JP2014518086A - Determination of tumor origin - Google Patents

Abstract

Methods are provided for using gene expression measurements to classify or identify 54 cancer types in samples obtained from subjects in a clinical setting, such as formalin fixed / paraffin embedded (FFPE) samples.
[Selection] Figure 1

Description

Cross-reference of related applications

  This application is related to US patent application Ser. No. 11 / 145,307, filed Jun. 3, 2005. This US patent application claims priority to US Provisional Patent Application No. 60 / 577,084, filed June 4, 2004, and Application 11 / 422,056, June 2, 2006. The latter application claims priority to US Provisional Patent Application 60 / 687,174, filed June 3, 2005. These four applications are incorporated herein in their entirety.

Disclosure field

  This disclosure is relevant to the use of gene expression to classify human tumors. Classification is a profile in which the sequence used is a profile to be performed or 50 or more, optionally 5 or more, of gene expression represented by a pattern, among multiple tumor types. The disclosure thus includes the use of overlapping gene expression levels in multiple tumor types or tumors arising from specific tissues. The disclosure also provides for using more than 50 or optionally 5 or more specific gene sequences, which are in multiple tissue sources of the tumor, expression and so tumor or cancer type. Regardless of whether it is implemented in nucleic acid expression, protein expression or other expression formats, the tissue type to allow more accurate identification of the cancer and thus its treatment as expected of the subject from which the sample was obtained As tumor cells from or of tissue origin are included, the gene expression profile may be used to measure the cells containing the sample.

Summary of disclosure

  This disclosure does not go through anything related to the use of small or minimal measures (such as simply storage of non-freezing point (temperature)), as well as a new sample that has been frozen. In the case of formalin where the tumor in the cell containing the measurement and / or sample to fixate is fixed, gene expression that is taken from the subject in the clinical setting, where the embedded (FFPE) paraffin takes the sample of. The disclosure thus provides the ability to categorize samples into real situations facing hospitals and other laboratories performing tests on clinical FFPE samples. The sample may be a primary tumor sample or a tumor resulting from metastasis of another tumor. Alternatively, if not limited, cells within a blood sample, for example because the sample may be a cytological sample. In molecules, the disclosure also outlines an unknown cancer or tumor by predicting the tissue of origin for the cancer or tumor that may be seen.

  In the case of a tumor sample, the tumor must not have a classification that has been undergone by conventional pathology techniques, other than confirmation may be classified at the beginning or classified as “unknown primary” “Cancer” (CUP) or “tumor of unknown origin” (TUO) or “unknown major tumor”. The need for confirmation is particularly relevant to the 10% misclassification using standard techniques, considering an estimate of 5. Thus, the disclosure may be seen as being one of multiple possible tumor types as it provides a means of tumor or tumor sample for cancer identification or CID. The range of possible tumor types includes those previously disclosed and previously not assigned to unknown cancers or tumor types.

  In a first aspect of the disclosure, a sequence to be used of a profile in which classification is performed or 5 or more, optionally 50 or more, gene expression represented by a pattern. Regardless of whether implemented in nucleic acid expression, protein expression or other landmarks of gene expression, to allow more accurate identification of cancer and thus treatment thereof as expected for the subject from which the sample was obtained As tumor cells of tissue type or from tissue origin are included, the gene expression profile may be used to measure the cells containing the sample.

  The expression products of the represented sequences are disclosed herein as possible tumor types that may be found in multiple tumor types within a plurality or group. The expression level of the sequence may thus occur in multiple tumor types in the group. Furthermore, the range of expression levels may overlap between known tumor types in the group. The disclosed methodology for classifying or confirming tumor types may also be applied to the classification or identification of tissue sources of cells (eg, tumor or cancer cells).

Classification or identification may be performed by comparison of gene expression profiles, or more than 50, or optionally more than 5, patterns appear in the tumor sample in the appearance of the same represented sequence in multiple known tumor types Expressed the sequence. At least one of the sequences is represented in one or more of the known tumor types, among a number of known tumor types. Optionally, the range of expression levels of at least one sequence in one known tumor type overlaps the range of expression levels of the same sequence in one or more other known tumor types in the plurality.
In some cases, the overlap is 5% or more, 10% or more, 15% or more, 20% or more, 25% or more, 30% or more, 35% or more, 40% or more, 45% or more of other or other known tumors Occurs in over 50% of types.

In some embodiments, 2 or more, 3 or more, 4 or more, 5 or more, 10 or more, 15 or more, 20 or more, 25 or more, 30 or more, 35 or more, 40 or more, 45 or more, 50 or more, 55 or more, 60 or more or The majority of represented sequences are represented in one or more of the known tumor types.
Optionally, the range of expression is one known tumor type overlap with a range of expression levels of the same sequence in one or more other known tumor types as described above within each plurality of leveling Of sequences generally represented within.

  In certain embodiments, the disclosure is used to classify types among a group of 54 known tumors or cancers. Classification may be performed with considerable accuracy during the clinical setting. Disclosure of classifying among 54 known tumor or cancer types (as well as a subset of those tumor types) in a meaningful way is capable of over 50 represented sequences in the human genome On the basis of unexpected and unexpected discoveries, the basis for the part is formed. Furthermore, 5 out of 49 represented sequences may be used to classify types among 54 subsets of known tumors or cancers.

  On the discovery that it is not necessary to use supervised learning to confirm the expressed gene sequence in the correlation with different tumor types, the disclosure forms the basis in part. Thus, on the recognition of flattening any expressed sequence of 50 or more (even a random collection of represented sequences), the disclosure provides the necessary information content that forms the basis in the part. By including and classifying tumor cells from multiple or groups of known tissue or tissue origin that may be used to classify.

  In another aspect, the disclosure provides for sorting cells that contain a sample. And including tumor cells of tissue type or origin by measuring an expression level of 5 or more or optionally 50 or more copied sequences and comparing expression to classify cells containing the sample The number of cancer (or tumor) types in a number or group of known tumor tissue types is horizontal to that of the same transcriptional sequence as containing a type of cancer (or tumor) cell. Of the 54 known cancer types and subsets thereof, only a few, any more than 5, or optionally more than 50 may be used for classification in a meaningful way representing the sequence. The disclosure also reveals that the represented sequence is clearly or relatively correlated with one or more of the known tumor types compared to other known tumors (directly or indirectly represented) The basis in the part is formed on the observation that not only with those with expression levels (through interrelation with different sequences). Thus, in the disclosed, further embodiments, provided tumor or cancer samples to be used include expression levels of genes that are not expressed in strong or high correlation with one or more of the known tumor types for comparison of. In some cases, all of the genes used for classification may be non-correlates, or only some of the genes may be non-correlates. In an 85% embodiment, at least 90%, 75%, 50% or 25% of the expression level used correlates with one or more of the unknown tumor types.

  Disclosure may be performed by assessing the expression level of a gene sequence that the sequence did not need to be selected based on the correlation between multiple known cancer or tumor type members and their same level of expression . Thus, by way of non-limiting example, gene sequences need not be selected based on their correlation values with cancer or tumor type or ranking based on correlation values. Furthermore, the disclosure may be practiced by the use of gene expression levels that are not necessarily correlated with one or more other genes whose expression level (s) was used for classification. So in additional embodiments, at least one other gene expression used for classification (non-participating) in that the ability for the expression level of one represented sequence to the function being classified is not redundant Level ability.

  The disclosure may be applied to identify the cause of cancer in a patient in various cases, including but not limited to identifying the cause of cancer in a clinical setting. In certain embodiments, the identification is made by classification of cells that contain samples known to contain cancer cells, but the origin of those cells is unknown. In other embodiments, as including one or more cancer cells is followed by identification of the origin of those cancer cells, the identification is made by classification of the cells containing the sample. In a further embodiment, the disclosure is performed on a sample from a subject with a history prior to cancer and identification that the cancer is from a previous cause of cancer made by cell classification or to a new origin.

Additional embodiments include multiple cancers containing them in the same organ or tissue, and the disclosure is used to determine the cause of each cancer, as well as whether the cancers are of the same origin.
On discovery that the expression level of a particular gene for which a sequence can be used classifies within a tumor type with greater accuracy than the expression level of a random group of gene sequences, the disclosure also forms the basis in part Is done. In certain embodiments, the disclosure provides for the represented sequence of expression in the human genome that classifies among the 54 known cancer types with a disclosed set of accuracy of the expression level used. The disclosure is thus provided for identification and used to confirm the origin of the 54 cancer types used in the gene expression pattern (or profile or “signature”) based on the represented sequence. Also have good information. The disclosure also provides a level of these represented sequences that classify among a subset of 54 cancer types for use in expression. In addition, the disclosure provides a subset of the disclosed represented sequences that classify, among 54 subsets of cancer types used (eg, 5 or more) of expression levels. Depending on the number of tumor types, accuracy varying from 80% or more to 100% may be achieved.

  The disclosure is based on the expression level of gene sequences in a set of known tumor cells from different tissues and different tumor types. Regardless of whether they are implemented in nucleic acid expression, protein expression or other expression formats, these gene expression profiles (different) as they include tumors of a specific known type and / or a specific known origin or cell type The gene sequence in a known tumor cell / type) may be compared to the expression level of the same sequence in an unknown tumor sample to confirm the sample. The disclosure provides for the benefits of cancer and thus more accurate identification of the treatment and / or the subject from which the sample was obtained, as well as the expected clinical setting, including survival, and / or the likelihood of cancer recurrence after the treatment. In.

  The disclosure also forms the basis for the part on discovery. And it has the sequencing ability expressed, using more than 5 or optionally more than 50, the classification of inevitably and effectively classifying among more than 2 known tumor types described here Remove one or more known tumor types from consideration during. This reflects the lack of need to select genes at an expression level that is highly correlated with all tumor types within the scope of the classification system. Disclosure with multiple genes that flatten out whether the expression is not highly correlated with either individual tumor type or multiple types within a group of tumor types that are categorized in various ways May be implemented. As the approach is opposed to “dominate” tumor types based on positive correlations, using genes that are based on selection and highly correlated, maybe other tumor types are “ruled out” This is in contrast to others who do not.

  Tumor sample classification Excluding one or more other cancer types, one of the possible cancer types described here is of course based on the level of confidence as described below. Where the level of confidence is low or an increase in the level of confidence is preferred, the classification can be made simply to a specific tissue origin or cell type height for the cancer in the sample. In addition to this, and where the tumor sample is not immediately classified, it is a single tumor type (disclosure permit where the possible classification of a few cancer types of the sample is described here together). This advantageously provides the patient from whom the sample was obtained because of the ability to reduce the number of possible tissue types, cell types and tumor types as to what to consider for therapy selection and management. .

  The disclosure provides a non-subjective means of identifying one or more cancer tissue sources and / or cancer types of an afflicted subject. This disclosure is an objective gene where subjective interpretations may have previously been used to measure tissue sources and / or cancer types (as well as cancer treatments based on expectations and / or measurements thereof). An expression pattern is provided, which may be used alone or in combination with subjective criteria that provide a more accurate identification of cancer classification. The disclosure is particularly advantageously applied to a sample of a second stage or metastasized tumor, but a cell containing a tissue source and / or a tumor type desired (including a primary tumor sample) Any of the above measured on an objective basis may also be used in the disclosure. Of course, the last measurement of the class may be based on a combination of objective and non-concrete (or subjective / partially subjective) criteria.

  The disclosure includes its use as part of a patient's clinical or medical concern. In addition to using gene expression profiles in this way, described herein for analyzing cells containing samples from cancers that measure tissue sources and / or subjects afflicted with cancer tumor types As such, the profile may also be used as part of the method to measure the expectation of cancer in the subject. Tumor / cancer and / or anticipation classification is a subject that may be selected or measured or said to be used to alter therapeutic treatment. Thus, the disclosed classification method may be directed to the treatment of illnesses. It is then diagnosed in the whole or part of the classification. Giving diagnosis or appropriate anti-tumor agent or therapy management or withdrawal or altering anti-tumor agent or therapy may be used to treat cancer.

  The details of one or more embodiments of the disclosure are set forth in the accompanying drawings and description below. Other features and advantages of the disclosure will be apparent from the drawings and detailed description, and from the claims.

The range of expression levels of the disclosed first transcribed sequence in 39 of the 54 disclosed tumor types is shown. The range of expression levels of the disclosed second transcriptional sequence in 39 of the 54 disclosed tumor types is shown.

Definition

  As used herein, a “gene” is a polynucleotide that encodes a distinct product, whether of RNA or proteinaceous nature. It is understood that it may be possible for multiple polynucleotides to encode separate products. The term is based on alleles and polymorphisms, the location of genes that encode the same product, or on the chromosome, and their ability to recombine during normal mitosis (increase, decrease, or regulation of function). Includes functionally related analogs).

  “Sequence” or “gene sequence” As used herein, assembled nucleic acid molecules or polynucleotides are in a nucleotide-based discrete order. The term includes a base sequence that encodes a discrete product (ie, a “coding region”), whether protein or protein in nature, wherein multiple polynucleotides may be capable of encoding a discrete product. It is understood that alleles and polymorphisms of a human gene sequence may be present and disclosed to confirm the expression level (s) of a gene sequence or allele or used during the execution of that polymorphism. It is also understood that discrimination of alleles or polymorphisms depends on the part to the position of the chromosome and the case in the ability to recombine during mitosis.

  The sequence that is “represented by the sequence” is within the range of the cell and by the cellular process. In order to detect the represented sequence, a region of the sequence that is unique compared to other represented sequences may be used. The represented sequence may encode a polypeptide product or is known to encode any product. Thus, the represented sequence may include anything other than an open reading frame or an open reading frame. Non-limiting examples are about 8 or more, about 10 or more, about 12 or more, about 14 or more, about 16 or more, about 18 or more, about 20 or more, about 22 or more, about 24 or more, about 26 or more, about 28 or more. Approximately 30 or more contiguous nucleotides that include the region or within the represented sequence may be used. As used in the “available” period, the previous sentence references from a fixed number to an increase or decrease of one. The physical form of the represented sequence may be an RNA molecule or a corresponding cDNA molecule.

  The term “correlates” or, for example, refers to an association between the expression of one or more genes and another event, without limitation being “correlation” or its equivalent (physiological expression types). Or characteristics) eg tumor type.

  A “polynucleotide” is a polymeric form of nucleotides of any length, either ribonucleotides or deoxyribonucleotides. This term refers only to the original structure of the molecule. Thus, this term includes double- and single-element DNA and RNA. It also includes art, methylation, “cap”, substitution of one or more naturally occurring nucleotides by analogs and internucleotide modifications (eg, uncharged linkages (eg, phosphorothioates), as well as unmodified forms of polynucleotides. , Phosphorodithioates, etc.)), including known types of modifications including known labels.

  The term is used in a broad sense to mean that producing an “expanding” expansion product can be made enzymatically with DNA or RNA polymerase. As used herein, “enlargement” generally refers to the process of generating multiple copies of a requested sequence (especially those of a sample). “Multiple copies” means at least two copies. “Copy” does not necessarily mean complete sequence complementarity or identity to the template sequence. Methods for enlarging mRNA are generally known in the art and include reverse copy PCR (RT-PCR) and quantitative PCR (or Q-PCR), or really time the PCR. Alternatively, the RNA may be directly labeled into the corresponding cDNA by known methods in the art.

  By “matching” is meant that a nucleic acid molecule shares a significant amount of sequence identity with another nucleic acid molecule. Substantial at least 95% means, usually at least 98% and more usually at least 99% and sequence identity is determined using the BLAST algorithm as described in Altschul et al. (1990) J. Mol. Biol 215: 403-410 (use the published default settings (ie parameters w = 4, t = 17)).

A “microarray” is not limited in area (having each defined area), eg, linear or two-dimensional or three-dimensional (3D) of independent devices formed on the surface of a strong support. And (phase) solid phase) (glass) plastic or synthetic film. The density of discrete regions on the microarray is determined by the total number of immobilized polynucleotides detected on at least a single intense phase-supported surface, around 50 / cm ² at least around 100/100 cm ² to at least about or until about the 1,000 / cm ² or more, 500 / ^cm 2. The sequence may comprise less than about 500 in total, about 1000, about 1500, about 2000, about 2500 or about 3000 immobilized polynucleotides. As used herein, a DNA microarray is a sequence of oligonucleotides, or a polynucleotide placed on a chip or other surface that yields a hybrid or expands from a sample or clones of polynucleotides Made. Since the location of the survey is known within each particular group of sequences, the identity of the sample that the polynucleotide can do is determined based on their binding to a particular location in the microarray. Arrays of any size used by the microarray for options may be used during the disclosure. It includes an arrangement of one or more positions in a two-dimensional or three-dimensional arrangement in a solid phase to detect the appearance of a single gene sequence. In certain embodiments, microarrays for use in the present disclosure may be prepared by photolithographic techniques (from the 3 ′ end of the nucleic acid probe on the surface, such as the integration of nucleic acid investigations) or nuclei followed by a testimony on a solid surface By integration.

  Where the disclosure relies on identification of gene expression, certain embodiments of the disclosure of sample cells to polynucleotides that are specific to a particular gene sequence are expressed by crosses of mRNA or expanded or cloned versions thereof. To decide. This type of polynucleotide comprises at least approximately 16, at least approximately 18, at least approximately 20, at least approximately 22, at least approximately 24, at least approximately 26, at least approximately 28, at least approximately 30, or entirely approximately 32 consecutive gene sequences In basepairs, it is not found in other gene sequences. As used in the “available” period, the previous sentence references from a fixed number to an increase or decrease of one. Other embodiments are polynucleotides at least or at least about 100 or at least about 50, or about 50 or at least or at least or at least or at least or at least or at least or or at least or other genes. For the 500 consecutive bases of the missing sequence in the sequence, for 450, for 400, for 350, for 300, for 250, for 200, 150. As it is used during the "available" period, the sentence references from a fixed number to a 10% increase or decrease. Longer polynucleotides may of course contain minor inappropriate combinations (eg, through the presence of mutations) that do not alter the hybridization to the sample nucleic acid. Such polynucleotides may also be referenced as one polynucleotide that can produce a hybrid in the sequence of the gene or a unique portion thereof digs deeper. And will be described here. Such polynucleotides may be labeled to aid in their detection. The sequences may be those of mRNA encoded by a gene (corresponding cDNA to such mRNA) and / or expanded versions of such sequences. In certain disclosed embodiments, the polynucleotide probe is immobilized on a sequence (other rigid support device) or in individual points that localize the probe.

In other embodiments of the disclosure, all or part of the gene sequence may be expanded and detected by methods such as polymerase chain reaction (PCR) and variations thereof, for example, but not limited thereto Means to measure the initial amount of mRNA (including quantitative PCR (Q-PCR)) copy PCR (RT-PCR) and reverse real-time PCR (copy for each sequence in the sample (Optionally real-time RT-PCR or real-time Q-PCR.). Such a method utilizes one or two primer coats that are complementary to a portion of the gene sequence, where the primer coat is used for primary nucleic acid synthesis.
Newly synthesized nucleic acids may optionally be labeled and detected directly by hybridization to the disclosed polynucleotides. Under circumstances that allow their hybridization, newly synthesized nucleic acids may be contacted with the disclosed polynucleotides (including sequences). Additional methods of detecting expressed nucleic acid expression include RNAse protection assay evaluation including liquid phase hybridization and in situ hybridization of cells.

  Alternatively, and in further embodiments of the disclosure, gene expression may be determined by analysis of the expressed protein in important cell samples, depending on the individual gene product (protein) or in said cell sample Use of one or more antibodies specific for one or more epitopes of the proteolytic fragment thereof or in the subject's body fluid. The cell sample uses an antibody labeled against cell surface landmarks followed by a fluorescence activated cell sort (FACS), for example, where the epithelial cells may be one of the breast cancers enriched from the subject's blood. Such antibodies may be labeled to allow their detection after being tied to a gene product. For the implementation of the disclosure, suitable detection methodologies include, but are limited (immunohistochemistry of the cell or tissue containing the sample), the enzyme is the cell containing the tissue or blood sample, mass spectroscopy and immuno- Immunosorbent assay evaluations (ELISAs) were included, including PCR antibody sandwich assay evaluations.

  The term “label” or “labeled” refers to a configuration that can generate a signal that is found to indicate the presence of a labeled molecule. Suitable labels include radioisotopes, nucleotide chromophores, enzymes, substrates, fluorescent molecules, chemiluminescent halves, magnetic strips, bioluminescent halves, and the like. For this reason, the label is in any configuration found by the spectroscopic (photochemical) biochemical, immunochemical, electrical, optical or chemical means.

The term “support” refers to conventional supports such as beads, pieces, metering rods, fibers, filters, membranes and silane or silicate supports (eg glass slides).
“Expression” and “gene expression” include copying and / or translation of nucleic acid material. The expression level of the represented sequence may optionally be standardized by reference, or comparison to the expression level of one or more controls represented the gene. These “normalizing genes” have a level of expression that is relatively constant in all multiple members or groups of known tumor types.

  As used herein, “includes” and the meanings that they include, as long as their relatives are used in; there is an equivalent “includes” and its corresponding relatives in the term.

  A situation that “allows” an event to occur or a situation that is “suitable” for an event to occur, such as a crossing, is stuck with an extension, etc., or a “appropriate” situation causes such an event to occur This is a situation where there is no prevention. Thus, these status permits are increased, facilitate and / or contribute events. For example, such situations (known in the art and described herein) depend on the nature of the nucleotide sequence, temperature and buffer conditions. These situations also depend on what events are required, such as crossings, rips, shore extension or copying.

  As used herein, the sequence “mutation” refers to the interest being compared to the reference sequence, whatever the sequence change disclosed herein within the sequence of the gene. For mechanisms such as substitution, deletion or insertion, sequence mutations involve single nucleotide changes or changes of multiple nucleotides in the sequence. As used herein, a single nucleotide polymorphism (SNP) is also a sequence mutation. As the present disclosure is based on the relative level of gene expression, as disclosed herein, mutations in non-coding regions of the gene may also be analyzed during the execution of the disclosure.

  “Detect” or “detect” includes any means of detecting. And includes direct and indirect measurements of gene expression and level of change therein.

  Unless defined otherwise, generally, all technical and scientific terms used herein have the same meaning as this disclosure is understood by a person skilled in the art.

Detailed Description of Embodiments

  This disclosure provides a method for the use of gene expression information to classify cancer and / or tumors in an objective manner than is possible with conventional pathology techniques. The disclosure describes the genetic sequence used to group tumor samples together for multiple tumor types (eg, the tumor types described under and in US Patent Publications US2006 / 0094035 and US2007 / 0020655). On the result of randomly decreasing the number, the basis in the part is formed. The total counts for 16,948 genes. Then, as described herein, down is larger than filtered, the displayed or invariant signal in the sample used is set based on the removal of the gene used low for cross-validation and prediction accuracy.

  Thus, in the first aspect, the disclosure provides (or from) a type of tissue (or as it is of tissue origin) that includes a cancer or tumor cell that classifies a cell containing a sample. The method includes determining or measuring an expression level of 5 or higher, or optionally 50 or higher. And the cells in the cell as the level of the tumor cells of a kind of tissue from multiple tumor types based on the expression level of said sequence in the cells of the sample being compared to the expression level in the known tumor Including a sample from which transcription sequences from the subject are obtained and classifying the sample. As used herein, “plurality” refers to two or more states.

  Based on comparison of expression levels of analyzed transcript sequences in cells of known tumor samples to classify to their expression levels and / or cells of known non-tumor samples. In addition, the same comparison within a known tumor sample and / or a known non-tumor sample based on or based on a comparison of the expression level of the analyzed transcript sequence to the appearance of a reference sequence within the same sample to classify. Non-limiting example (a sequence may be determined for a set of known tumor samples to provide a database in which the determined expression is compared in cells containing levels detected or containing samples from the subject. Gene expression level). Even within a sample, the expression level (s) of the gene sequence (s) is compared with the expression level (s) of the said sequence (s) in a normal or non-cancellous cell, preferably from the same sample or subject. Also good. As described and under and in disclosed embodiments utilizing Q-PCR or real-time Q-PCR, the expression level may be compared to the expression level of a reference gene in the same sample, or expression Level ratios may be used.

  Alternatively, the selection of the represented sequence to use may be random, with selection based on various criteria. In one non-limiting example, genes may be selected based on unsupervised learning, including assembled techniques for sequencing. As another non-limiting example, the selection may be to reduce or transfer redundancy regarding their ability to classify the tumor type. For example, gene sequences are selected based on the lack of correlation between their expression and the appearance of one or more other gene sequences used to classify. This spans a large number of samples with the expression level of each other gene in the data set that generates the correlation matrix of correlation coefficients, and the expression level of each gene sequence in the expression data set for correlation. Achieved by evaluating. These correlation measurements may be performed directly between the expression of each pair of gene sequences or indirectly without direct comparison of the expression values of each pair of gene sequences.

  Various correlation methodologies may be used during the correlation of expression data for individual gene sequences within the dataset. Non-limiting examples include parametric and non-parametric methods as well as methodologies based on mutual information and non-linear approaches. Non-limiting examples of parametric approaches include Pearson correlation (or Pearson r is also linear or product-moment correlation for reference) and cosine correlation. Non-limiting examples of non-parametric methods include Spearman's R (or rank-order) correlation, Kendall's Tau correlation, and Gamma statistics. Each correlation methodology can be used to determine the level of correlation between the occurrences of individual gene sequences in the data set. The interrelationship of all sequences by all other sequences is the most conceivable matrix. In a non-limiting example using Pearson correlation, the correlation coefficient r in the method is an indicator of the level of correlation used. When other correlation methods are used, r is 0.5, or, in particular, at 0.25, or with the recognition of the equivalent level of correlation that is consistent with r. Similar correlation coefficients may be used.

  Correlation coefficients may be selected as required to reduce the number of gene sequences that are correlated to various numbers. In certain disclosed embodiments using r, the selected coefficient value may be approximately 0.25, or for 0.3 or 0.4 or more (approximately 0.45 or more), Alternatively, it may be higher for more than about 0.5 or more (approximately 0.35 or more). Selective disclosure of co-action value means that they are probably not included in the subset, correlated by their value or higher where there is expression between gene sequences in the dataset. Thus, in certain embodiments, the method is used for classification of one or more (in classification) represented in the correlation above the required correlation coefficient by another gene sequence in the tumor type data set. Not) includes excluding or deleting gene sequences. It is pointed out that the situation of gene sequences that are not correlated with any other gene sequence may be about which case they are not necessarily moved but used during classification .

  Thus, expression is approximately 10%, approximately 20% or greater, approximately 30% or greater, approximately 40% or greater, approximately 50% or greater, approximately 60% or greater, approximately 70% or greater, where level of the gene sequence is horizontal. More than about 80% or about 90% or more may be used during the execution of the disclosure, rather than uncorrelated with that of another one of the genes used by the sequence. The correlation between expression levels is below about 0.9, about 0.8, about 0.7, about 0.6, about 0.5, about 0.4, about 0.3 or about 0.2. It may be based on the value. The ability to classify between classes with the exclusion of the expression level of a gene sequence is a book because the appearance of a gene sequence in a subset correlates with the appearance of a gene sequence that is locked out of the subset. So the information was not lost because the information based on the appearance of the excluded gene sequence is further represented by the retained sequences in the subset. Thus, the appearance of a subset of gene sequences has information content and / or battery characteristics (or expression types) associated with the property. This is an additional table using those subsets of disclosures that are relevant to the classification of additional tumor type classes not included as part of the original gene expression dataset that can be filed and classified Based on the subset of information and the redundancy of information between the occurrences of the sequence within the sequence belongs. Thus, the disclosure may be used to classify cells as being more than a known class of tumor types is what produced the original gene expression dataset.

  Selection of gene sequences based on reducing the correlation of expression to a particular tumor type may also be used. This also reflects the discovery of this disclosure. And based on the observation that the expression level most correlated with one or more tumor types was not necessarily the highest value among the different tumor types. This is reflected by the ability to use randomly selected gene sequences for classification, both of which reflect the specific sequence used, as described here, top-level correlation in one or more tumor types Not represented by Thus, the disclosure may be performed without the most significant P-value based gene sequence or gene expression and ranking selection based on the correlation of one or more tumor types. Thus, the disclosure may be implemented without using a first-class based methodology (eg, Kruskal-Wallis H-test).

  The gene sequences used in the implementation of the disclosure may include those that have been found to be represented in a particular known tumor type (eg, estrogen receptor manifestations). And it has been shown to be interrelated in certain breast and ovarian cancers. In one embodiment of the disclosure, however, the disclosure is implemented using the expression levels of overlapping gene sequences in two or more of the known tumor types. In some cases, one or more of the transcribed sequences are known tumor types, at least 50 of known tumor types, at least 45 of known tumor types, at least 40 of known tumor types, known At least 35 of the tumor types, at least 30 of the known tumor types, at least 25 of the known tumor types, at least 20 of the known tumor types, at least 10 of the known tumor types or disclosed Have overlapping expression levels in all of at least 5 of the known tumor types of the group.

  2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more , 19 or more, 20 or more, 22 or more, 24 or more, 26 or more, 28 or more are known from the performance of the disclosed method, the number of transcription sequences represented by the range of overlapping levels in 2, or the disclosed group 30 or more, 32 or more, 34 or more, 36 or more, 38 or more, 40 or more, 42 or more, 44 or more, 46 or more, 48 or more, 50 or more, 52 or more, 54 or more, 56 or more, 58 or more, 60 or more, 62 or more, 64 or more, 66 or more, 68 or more, 70 or more, 72 or more, 74 or more, 76 or more, 78 or more, 80 or more, 92 or more, 94 or more, 96 or more, 98 or more , Over 100, 05 or more, 110 or more, 120 or more, 130 or more, 140 or more or 150 or more. Of course, the above values may be used with one or more transcription sequences with expression levels that do not overlap between two or more members of a group of known tumor types. Based on the number of known tumor types in the multitude, the skilled person will be able to copy any sequence and many transcribed sequences with the appropriate combination of many tumor types by overlapping the appearance of the sample. Flattening disclosures in more than one tumor type may be implemented.

  While the disclosure is primarily described with respect to human subjects, samples from other subjects may also be used. All that is needed is a comparison of the ability to assess the expression level of a gene sequence in a plurality of known tumor samples, where expression may be unknown or level in the test sample. Thus, the disclosure may be applied to the sample from any organism for which multiple represented sequences and multiple known tumor samples are available. One non-limiting example is a disclosure application to a mouse sample. The availability of the mouse genome is then based on the detection of the represented murine sequence and the availability of known mouse tumor samples or capabilities to allow obtaining known samples. Thus, the disclosure is considered for the use of mammals, primates and other samples including but not limited to those of animals used in clinical tests (eg, mice, mice, rabbits, dogs, cats and chimpanzees). Example.

  While the disclosure is performed immediately using the cell containing the sample, any nucleic acid containing sample that may be analyzed for gene expression leveling may be used during the execution of the disclosure. . Without limiting the disclosure, the disclosed sample may be suspected or known to contain tumor cells. Alternatively, the disclosed sample may be a “tumor sample” or “tumor containing a sample”, or “tumor cells containing a sample” of tissue or fluid separated from an individual, or dangerous Suspiciously suffering from cancer by developing into a state. For example, non-limiting examples of samples for disclosure include, but are not limited to, fresh samples or frozen samples to contain clinical samples (fixed samples). The sample may be a sound, cytology sample (including fluid from blood or ascites or capsule cavity, including other bodily fluids) or tissue specimen. And so long as appropriate cells or nucleic acids are available for measuring gene expression levels, it contains at least some information regarding the relationship of the original location of the cells in the sample. The disclosure is based on the discovery that the results obtained in the extremely cold tissue areas can be applied to the situation rightly in fixed tissue or cell samples and extended to new samples.

Non-limiting examples of fixed samples include those of Boudin, glutadehyde, acetone, alcohol, or any other such as those used to fix cells or tissue samples for immunohistochemistry (IHC) Staining agents also include those that can be placed with formalin or formaldehyde (including FFPE samples). Other examples include color-fixing agents in which sudden cells have associated nucleic acids and proteins. Given the possible complications in handling frozen tissue specimens (e.g., needing to maintain their frozen state), the disclosure discloses non-frozen samples containing cells or tissues from blood or other bodily fluids ( For example, a fixed sample, a new sample) may be performed and a minimal sample was handled.
In certain applications disclosed, immunohistochemistry-based analytical assessments where samples were not classified, for example, without limitation, using standard pathology techniques.

  In certain disclosed embodiments, the sample is categorized as comprising a type of tumor cell selected from following 54 and a subset thereof: adrenal-cortical tumor, adrenal chromaffin cell tumor, brain tumor , Breast adenocarcinoma, cervical adenocarcinoma, cervical squamous cell carcinoma, cholangiocarcinoma, endometrial adenocarcinoma, esophageal squamous cell carcinoma, gastrointestinal stromal tumor, gallbladder adenocarcinoma, gastroesophageal tract Adenocarcinoma, seminomatous sex cell tumor, nonseminomatous sex cell tumor, salivary gland, squamous cell carcinoma, colorectal adenocarcinoma, small intestine adenocarcinoma tumor (clear cell renal cell carcinoma, non-staining cell renal cell carcinoma , Papillary renal cell carcinoma, hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma, melanoma, meningioma, mesothelioma, small / large cell neuronal internals Lung cancer, neuroendocrine pancreatic cancer, merkel cell carcinoma, gastrointestinal carcinoid, pulmonary carcinoid, clear cell adenocarinoma, endometrioid adenocarcinoma, mucinous adenocarcinoma, serous adenocarcinoma, pancreatic adenocarcinoma, prostate), adenocarcinoma Histiocytoma of malignant fiber, first neuroectodermal tumor, leiomyosarcoma, liposarcoma, osteosarcoma, synovial sarcoma, sexually encoded stromal tumor, basal cell carcinoma, squamous cell carcinoma of the skin, thymiclar, follicular Adenocarcinoma of cancer, bone marrow cancer, transitional cell cancer, bladder bladder and squamous cell carcinoma.

These 54 tumor types are consistent with the following tissue types: adrenal tissue (adrenal-cortical tumor and adrenal chromaffincytoma), brain tissue (brain tumor), breast tissue (breast adenocarcinoma) ), Cervical tissue (cervical adenocarcinoma and cervical squamous cell carcinoma), bile duct tissue (cholangiocarcinoma), endometrial tissue (endometrial adenocarcinoma), esophageal tissue (esophageal squamous) Epithelial cell carcinoma), gastrointestinal tissue (gastrointestinal stromal tumor or GIST), squamous bladder tissue (squamous bladder adenocarcinoma), gastroesophageal tissue (gastroesophageal adenocarcinoma), sex cell tissue (seminomatous sex) Cell tumors and nonseminomatous sex cell tumors), head and neck tissues (saliva and squamous cell carcinoma tumors), intestinal tissues (colorectal adenocarcinoma and small intestinal adenocarcinoma), kidney assembly (Clear cell renal cell carcinoma, non-staining cell renal cell carcinoma and papillary renal cell carcinoma), liver tissue (hepatocellular carcinoma), lung tissue (lung adenocarcinoma and lung squamous cell, cancer) (lymphocyte (lymphoma) )) Melanocytes (melanoma), meningeal tissue (meningioma), mesothelioma (mesothelioma) tissue, neuroendocrine tissue (small / large cell neuroendocrine lung cancer, neuroendocrine pancreatic cancer) , Merkel cell carcinoma, gastrointestinal carcinoid and pulmonary carcinoid), ovarian tissue (clear cell adenocalinoma, endometrioid adenocarcinoma, mucinous adenocarcinoma and serous adenocarcinoma), pancreas (pancreatic adenocarcinoma) tissue, prostate tissue (prostate Adenocarcinoma), sarcoma tissue (malignant fibers histicytoma or leiomyosarcoma, liposarcoma MF (First neuroectodermal tumor or PNET) (osteosarcoma) and sarcoma secreting synovial fluid (sexually encoded stromal tissue (sexually encoded stromal tumor)) skin tissue (basal cell carcinoma and squamous cell carcinoma of the skin) Thymic tissue (thymic cancer / thymoma), thyroid tissue (fullic / papillary cancer and bone marrow cancer) and bladder tissue (transplanting cell cancer (bladder adenocarcinoma) and squamous cell carcinoma of the bladder).
As including a subset of tumor cells of any of the types listed above, the disclosed methods may also be applied to sort the cells containing the sample.

The subset size may be small. And 2, 3, 4, 5, 6, 7, 8, 9 or 10 of the tumor types described above. Alternatively, the size of the subset may be any integer up to the full size of what to set. Thus, the disclosed embodiments are 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53 or of the above type The classification is included in 54 of them. In certain embodiments, the subset consists of certain tumor types of the same tissue or organ type. Alternatively, the subset consists of tumor types of different tissues or organs. It cannot be overemphasized that the disclosure is not based on any particular combination of tumor types and that all possible combinations of the 54 known types mentioned above are clearly contemplated. In the disclosed embodiment.
The finite possibility of explicitly writing all combinations of 54 tumor types is to require any form on the gist of discovery and disclosure.

  Classification within a subset of the above tumor types is reported in US patent publications US 2006/0094035 and US 2007/0020655 as well as Ma et al. (Arch. Pathol. Lab. Med., 130: 465-473, 2006). However, the immediate disclosure is at least an adrenal-cortical tumor, a salivary gland tumor, a squamous cell carcinoma, a neuroendocrine-pancreas cancer, a merkel cell carcinoma, a lung carcinoid, the first neuroectodermal tumor, the sexually encoded stroma It appears to be the first noted successful identification of tumors, thymic cancer, adenocarcinoma of the bladder and squamous cell carcinoma of the bladder. Thus, and in certain embodiments, a group of known tumor types includes one or more types from this list.

  Disclosure is in human “transscriptome” (representing a portion of the genome) that may be performed at an expression level of about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more About 40 or more, about 45 or more, or about 50 or more copied sequences. In certain disclosed embodiments, the copied genes may be randomly picked or may include all or certain of the specific gene sequences disclosed herein. Classification with approximately 55%, approximately 60%, approximately 65%, approximately 70%, approximately 75%, approximately 80%, approximately 85%, approximately 90% or approximately 95% or more accurate can be performed immediately Use of the disclosure.

  In other embodiments, the gene expression levels of other gene sequences may be determined along with the above-described measurements of expression levels for classification. One non-limiting example of this is seen in the case of microarray-based platforms to determine gene expression, where the appearance of other gene sequences is also measured. Where those other expression levels are not used in comparison to expression in known tumor types, they may be considered “excess” transcript sequences and results that are not important to the implementation of the disclosure. In addition to this, and where those other expression levels are used in the classification, they are within the scope of the disclosure. And the explanation of using the matter does not necessarily exclude the expression level of the additional sequence to use. In certain embodiments, the disclosure includes one or more “excessive”, such as those that may provide redundant information to one or more other gene sequences used in the disclosed methods. “Of expression levels from gene sequences.

  Sample Classification Essentially containing cells of one of the above tumor types classifies the tissue or organ site origin of the sample, as the sample is the specific tissue or organ site of interest from which the sample was obtained The method may be applied to the classification of tumor samples. This application of disclosure is particularly useful in cases where the sample is a tumor that is the result of metastasis by another tumor. In certain disclosed embodiments, tumor samples are classified according to one of the following 30 known tissue types: adrenal tissue, brain tissue, breast tissue, cervical tissue, bile duct tissue, endometrium Tissue, esophageal tissue, gastrointestinal tissue, squamous bladder tissue, gastroesophageal tissue, sex cell tissue, head and neck tissue, intestinal tissue, kidney tissue, liver tissue (lung tissue, lymphocyte, melanocyte, marrow) Membrane tissue, mesothelial tissue, neuroendocrine tissue, ovarian tissue, pancreatic tissue, prostate tissue, sarcoma, sexually encoded stroma tissue, skin tissue, thymus tissue, thyroid tissue and bladder tissue).

  Classification of cells containing sample Having essentially one tumor cell of the 54 tumor types disclosed above classifies the tissue or organ site origin of the sample. For example, sample identification is necessarily squamous cell carcinoma of the cervix to classify the tumor, rather than the origin of the cervix (squamous cell type (and thus non-epithelial in origin) Rather about the epithelium)). It also means that the tumor was not necessarily a sex cell within its origin. Thus, as it is a particular tissue or organ site of a subject or patient, the disclosed method may be applied to the classification of tumor samples. This application of disclosure is particularly useful in cases where the sample is a tumor that is the result of metastasis by another tumor.

  Implementation of Disclosure to Classify Cells Containing Samples Using an Appropriate Classification Algorithm that Utilizes Supervised Learning to Accept 1, Having a Tumor with a Cell of One of the Kinds Above 2) level-set training of gene sequence occurrences in multiple known tumor types and 2) genes in one or more cells of the sample classifying the same sample as having one cell of the tumor type Level of expression. Such algorithms are known to the skilled person and have been described elsewhere. The level of expression may be provided based on the signal in any format. And as described herein, includes nucleic acid expression or protein expression.

  The disclosed embodiments include the materials described herein for identifying the cause of cancer from the use of the method and the patient. Given a sample containing tumor cells in this way, the tissue origin of the tumor cells is confirmed by use of the present disclosure. One non-limiting example is a burned lymph node containing cancer cells, which may be the subject. The cells may be from the tissue, or the lymph nodes or organs flowing out to it may be from another tissue source. As a particular tumor or tissue type (or origin), the present disclosure may be used to classify cells, which allows for the identification of the source of cancer cells. In other non-limiting examples, the sample (eg, from a lymph node) contains cells, and it is the first to classify at least one cell to be analyzed. By being. This is then used to identify the source of cancer cells in the sample. Both of these are examples of the advantageous use of the disclosure that keeps diagnostic tests on the time, effort and cost of using other guns.

  In a further embodiment, the disclosure is performed on a sample from a subject with a previous history of cancer. Non-limiting examples include the cell containing the sample (from the lymph node or elsewhere), whether the cell is then the same or from a different tissue in the previous cancer, and this disclosure determines Things that may be used to do so are known to include cancer cells and may be of interest. This application of the disclosure may also be used to identify new major tumors (eg, cases where new cancer cells are found in the liver of a subject who previously had breast cancer). The disclosure is the result of metastasis from a previous breast cancer (or whether previously confirmed from another tumor type) that may be used to identify new cancer cells or liver New major outbreak of cancer. The disclosure may also be applied to a tissue sample or multiple cancers known to determine the cause of each cancer, as well as whether the cancers are of the same origin.

  While the disclosure may be performed using expression levels of a random group of represented gene sequences, the disclosure also provides exemplary gene sequences for implementation of the disclosure. The disclosure includes a group of 87 gene sequences, five or more that may be used in the execution of the disclosure. The gene sequence classifies the gene from which the sequence from the additional sequence set in 87 may be used in conjunction with the measurement of expression level as long as the expression is level. A non-limiting example of such an embodiment of the disclosure is a method in which the base platform is disclosed by measuring along with expression levels of multiple other sequences from 5 or more of the gene sequences of sputum expression 87, wherever Use of the microarray that was used to perform. Where those other expression levels are not used in the classification, they may be considered “excess” transcript sequences and optional results in performing the disclosure. In addition to these, and other expression levels used in the classification, the above described sequences not necessarily exclude additional sequences used where they are not used where they are within the scope of the disclosure Of expression level.

Representative and non-limiting mRNA sequences consistent with a set of 87 gene sequences have been previously reported in US Patent Publications US2006 / 0094035 and US2007 / 0020655 for the purposes of the disclosure. By the following table where ATF shows ascites tumor fluid, a list of confirming information is provided, including registration number, Gene Symbols and Description. And EGF is epidermal growth factor and Cl is chronic lymphocytic leukemia.
table

As understood by the skilled person, the detection of the occurrence of any of the above identified sequences may be performed by the detection of the occurrence of any suitable part or a fragment of these sequences. Desirably, the portion is large enough to contain a unique sequence as compared to other sequences represented in the cell containing the sample. In addition, the skilled person will recognize that the disclosed sequence represents one shore of the double stranded molecule and may be detected on either shore as an indicator of the appearance of the disclosed sequence.
This is because the disclosed sequence is represented as the RNA molecule in the cell, which is preferably converted into a cDNA, becomes lighter to manipulate and detect. The resulting cDNA molecule may have a sequence of RNA represented similar to those of the shore complementary to it. Thus, RNA sequence banks or complementary banks may be detected. Of course, it is possible to detect the expressed RNA without conversion to cDNA.

  In certain disclosed embodiments, expression is represented in the cell containing the sample as it lays hybrid to the oligonucleotide of the disclosed gene sequence as indicated by the registration number provided that the level of the gene sequence is leveled. Measured by detecting the sequence.

  In additional embodiments, the disclosure provides a sequence of what to set at 87 for use of any number of genes in the disclosed methods. Thus, any integer to all of the 1 to 87 gene sequences may be used during the disclosure.

  As used herein, "tumor sample" or "tumor containing sample" or "tumor cell containing sample" or variations thereof, or variations thereof, are suspected of developing cancer in developing into a dangerous state. Reference is made to a cell containing a sample of tissue or fluid that is separated from the individual. The sample may contain tumor cells that may be isolated by known or other suitable methods as deemed desirable by the skilled practitioner. These include, but limit (microdissection), laser use in the immediate disclosure to capture microdissection (LCM) or laser microdissection (LMD). Alternatively, unscrutinized cells within a “region” of tissue may be used. Non-limiting examples of such samples include non-invasive or minimally invasive, including but not limited to isolated (including polished cells) including primary The means to do may be collected by any of the fine methods described in needle aspiration, needle biopsy, device and recognized US Pat. No. 6,328,709 or any other suitable means Technology. Alternatively, including but not limited to surgical biopsy, the sample may be collected by invading methods.

  Transcription sequence detection and measurement may be accomplished by various means known in the art or as deemed appropriate by a skilled practitioner. Essentially any analytical evaluation method may be used as long as the analytical evaluation reflects quantitatively or qualitatively. Then, the level of appearance of the transcription sequence is detected.

The ability to classify a tumor sample is provided by an associated recognition of the level of appearance of the gene sequence (selected and random or specific), and the actual level of expression is determined by the form of the assay. Not in. As disclosed herein, either quantitatively or qualitatively, the analytical evaluation is “transscriptome” (a copied fraction of the gene in the genome) or “proteome” (the translated gene of the represented gene in the genome). As long as the expression of the gene in the fraction) is reflected, the disclosed analytical evaluation may be used for any confirmation of individual gene sequence characteristics. Additional analytical assessments include those based on the detection of related member polypeptide fragments or members of the protein. Non-limiting examples of the latter include the detection of proteolytic fragments found in biological fluids (eg blood or serum).
Identifying features include, but restrict (sequence unique (DNA) unique nucleic acids) or express (RNA), for the said gene or specific epitope or activity A protein encoded by a gene sequence.

  Additional means include increased expression levels of nucleic acid expansion and nucleic acid inactivation (deletion), including detection or expression levels with reduced methylation. The expression of the RNA of each gene sequence used, or the sequence (as such, underlies various statements that the disclosure may be implemented by analyzing one or more aspects of DNA template (s). To the intermediate representing the sequence of the proteinaseous product represented by (as well as the proteolytic fragment of the product). For this reason, detection, reach, stability or degradation (including rate) (such DNA), RNA and proteinaseous molecules may be used during the execution of the disclosure.

  In certain embodiments, all or part of a gene sequence may be expanded and detected by methods such as polymerase chain reaction (PCR) and, for example, variations thereof, but should not be limited. (Quantitative PCR (Q-PCR)) Copy PCR (RT-PCR) and real-time PCR (including means for measuring the initial amount of mRNA copied for each sequence in the sample) (optional Real-time RT-PCR or real-time Q-PCR.). Such a method utilizes one or two primer coats that are complementary to a portion of the gene sequence, where the primer coat is used for primary nucleic acid synthesis. Newly synthesized nucleic acids may optionally be labeled and detected directly by hybridization to the disclosed polynucleotides. Under circumstances that allow their hybridization, newly synthesized nucleic acids may be contacted with the disclosed polynucleotides (including gene sequences). Additional methods of detecting expressed nucleic acid expression include RNAse protection assay evaluation including liquid phase hybridization and in situ hybridization of cells.

  Alternatively, as disclosed in US Pat. No. 7,364,846 B2, which is incorporated by reference as if fully set forth, gene sequence manifestations may be detected within the FFPE sample. Temporarily, the appearance of all or part of a represented gene sequence or a copy may be detected using cross-adjusted detection (e.g., without limitation, because a microarray, bead or piece is based on Non-limiting examples) or quantitative PCR-mediated detection traps (eg, without limitation, really PCR and reverse transcriptase PCR times). The appearance of all or part of the represented polypeptide may be detected by use of immunohistochemical techniques or other antibody-mediated detection (eg, without limitation, especially compared to other polypeptides). Use of labeled antibodies that bind to at least separate the polypeptides))) for non-limiting examples. Additional means of analyzing gene expression are available worldwide, including detection of expression within the scope of the analytical evaluation, for example one of the gene expressions that outline the analysis, eg, on a microarray. As part) gene expression within.

In embodiments where the base analytical evaluation to determine expression uses nucleic acids comprising immobilization of one or more gene sequences on strong support, however, restricts to rigid substrate sequences or in the art As is known, the technology was based on beads or beads. Alternatively, known solution-based expression analysis assessments in the art may also be used. The fixed gene that sequence (s) may do in the form of a polynucleotide that is unique or otherwise specific to gene (s) is the DNA of gene (s). Or it seems that hybrids can be born in RNA. These polynucleotides can be the full length of gene (s) or can be a short sequence of genes (5 'or 3 rising to 1 nucleotide shorter than the known full length sequence in the art by deletion). 'Sequence termination', optionally gene-altered (eg, by inappropriate combinations or inserted non-complementary basepairs) with hips in such cross-matching DNA or RNA. In certain embodiments, the polynucleotide used is, therefore, within the range of a 3 ′ gene, such as approximately 350, approximately 300, approximately 250, approximately 200, approximately 150, approximately 100 or a gene or represented sequence. Terminates for 50 nucleotides from the polyadenylation signal or polyadenylation site. Polynucleotides containing mutations may also be used, as compared to the disclosed gene sequences, so long as the presence of the mutation still allows the generation of a signal where a cross is found.
Thus, the performance of the present disclosure is not affected by minor improper combinations between the disclosed sequences and their presence represented by cells of the subject sample. Non-limiting examples of the presence of such inappropriate combinations have been found in the case of sequence polymorphism among individuals of species (eg, individual human patients within the scope of human sapiens.).

  As known to those skilled in the art, poly A (or complementary polyester fibers T on the complementary shore) that contribute to the uniqueness of the disclosed sequence does not extend, as certain gene sequences include 3 '. The disclosure may be implemented with gene sequences thus missing 3 ', poly A (or polyester fiber T) stretches. The uniqueness of the disclosed sequence is 3 'its untranslated part that refers to the part or all of the found sequence only in the nucleic acid containing the found unique sequence. Certain unique sequences for performing the disclosure are presented in individuals where unique sequences are useful in detecting expression in various individuals rather than specific due to polymorphism For those genes that contribute to the consensus sequence. Alternatively, sequences that are unique to individuals or subpopulations may be used. As described herein, the unique sequence may be the length of the disclosed polynucleotide.

  In additional embodiments of the disclosure, the polynucleotide having the sequence is 3 ′ presented in the untranslated and / or non-coding region of the gene sequence comprises the disclosed sample. It is used to detect the level of expression in cells. Such a polynucleotide may comprise an optionally found sequence part of the coding region of the 3 'gene sequence. A polynucleotide non-coding region containing a combination of coding and sequence from 3 'desirably has sequences arranged adjacently without intervening heterologous sequence (s).

  Alternatively, the disclosure may be implemented with polynucleotides having this sequence 5 'disclosed cells and untranslated and / or non-coding regions of a gene sequence that detects the level of expression in a sample . Such a polynucleotide may comprise a portion of a 5 'coding region that may optionally contain a sequence. A polynucleotide non-coding region containing a combination of coding and 5 'sequence may have a sequence arranged adjacently without intervening heterologous sequence (s). The disclosure may also be performed with this sequence in the coding region of the gene sequence.

  The polynucleotide of certain embodiments comprises a sequence from 3 ′ or 5 ′ approximately 16, at least approximately 18, at least approximately 20, at least approximately 22, at least approximately 24, at least approximately 26, at least approximately 28, at least approximately 30, at least approximately 32, at least approximately 34, at least approximately 36, at least approximately 38, at least approximately 40, at least approximately 42, at least approximately 44 or at least approximately 46 contiguous nucleotides at least untranslated and / or non-coding regions. As used in the “available” period, the previous sentence references from a fixed number to an increase or decrease of one. For other or at least about 100, embodiments using polynucleotides comprising at least a sequence, approximately at least about 50 or at least or at least or at least or at least or or at least 400 consecutive nucleotides 350, 300, 250, 200, 150. As it is used during the "available" period, the sentence references from a fixed number to a 10% increase or decrease.

  They are found in the 3 'or 5' gene coding region end-disclosed polynucleotides from the sequence, they are of course the same length as described above, except that they are limited by the length of the coding region. 3 ', the end of the coding region is half of the 3' coding region which may contain the sequence. Conversely, 5 ', the end of the coding region is half of the 5' coding region which may contain the sequence above. Of course, the above describes the sequence, or coding regions and polynucleotides containing portions thereof may be used as a whole.

  In another embodiment of the disclosure, 5 'and / or 3' from the polynucleotide containing nucleotide deletions, the end of the gene sequence may be used. Deletion is preferably 1-5, 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, 40-45, 45-50, 50-60, 60 -70, 70-80, 80-90, 90-100, 100-125, 125-150, 150-175 or 175-200 nucleotides, so the range of 5 'and / or 3' deletion is naturally the detection of the expression level Polynucleotides can be used for sequencing and termination limited by the required length.

  Terminations of 3 'gene sequences from other polynucleotides of the disclosure include those for primer and optional studies for quantitative PCR. Preferably, the primers and probes are those that amplify less than about 750 and are less than about, less than about 700, less than about 650, less than about 6000, less than about 550, less than about 500, less than about 450, some less than 400, Less than about 350, less than about 300, less than about 250, less than about 200 polyadenylation signals or polyadenylation sites of genes or expressed less than about 50 nucleotides, less than about 100, or less than about 150 sequences. At least or at least around 100, the disclosed amplicon may contain at least 50 or whatever size, or at least or at least about 200, or at least or at least about 300, or at least Alternatively, the size of PCR used by 150 for 250, for 250, with inclusion of a portion completely complementary to the PCR primer for 400 consecutive nucleotides.

  Other polynucleotides used in the practice of the disclosure include those whose sequences for detecting expression have sufficient isomorphism in the gene used by the hybridization technique. Such a polynucleotide desirably has around 95% or ‖ available or 96% available 97% or ‖ approximately 98% or ‖ or approximately or 99% identity by the gene sequence used. The identity is determined using the BLAST algorithm as described above. Other polynucleotides used in the practice of the disclosure are also about 50% formamide and from about 0.01 M to about 0.15 M salt for hybridization and at about 55 ° C. to about 65 ° C. wash conditions. May be explained based on their ability to produce hybrids in the disclosed polynucleotides under stringent conditions of about 0.01 M to about 30% v / v to about 0.15 M salt. Or more or equal situation to it.

  In a further embodiment of the disclosure, at least a publicly-known population may be crossed to one or both is quantitatively probed with nucleic acid molecules from the cellular RNA or the disclosed sample. In time, a single population of nucleic acid molecules is provided that contains one or both banks of the human gene sequence. The population may be just the antisense shore of the human gene sequence, such as that sensory molecule stranded or detailed, cells may be hybridized to some said populations. The population is in excess of the amount of expressed (or expanded) nucleic acid molecules that contain complementary gene sequences, preferably in excess of the stated one or both banks of the human gene sequence being compared. Including quantity.

  The disclosure further provides a method of classifying human tumor samples by detecting expression levels of 5 or higher. And a sample from which a transcription sequence or cell in a nucleic acid is optionally obtained from a human subject as it includes tumor cells of a tumor type found in humans, excluding one or more other human tumor types And classify the sample. In certain embodiments, the method may be used to classify a sample or have cells, one of 54 tumor types listed above, excluding one or more others.

  The disclosure also provides a method for classifying tumor samples as being one of a subset of possible tumor types described herein by detecting expression leveled more than 5 or optionally more than 50 . And including one or more other human tumor types, including tumor samples in which the transcription sequence in the nucleic acid is derived from a human subject as being one of many tumor types found in humans and Classify the sample. In certain embodiments of the disclosure, the number of other tumor types is from 1 to about 3, more desirably from 1 to about 1, from 1 to about 5, or from 1 to about 9 or about 10. In other embodiments, the tumor type is all of the same tissue or organ origin, such as those listed above.

  In additional embodiments, disclosure that is isolated, purified, different or distant may be implemented by analyzing gene expression from single cells or dissected homogenous cell populations. And it will contaminate the cells of the book and sample during a simple biopsy. One advantage provided by these embodiments is that it contaminates, so non-tumor cells (eg, permeating lymphocytes or other immune system cells) may be removed, provided here As it is done, rest to change the subsequent analysis of the identified genes or gene expression levels. Such contamination is a book where biopsy is used to generate gene expression profiles.

  In a further embodiment of the disclosure utilizing Q-PCR or reverse transcriptase Q-PCR, in an analytical evaluation platform, the expression level of the disclosed gene sequence may be compared to the expression level of a reference gene in the same sample Alternatively, the ratio of expression levels may be used. This provides a means to “normalize” expression data for comparison of cells containing data and samples to be analyzed on multiple known tumor types. Furthermore, all or part of the Q-PCR may be performed using a composite format.

  In additional aspects, all or some of the methods provided by the present disclosure may also be automated. This includes the disclosed embodiments in software. A non-limiting example is that instructions that are executable on one or more computer-readable storage devices direct there to level the classification of tumor samples based on gene expression as the instructions are described herein. Processor included. Additional processor-executable instructions on one or more computer-readable storage devices may be manipulated via a computer output device of the possible cause-and-occurrence and / or classification method process or result It is.

  The disclosure is in a data set comprising software and hardware embodiments in which gene expression data for a set of gene sequences in a plurality of known tumor types is implemented. In certain embodiments, the gene expression data set is used to perform the disclosed method. The disclosure also provides computer-related means and systems for performing the methods disclosed herein. In certain embodiments, an apparatus for classifying cells containing a sample is provided. Such a device may include a query input configured to receive a query storage configured to store a gene expression data set as described herein. And received from the query input; and a module for accessing and using data from storage in the classification algorithm is described herein. The apparatus is described herein with a module for accessing and using data from the string storage, optionally in the output algorithm, which may further include string storage for the results of the classification algorithm.

  The steps of the algorithm described in connection with the method, process or embodiment disclosed herein in a software module executed on a processor or in a combination of the two may be implemented directly in hardware. Good. Various steps or actions in a method or process may be performed in the order shown, or may be performed in another order. Further, one or more method steps may be omitted, or one or more method steps may be added to the methods and processes.

  Additional steps, blocks or actions may be added at the beginning, termination or existing elements of the method and process intervention. A further aspect of the disclosure provides within it a relationship to clinical activity for use of the present disclosure. Performed as part of providing a therapy to a patient, including providing a diagnostic service in support of providing a therapy, as described herein in certain embodiments, measuring or measuring gene expression . Thus, the disclosure includes methods during treatment of a patient. And as described herein, a method is obtained from a patient that includes determining in a cell containing a sample or measuring the expression level of a gene sequence. The method may further comprise classifying the sample. And as described herein, it is based on measurements / measurements as it includes tumor cells of the tumor type or tissue origin in the method.

  As described herein, measurements and / or classifications may exist for any aspect to any relationship or embodiment of the disclosure. The measurement or measurement of the expression level may be preceded by various related actions. In some embodiments, the measurement is after the measurement or diagnosis of the human subject, as in the case of said measurement needs. Measurements may be preceded by measurements, such as a doctor, nurse or other health care provider's necessary or professional measurements, or their orders in approving the performance of measurements or health insurance or maintenance In compensation for grounds or performance to request reimbursement for those working under the personnel of the organization.

  The measurement may also be preceded by preliminary actions necessary for the documentary being measured. Non-limiting examples include actually obtaining a cell containing a sample from a human subject; or receiving a cell containing the sample; or sorting a cell containing the sample; or Separating the cells from the cells containing the sample; or obtaining the RNA from the cells of the cell containing the sample; or reversing the RNA from the cells of the cell containing the sample. As described herein for the implementation of the disclosure, the sample may be any. The disclosure further provides kits for measurement, or as described herein, measurement of gene expression is horizontal in the cell containing the sample.

  For the practice of the present disclosure, as described herein, the kit typically includes one or more reagents to detect gene expression. Non-limiting examples include polynucleotide surveys or priming for detection of expression levels. And one or more enzymes are used in the disclosure and one or more tube methods for the implementation of the disclosure. In certain embodiments, as described herein, the kit includes a rigid medium for the detection of gene expression that can be collected into a sequence or sequence. In other embodiments, the kit may include one or more antibodies that are immunoreactive with the epitope on a polypeptide that exhibits the appearance of a gene sequence. In certain embodiments, the antibody is an antibody fragment. The disclosed kits may also include teaching materials of the disclosed or illustrated kit or primer used or as provided herein, explore the disclosure in depth within the disclosed method. The kit may also include additional parts to facilitate the specific application for which the kit is designed. Thus, for example, the kit may further include a label (eg, an enzyme substrate for labeling the enzyme, a filter set that detects fluorescent labels, a suitable second label such as sheep anti-mouse-HRP, or the like Means for detecting other species). The kit may further include recognized buffers and other reagents for the disclosed methods.

  The disclosure is now generally provided and is more easily understood through reference to the following examples which are provided by way of illustration and are not restrictive of disclosure, unless the same is specified.

Example 1: Expression level of PANX3 (pannexin 3) The expression level of PANX3 (pannexin 3) was determined in multiple samples of 39 known tumor types from human subjects and the results are shown in FIG. It is. The range of expression levels in each tumor type overlapped considerably in 38 of 39 tumor types.

Example 2: BATF expression levels Within a number of multiple samples of the 39 known tumor types of Example 1, the expression level of BATF (basic leucine zipper copy factor (ATF-like)) was determined and the results Indicated in 2. The range of expression levels in each tumor type overlapped among all 39 tumor type members.

Example 3: Additional Transcript Sequence Expression Levels As disclosed herein, expression was determined in the same manner as Examples 1 and 2 across the same 39 tumor types that leveled out the additional 85 transcript sequences. The range of expression levels for each transcribed sequence was seen to overlap between multiple tumor types.

Example 4: Expression levels of transcriptional sequences in additional tumor types Expression is in the same manner as in Examples 1 and 2 across the 54 disclosed tumor types disclosed here for each of the 87 transcriptional sequences leveled Determined. The range of expression levels for each transcribed sequence was seen to overlap between multiple tumor types.

  All references cited herein, including patents, patent applications and publications, are hereby incorporated by reference in their entirety, whether or not specifically incorporated previously.

  Having now fully described the subject matter, the same can be practiced within a wide range of equivalent parameters, concentrations and circumstances without departing from the spirit and disclosure and without undue experimentation, As understood by those skilled in the art.

While this disclosure is described in connection with that specific embodiment, it is understood that it can be further modified. This application is meant to cover any variation, or generally, from the present disclosure as disclosed and known within the scope of the disclosure, followed by adaptation of the disclosure, followed by disclosure. The features started earlier as principles, including withdrawal or customary practices, were implemented and could be applied to the point.

Claims (17)

A method of amplifying a transcription sequence for classifying a sample containing cells to contain tumor cells of a tissue type, said method comprising 50 or more cells from a sample in a sample containing cells obtained from a human subject. A generated cDNA copy of the transcribed sequence of
Amplification is the process of generating an amplified molecule of a cDNA copy;
The same 50 or more transcripts of a plurality of known tumor types including one or more known tumor tissues, salivary gland tumors, squamous cells selected from the levels of Formula 50 or one or more adrenocortical tumors to be compared Transcriptional sequence cancer, neuroendocrine pancreatic cancer, Merkel cell carcinoma, lung carcinoid, primitive neuroectodermal tumor, sex cord stromal tumor, thymic cancer / adenoma, bladder adenocarcinoma, and Samples are classified as containing squamous cell carcinoma of the bladder and tumor cells of the plurality of tumor types or tissues.   2. The method according to claim 1, wherein the plurality of adrenocortical tumors, salivary gland tumors, squamous cell carcinomas, neuroendocrine pancreatic cancers, Merkel cell carcinomas, lung carcinoids, undifferentiated neuroectodermal tumors, sex cord stromal tumors , Including thymoma / thymoma, bladder and bladder squamous cell carcinoma, adenocarcinoma, and classification of adrenal cortex tumor cells, salivary gland, squamous cell carcinoma, neuroendocrine pancreatic cancer, Merkel cell carcinoma, lung carcinoid, The samples, thymic / adenomas, adenocarcinoma of the bladder, and squamous cell carcinoma of the bladder, so as to include or not include tumors of undifferentiated neuroectodermal tumors, sex cord stromal tumors.   3. The method of claim 2, wherein the plurality of adrenocortical carcinomas, adrenal pheochromocytoma, brain tumor, breast adenocarcinoma, cervical adenocarcinoma, cervical squamous cell carcinoma, bile duct cancer, intrauterine Membrane adenocarcinoma, squamous cell carcinoma of the esophagus, gastrointestinal stromal tumor, gallbladder including adenocarcinoma, gastroesophageal adenocarcinoma, seminoma germ cell tumor, non-seminoma germ cell tumor, salivary gland, squamous cell carcinoma, colorectal adenocarcinoma, small intestine gland Cancer, clear cell renal cell carcinoma, chromophore renal cell carcinoma, papillary renal cell carcinoma tumor, hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma, melanoma, meningioma, mesothelioma, small / Large cell neuroendocrine lung cancer, neuroendocrine pancreatic cancer, Merkel cell carcinoma, gastrointestinal carcinoid, lung carcinoid, clear cell adenocarcinoma, endometrioid adenocarcinoma, mucinous adenocarcinoma, serous gland carcinoma, pancreatic adenocarcinoma, prostate cancer, malignant fiber Histiocytoma, primitive neuroectodermal tumor, leiomyosarcoma, liposarcoma, osteosarcoma, synovial sarcoma, intersex Tumor, basal cell carcinoma, squamous cell carcinoma of the skin, thymoma / thymoma, follicular / papillary carcinoma, medullary cancer, transitional cell carcinoma, bladder adenocarcinoma, and squamous cell carcinoma of the bladder, and classification is adrenal cortex tumor With or without tumor cells, adrenal pheochromocytoma, brain, breast adenocarcinoma, cervical adenocarcinoma, cervical squamous cell carcinoma, bile duct cancer, endometrial adenocarcinoma, esophageal squamous cell carcinoma Gastrointestinal stromal tumor, gallbladder, gastroesophageal adenocarcinoma, seminoma germ cell tumor, non-seminoma germ cell tumor, salivary gland, squamous cell carcinoma, colorectal adenocarcinoma, small intestine adenocarcinoma, clear cell renal cell carcinoma , Pigmented renal cell carcinoma tumor adenocarcinoma, papillary renal cell carcinoma, hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma, melanoma, meningioma, mesothelioma, small / large cell nerve Endocrine lung cancer, neuroendocrine pancreatic cancer, Merkel cell carcinoma, gastrointestinal carcinoid, lung carcinoid, clear cell adenocarcinoma, endometrioid Cancer, mucinous adenocarcinoma, serous gland, pancreatic adenocarcinoma, prostate cancer, malignant fibrous histiocytoma, primitive neuroectodermal tumor, leiomyosarcoma, liposarcoma, osteosarcoma, synovial sarcoma, sex stroma tumor, Basal cell carcinoma, cutaneous squamous cell carcinoma, thymic carcinoma / thymoma, follicular / papillary carcinoma, medullary carcinoma, transitional cell carcinoma, bladder adenocarcinoma, and squamous cell carcinoma of the bladder.   4. The method according to any of claims 1, 2 or 3, wherein the 50 or more transcription sequences comprise the disclosed 87 gene sequences.   5. A method according to any of claims 1, 2, 3 or 4 wherein all the expression levels are determined using a microarray.   The method according to claim 1, 2, 3, 4 or 5, wherein the classification has an accuracy of 60% or more.   The method according to claim 1, 2, 3, 4, 5 or 6, wherein the amplification comprises amplification of all or part of the transcribed sequence or reverses the transcribed and labeled RNA corresponding to the transcribed sequence. .   8. The method of claim 7, wherein the amplification comprises linearly amplified RNA or quantitative PCR.   9. The method of claim 8, wherein the amplification is a quantitative PCR amplification of at least 50 nucleotides of the transcribed sequence.   2. The method of claim 1, wherein a cDNA copy of 50-100 transcription sequences is generated.   11. The method of claim 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10, wherein the sample is a formalin fixed paraffin embedded (FFPE) sample.   The method according to claim 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or 11, wherein the expression levels of most of 50 or more transcription sequences in the plurality overlap.   The method according to claim 1, wherein 30 or more expression levels of 50 or more transcription sequences overlap in the plurality.   The method according to claim 1, wherein 35 or more expression levels of 50 or more transcription sequences overlap in the plurality.   The method according to claim 1, wherein 40 or more expression levels of 50 or more transcription sequences overlap in the plurality.   The method according to claim 10, wherein 55 or more expression levels of 50 or more transcription sequences overlap in the plurality. The method according to claim 1, wherein 60 or more expression levels of 60 or more transcription sequences overlap in the plurality.

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