−
intron
Insulin receptor substrate 1
NC


453
−6.7
−5.9
4
+
exon
V-kit Hardy-Zuckerman 4 feline sarcoma
NC








viral oncogene homolog


454
−6.7
−6.7
4
−
exon
Peroxisome proliferator-activated receptor
NC








gamma, coactivator 1 alpha


455
−6.7
−4.3
5
+
exon
Leukemia inhibitory factor receptor alpha
NC


456
−6.7
−4.1
5
−
intron
Kelch-like 3 (Drosophila)
NC


457
−6.7
−7.0
6
−
intron
Lymphocyte antigen 6 complex, locus
CDS








G5C


458
−6.7
−4.5
8
+
exon
Chromosome 8 open reading frame 79
NC


459
−6.7
−3.9
8
+
intron
Solute carrier family 26, member 7
NC


460
−6.7
−5.0
8
+
exon
Matrilin 2
NC


461
−6.7
−5.2
8
+
exon
Polycystic kidney and hepatic disease 1
CDS








(autosomal recessive)-like 1


462
−6.7
−5.2
8
−
exon/
Matrilin 2
NC







antisense


463
−6.7
−4.1
8
−
intron/
Thyroglobulin
NC







antisense


464
−6.7
−4.1
8
−
intron/
Thyroglobulin
NC







antisense


465
−6.7
−5.9
8
−
intron/
Thyroglobulin
NC







antisense


466
−6.7
−7.8
10
−
intron
Methionine adenosyltransferase I, alpha
NC


467
−6.7
−6.9
11
+
extra-
Chromosome 11 open reading frame 74
NC







genic


468
−6.7
−5.6
11
+
extra-
Transmembrane protein 123
NC







genic


469
−6.7
−4.1
11
+
exon
Neural cell adhesion molecule 1
NC


470
−6.7
−3.9
11
+
intron
Neural cell adhesion molecule 1
NC


471
−6.7
−4.2
11
−
exon
Neural cell adhesion molecule 1
NC


472
−6.7
−4.4
17
+
extra-
Breast carcinoma amplified sequence 3
NC







genic


473
−6.7
−7.1
20
+
intron
Phospholipase C, beta 4
NC


474
−6.3
−4.3
2
+
intron
Thyroid peroxidase
CDS


475
−6.3
−5.2
2
+
extra-
ATP synthase, H+ transporting,
NC







genic
mitochondrial F0 complex, subunit C3








(subunit 9)


476
−6.3
−6.1
2
−
intron/
Thyroid peroxidase
NC







antisense


477
−6.3
−4.2
2
−
exon
Low density lipoprotein-related protein
CDS








1B (deleted in tumors)


478
−6.3
−7.1
3
+
extra-
Roundabout, axon guidance receptor,
NC







genic
homolog 2 (Drosophila)


479
−6.3
−5.1
4
+
exon
Solute carrier family 4, sodium
CDS








bicarbonate cotransporter, member 4


480
−6.3
−5.4
4
+
exon
Solute carrier family 4, sodium
NC








bicarbonate cotransporter, member 4


481
−6.3
−5.3
4
−
intron
Sorbin and SH3 domain containing 2
NC


482
−6.3
−4.6
5
−
exon
Leukemia inhibitory factor receptor alpha
CDS


483
−6.3
−6.0
5
−
exon
Leukemia inhibitory factor receptor alpha
CDS


484
−6.3
−6.0
7
+
exon
Solute carrier family 26, member 4
CDS


485
−6.3
−5.3
7
−
intron
Engulfment and cell motility 1
NC


486
−6.3
−5.3
8
+
exon
Chondroitin beta1,4 N-
NC








acetylgalactosaminyltransferase


487
−6.3
−4.4
8
+
intron
Thyroglobulin
NC


488
−6.3
−3.8
8
+
intron
Thyroglobulin
NC


489
−6.3
−7.1
8
−
exon
Zinc finger, matrin type 4
CDS


490
−6.3
−5.1
11
+
exon
Cdon homolog (mouse)
NC


491
−6.3
−4.9
14
−
extra-
General transcription factor IIA, 1,
NC







genic
19/37 kDa


492
−6.3
−5.1
X
−
exon
Four and a half LIM domains 1
NC


493
−5.9
−4.5
1
+
intron
Acyl-CoA thioesterase 11
NC


494
−5.9
−4.6
1
+
exon
KIAA1324
NC


495
−5.9
−5.4
1
−
exon
RAP1 GTPase activating protein
NC


496
−5.9
−4.5
3
+
intron
Inositol 1,4,5-triphosphate receptor, type 1
NC


497
−5.9
−4.5
4
+
intron/
Peroxisome proliferator-activated receptor
NC







antisense
gamma, coactivator 1 alpha


498
−5.9
−6.1
4
+
intron/
Sorbin and SH3 domain containing 2
NC







antisense


499
−5.9
−4.7
6
−
exon/
Opioid receptor, mu 1
CDS







antisense


500
−5.9
−4.5
7
+
extra-
CD36 molecule (thrombospondin
NC







genic
receptor)


501
−5.9
−4.5
7
−
intron
Engulfment and cell motility 1
NC


502
−5.9
−4.1
7
−
extra-
Solute carrier family 26, member 4
NC







genic


503
−5.9
−4.3
8
+
exon/
Solute carrier family 26, member 7
NC







promoter


504
−5.9
−4.0
8
+
exon
Solute carrier family 26, member 7
CDS


505
−5.9
−4.1
8
+
exon
Solute carrier family 26, member 7
NC


506
−5.9
−4.2
8
+
exon
Polycystic kidney and hepatic disease 1
CDS








(autosomal recessive)-like 1


507
−5.9
−5.0
8
+
intron
Thyroglobulin
NC


508
−5.9
−4.5
8
−
intron/
Thyroglobulin
CDS







antisense


509
−5.9
−5.8
8
−
intron/
Thyroglobulin
NC







antisense


510
−5.9
−4.8
9
−
exon
Ankyrin repeat domain 18A
CDS


511
−5.9
−4.6
11
+
exon
Neural cell adhesion molecule 1
NC


512
−5.9
−4.4
11
−
intron
Metallophosphoesterase domain
NC








containing 2


513
−5.9
−4.0
15
−
exon
WD repeat domain 72
CDS


514
−5.9
−6.7
16
+
exon
Metallothionein 1F
CDS


515
−5.6
−7.3
2
+
intron
Thyroid peroxidase
NC


516
−5.6
−4.2
2
−
intron
Phospholipase A2 receptor 1, 180 kDa
NC


517
−5.6
−4.0
3
+
exon
Inositol 1,4,5-triphosphate receptor, type 1
CDS


518
−5.6
−3.8
3
+
extra-
Polymerase (DNA directed), theta
CDS







genic


519
−5.6
−5.4
4
+
exon
Superoxide dismutase 3, extracellular
NC


520
−5.6
−6.0
4
−
intron
Sorbin and SH3 domain containing 2
NC


521
−5.6
−4.1
5
+
exon
G protein-coupled receptor 98
CDS


522
−5.6
−4.3
7
+
intron/
Engulfment and cell motility 1
NC







antisense


523
−5.6
−3.7
7
+
intron/
Engulfment and cell motility 1
NC







antisense


524
−5.6
−6.3
7
+
exon
Solute carrier family 26, member 4
CDS


525
−5.6
−5.1
7
+
exon
Solute carrier family 26, member 4
NC


526
−5.6
−4.3
7
−
intron/
Williams-Beuren syndrome chromosome
NC







antisense
region 17


527
−5.6
−4.4
8
+
exon
Thyroglobulin
CDS


528
−5.6
−3.9
8
+
intron
Thyroglobulin
NC


529
−5.6
−4.3
8
−
intron/
Thyroglobulin
NC







antisense


530
−5.6
−4.0
8
−
intron/
Thyroglobulin
NC







antisense


531
−5.6
−4.5
8
−
intron/
Thyroglobulin
NC







antisense


532
−5.6
−5.0
11
+
intron/
MACRO domain containing 1
NC







antisense


533
−5.6
−3.7
11
+
exon
Neural cell adhesion molecule 1
CDS


534
−5.6
−4.9
11
−
intron
Metallophosphoesterase domain
NC








containing 2


535
−5.6
−5.5
11
−
intron
Cdon homolog (mouse)
NC


536
−5.6
−3.9
11
−
exon
Cdon homolog (mouse)
CDS


537
−5.6
−4.5
13
−
exon
Centromere protein J
CDS


538
−5.6
−4.5
21
+
exon
SH3 domain binding glutamic acid-rich
NC








protein


539
−5.3
−4.0
2
−
exon
Low density lipoprotein-related protein
CDS








1B (deleted in tumors)


540
−5.3
−7.0
2
−
exon
Low density lipoprotein-related protein 2
CDS


541
−5.3
−5.8
4
+
exon
Sorbin and SH3 domain containing 2
NC


542
−5.3
−5.3
4
−
intron
Sorbin and SH3 domain containing 2
NC


543
−5.3
−5.7
4
−
exon
Sorbin and SH3 domain containing 2
CDS


544
−5.3
−4.6
7
−
intron
Engulfment and cell motility 1
NC


545
−5.3
−4.3
7
−
exon
Diacylglycerol kinase, iota
CDS


546
−5.3
−3.9
8
+
exon
Solute carrier family 26, member 7
CDS


547
−5.3
−6.1
10
−
intron
Protocadherin 15
NC


548
−5.3
−8.2
11
+
extra-
Sodium channel, voltage-gated, type III,
NC







genic
beta


549
−5.3
−3.8
11
+
exon
Cdon homolog (mouse)
NC


550
−5.3
−4.2
11
−
intron
Metallophosphoesterase domain
NC








containing 2


551
−5.3
−7.1
14
+
intron
Ras and Rab interactor 3
NC


552
−5.3
−6.1
16
−
extra-
Metallothionein 4
NC







genic


553
−5.0
−5.8
4
−
intron
Sorbin and SH3 domain containing 2
NC


554
−5.0
−7.5
4
−
exon
Sorbin and SH3 domain containing 2
CDS


555
−5.0
−5.6
5
−
exon
Leukemia inhibitory factor receptor alpha
CDS


556
−5.0
−4.6
6
+
exon
Opioid receptor, mu 1
CDS


557
−5.0
−5.0
7
+
exon
Solute carrier family 26, member 4
CDS


558
−5.0
−5.3
8
+
intron
Zinc finger, matrin type 4
NC


559
−5.0
−3.7
8
+
exon
Solute carrier family 26, member 7
CDS


560
−5.0
−5.4
8
−
intron/
Thyroglobulin
CDS







antisense


561
−5.0
−6.8
9
+
intron/
Guanine nucleotide binding protein (G
NC







antisense
protein), alpha 14


562
−5.0
−4.5
11
+
exon
Neural cell adhesion molecule 1
NC


563
−5.0
−4.0
14
−
exon/
Deiodinase, iodothyronine, type II
CDS







promoter


564
−5.0
−3.9
18
+
intron
Katanin p60 subunit A-like 2
NC


565
−4.8
−6.2
2
−
exon
Low density lipoprotein-related protein 2
CDS


566
−4.8
−4.5
4
+
intron
Ankyrin 2, neuronal
NC


567
−4.8
−3.7
4
−
intron
Sorbin and SH3 domain containing 2
NC


568
−4.8
−4.1
8
+
exon
Solute carrier family 26, member 7
CDS


569
−4.8
−4.0
16
+
intron
Splicing factor 3b, subunit 3, 130 kDa
NC


570
−4.5
−5.2
7
+
exon
Solute carrier family 26, member 4
CDS


571
−4.5
−5.1
8
+
exon
Chromosome 8 open reading frame 13
NC


572
−4.5
−5.5
8
−
intron/
Werner syndrome
NC







antisense


573
−4.5
−4.2
11
+
intron
Neural cell adhesion molecule 1
NC


574
−4.3
−5.2
2
−
exon
Low density lipoprotein-related protein 2
CDS


575
−4.3
−3.9
3
+
intron
Potassium voltage-gated channel, shaker-
NC








related subfamily, beta member 1


576
−4.3
−4.1
4
−
intron
Collagen, type XXV, alpha 1
NC


577
−4.3
−4.2
8
+
exon
Solute carrier family 26, member 7
CDS


578
−4.3
−4.2
8
+
exon
Matrilin 2
CDS


579
−4.2
−4.5
1
+
exon
Deiodinase, iodothyronine, type I
NC


580
−4.2
−5.4
4
−
exon
Sorbin and SH3 domain containing 2
CDS


581
−4.2
−4.3
4
−
intron
Sorbin and SH3 domain containing 2
NC


582
−3.7
−4.5
17
+
exon
Glutamate receptor, ionotropic, N-methyl
NC








D-aspartate 2C


583
−3.6
−5.4
3
+
intron
Inositol 1,4,5-triphosphate receptor, type 1
NC


584
−3.6
−3.8
17
−
exon/
Hepatic leukemia factor
NC







promoter

## Claims

1. A method, comprising:
(a) assaying by sequencing, array hybridization, or nucleic acid amplification an expression level of each of a first group of transcripts and a second group of transcripts in a fine needle aspirate test sample from a thyroid nodule of a subject, wherein said first group of transcripts includes at least two transcripts corresponding to at least two sequences selected from SEQ ID Nos. 1-2, and wherein said second group of transcripts includes at least two transcripts corresponding to at least two sequences selected from SEQ ID Nos. 7-8; and
(b) in a programmed computer, comparing said expression level of each of said first group of transcripts and said second group of transcripts with reference expression levels of transcripts corresponding to sequences as set forth in SEQ ID Nos. 1-2 and 7-8 to (1) classify said thyroid nodule as malignant if there is an increase in an expression level corresponding to said first group of transcripts or a decrease in an expression level corresponding to said second group of transcripts, or (2) classify said thyroid nodule as benign if there is an increase in said expression level corresponding to said second group of transcripts or a decrease in said expression level corresponding to said first group of transcripts.

2. The method of claim 1, wherein an increased relative level of expression of one or more transcripts, a decreased relative level of expression of one or more transcripts, or a combination thereof is used to classify the thyroid nodule as malignant.

3. The method of claim 1, wherein an increased relative level of expression of one or more transcripts, a decreased relative level of expression of one or more transcripts, or a combination thereof is used to classify the thyroid nodule as benign.

4. The method of claim 1, wherein said assaying comprises determining said expression level using RT-PCR, Northern blotting, ligase chain reaction, or any combination thereof.

5. The method of claim 1, further comprising measuring an expression level of at least one control nucleic acid in said fine needle aspirate test sample.

6. The method of claim 1, wherein said fine needle aspirate test sample is fresh-frozen or fixed.

7. The method of claim 1, wherein said expression level is measured by pattern recognition.

8. The method of claim 7, wherein said pattern recognition comprises a linear combination of expression levels.

9. The method of claim 7, wherein said pattern recognition comprises a nonlinear combination of expression levels.

10. The method of claim 1, wherein (b) comprises using said programmed computer to (1) classify said thyroid nodule as malignant if there is an increase in an expression level corresponding to said first group of transcripts and a decrease in an expression level corresponding to said second group of transcripts, or (2) classify said thyroid nodule as benign if there is an increase in said expression level corresponding to said second group of transcripts and a decrease in said expression level corresponding to said first group of transcripts.

11. The method of claim 1, wherein said assaying comprises nucleic acid amplification using at least one primer that amplifies a transcript corresponding to a sequence as set forth in any one of SEQ ID Nos. 1-2 and 7-8.

12. The method of claim 1, wherein said assaying comprises (i) nucleic acid amplification using at least one primer that amplifies a transcript corresponding to a sequence as set forth in any one of SEQ ID Nos. 1-2, and (ii) nucleic acid amplification using at least one primer that amplifies a transcript corresponding to a sequence as set forth in any one of SEQ ID No. 7-8.

13. The method of claim 1, further comprising (c) based upon a classification of said thyroid nodule as malignant or benign, designating a treatment modality for said subject.

14. The method of claim 13, wherein said treatment modality is selected from the group consisting of total thyroidectomy, near-total thyroidectomy, partial thyroidectomy, cosmetic debulking, radioactive iodine treatment, watchful waiting, thyroid hormone suppression therapy, total or near-total thyroidectomy followed by radioactive iodine ablation therapy and permanent thyroid hormone replacement therapy, and a combination thereof.

15. The method of claim 1, further comprising (c) based upon a classification of said thyroid nodule as malignant or benign, generating a report that designates said thyroid nodule as malignant or benign.