Patent Application
20150191793
1. Field of the Invention
The present invention relates to novel molecular markers of prostate cancer, and a method and a kit for detection of prostate cancer comprising the molecular markers.
2. Description of the Related Art
Prostate cancer is a leading cause of cancer-related death in men. For early-stage, localized prostate cancer, radical prostatectomy offers an opportunity of eradicating the disease. However, approximately 15-30% of patients with initially localized diseases develop recurrence within 5-10 years, resulting in poor therapeutic outcomes (Bill-Axelson et al., 2005; Pound et al., 1999). Further improvements in the prognosis of patients with prostate cancer may rely on a deeper understanding of the patho-molecular mechanisms underlying disease recurrence as well as rationalized treatment plans based on a better prediction of the clinical behaviors of human prostate cancer.
Like most glandular cancers, the malignant transformation of prostatic epithelium involves a gradual and variable loss of the normal glandular architectures. As such human prostate cancer frequently displays considerable intra-tumoral heterogeneity in glandular differentiation, a factor widely used for the pathological classification of prostate cancer such as the Gleason grading system (Gleason, 1992). Large scale clinical studies have established the degree of glandular differentiation as a determinant of the clinical behaviors of prostate cancer. Specifically, poorly to differentiated, high-Gleason-grade tumors were associated with higher probabilities of tumor recurrence and poor prognosis (Albertsen et al., 1995; Stamey et al., 1999). This morphology-based classification system, however, is only modestly prognostic and does not allow for risk stratification of prostate cancer with similar histopathological characteristics. Assessments of tissue architectures did not provide functional or mechanistic insights into observed tumor variations. There is thus a critical need for pathway-informed and molecularly-based diagnostic assays with increased accuracy in the prediction of clinical outcome in prostate cancer.
Recently, high throughput genomic profiling techniques have facilitated the molecular characterization of human malignant tumors, including prostate cancer (Glinsky et al., 2004; Henshall et al., 2003; Singh et al., 2002; Stratford et al., 2010; van 't Veer et al., 2002; van de Vijver et al., 2002). The profound prognostic utilities of these genomic markers point to the intrinsic molecular characteristic of tumors as a crucial determinant to their clinical behaviors (Ramaswamy et al., 2003). For instance, by comparing gene expression profiles of prostate cancer specimen and normal adjacent prostate, Dhanasekaran et al. identified clusters of coordinately expressed genes of prostate cancer (Dhanasekaran et al., 2001). Two of these genes, including hepsin (HPN) and pim-1 (PIM1), were shown to correlate with measures of clinical outcome. Similarly, by comparing the gene expression patterns of metastatic prostate cancer and localized prostate cancer, Varambally et al. identified 55 upregulated genes and 480 downregulated genes (Varambally et al., 2002). Focusing on the top-ranked genes they experimentally verified enhancer of Zeste homolog 2 (EZH2) as a metastasis-promoting gene and a prognostic marker in prostate cancer. Studying gene expression patterns of tumors from 21 patients with prostate cancer who received radical prostatectomy, Singh et al. established a 5-gene model that predicted risk of post-operative disease recurrence with an accuracy reaching 90% (Singh et al., 2002). This model was established based on few tumor samples and its performance had not been verified in independent patient cohorts. Based upon the same set of 21 prostate cancer tumor samples, Glinsky et al. identified three sets of genes by comparing gene-expression profiles in tumors from patients with recurrent versus nonrecurrent prostate cancer (Glinsky et al., 2004). These gene signatures were able to discriminate human prostate cancers exhibiting recurrent or nonrecurrent clinical behaviors with 86-95% accuracy. Using a small number of tumor samples including four from patients with recurring prostate cancer and five from those with non-recurring tumors, Gary et al. identified a set of 33 genes that differentially expressed between the two groups of prostate cancer (US Patent Application US 2010/0196902 A1). This gene signature of prostate cancer also suffered from the small sample size and the lack of independent verification.
Aside from the development of molecular markers, genomic tools can also be used to molecularly define tumor subtypes or distinguish among primary and metastatic prostate cancers. For example, transcript profiling of human prostate cancer tissues has supported the existence of three distinct tumor subclasses that were associated with tumor grades and stages (Lapointe et al., 2004). LaTulippe et al. identified more than 3000 genes that were differentially expressed between primary and metastatic prostate cancers (LaTulippe et al., 2002). Gene expression patterns of tumor differentiation as reflected by the Gleason scores have also been described. For instance, gene expression profiling of 29 microdissected prostate tumors corresponding led to the identification of a 86-gene model capable of distinguishing low-grade from high-grade prostate cancer (True et al., 2006). It should be noted that the above mentioned molecular patterns were identified from clinical prostate tumor specimen and might only reflect established tumor characteristics without providing mechanisms underlying the pathogenesis of these tumor variations. In this regard, knowledge-based approaches offer an opportunity to identify more rational markers or classification systems that benefit clinical decision-making and therapeutic advancement. Such approaches have been used to establish the prognostic roles of gene profiles associated with tumor progenitor cells, stromal activation or tissue differentiation in several types of solid tumors (Chang et al., 2004; Fournier et al., 2006; Liu et al., 2007; Sotiriou et al., 2006).
Currently prevailing models of tumorigenesis suggest that tissue differentiation and tumor progression share similar gene regulations and molecular pathways. Molecular changes associated with the differentiation process of glandular epithelium may be difficult to study in vivo. However, a physiological relevant three-dimensional organotypic culture model has been used to recapitulate the structural and functional differentiation processes of mammary acini, the basic structural unit of normal mammary epithelium (Debnath and Brugge, 2005; Lee et al., 2007). Similar models have successfully recapitulated the morphogenetic and differentiation processes of prostate, pancreatic and pulmonary epithelium (Gutierrez-Barrera et al., 2007; Mondrinos et al., 2006; Webber et al., 1997). Comparative gene expression analysis using this developmental model has led to the identification of gene expression profiles and marker genes that showed significant association with breast cancer prognosis (Fournier et al., 2006; Kenny et al., 2007). Whether or not the same paradigm can be applied to other types of glandular cancers, such as prostate cancer, remains unclear.
Therefore, it still needs molecular markers for predicting the clinical outcomes of prostate cancer, such as recurrence, with improved accuracy and clinical applicability.
The present application describes a method for predicting clinical prognosis for a human subject diagnosed with prostate cancer, comprising: detecting an expression level of a marker gene selected from a group consisting of ABCG1, PDCD4, KLF6, ST6, BTD, BANF1, IRS1, ZNF185, ANXA11, DUSP2, KLF4 and DSC2, in a biological sample containing prostate cancer cells obtained from the human subject; and predicting a likelihood of the clinical prognosis by comparing the expression level of the marker gene with a reference level. The biological sample can be obtained by aspiration, biopsy, or surgical resection.
The present application also provides a combination of molecular markers for predicting clinical prognosis of prostate cancer, comprising at least two of marker genes ABCG1, PDCD4, KLF6, ST6, BTD, BANF1, IRS1, ZNF185, ANXA11, DUSP2, KLF4 and DSC2.
The present application further provides a kit for predicting clinical prognosis of prostate cancer, comprising a means for detecting an expression level of a marker gene selected from a group consisting of ABCG1, PDCD4, KLF6, ST6, BTD, BANF1, IRS1, ZNF185, ANXA11, DUSP2, KLF4 and DSC2.
The file of this patent contains at least one drawing executed in color. Copies of this patent with color drawing(s) will be provided by the Patent and Trademark Office upon request and payment of the necessary fee.
Table. P values were calculated using the log-rank test.
Table. P values were calculated using the log-rank test.
Table. The threshold value for each gene marker was determined by the maximal Youden's index. P values were calculated using the log-rank test.
As used herein, “prostate cancer” refers to malignant mammalian cancers, especially adenocarcinomas, derived from prostate epithelial cells. Prostate cancers embraced in the current application include both metastatic and non-metastatic cancers.
The term “differentiation” refers to generalized or specialized changes in structures or functions of an organ or tissue during development. The concept of differentiation is well known in the art and requires no further description herein. For example, differentiation of prostate refers to, among others, the process of glandular structure formation and/or the acquisition of hormonal or secretory functions of normal prostatic glands.
As used herein, the term “clinical prognosis” refers to the outcome of subjects with prostate cancer comprising the likelihood of tumor recurrence, survival, disease progression, and response to treatments. The recurrence of prostate cancer after treatment (e.g., prostatectomy) is indicative of a more aggressive cancer, a shorter survival of the host (e.g., prostate cancer patients), an increased likelihood of an increase in the size, volume or number of tumors, and/or an increased likelihood of failure of treatments.
As used herein, the term “predicting clinical prognosis” refers to providing a prediction of the probable course or outcome of prostate cancer, including prediction of metastasis, multidrug resistance, disease free survival, overall survival, recurrence, etc. The methods can also be used to devise a suitable therapy for cancer treatment, e.g., by indicating whether or not the cancer is still at an early stage or if the cancer had advanced to a stage where aggressive therapy would be ineffective.
As used herein, the term “recurrence” refers to the return of a prostate cancer after an initial or subsequent treatment(s). Representative treatments include any form of surgery (e.g., radical prostatectomy), any form of radiation treatment, any form of chemotherapy or biological therapy, any form of hormone treatment. In some examples, recurrence of the prostate cancer is marked by rising prostate-specific antigen (PSA) to levels (e.g., PSA of at least 0.4 ng/ml or two consecutive PSA values of 0.2 mg/ml and rising) (Stephenson et al., 2006) and/or by identification of prostate cancer cells in any biological sample from a subject with prostate cancer.
As used herein, the term “disease progression” refers to a situation wherein one or more indices of prostate cancer (e.g., serum PSA levels, measurable tumor size or volume, or new lesions) show that the disease is advancing despite treatment(s).
The terms “molecular marker”, “gene marker”, “cancer-associated antigen”, “tumor-specific marker”, “tumor marker”, “maker”, or “biomarker” interchangeably refer to a molecule or a gene (typically protein or nucleic acid such as RNA) that is differentially expressed in the cell, expressed on the surface of a cancer cell or secreted by a cancer cell in comparison to a non-cancer cell or another cancer cells, and which is useful for the diagnosis of cancer, for providing a prognosis, and for preferential targeting of a pharmacological agent to the cancer cell. Oftentimes, a cancer-associated antigen is a molecule that is overexpressed or underexpressed in a cancer cell in comparison to a non-cancer cell or another cancer cells, for instance, 1-fold over expression, 2-fold overexpression, 3-fold overexpression or more in comparison to a non-cancer cell or, for instance, 20%, 30%, 40%, 50% or more underexpressed in comparison to a non-cancer cell. Oftentimes, a cancer-associated antigen is a molecule that is inappropriately synthesized in the cancer cell, for instance, a molecule that contains deletions, additions or mutations in comparison to the molecule expressed in a non-cancer cell. Oftentimes, a cancer-associated antigen will be expressed exclusively on the cell surface of a cancer cell and not synthesized or expressed on the surface of a normal cell. Exemplified cell surface tumor markers include prostate-specific antigen (PSA) for prostate cancer, the proteins c-erbB-2 and human epidermal growth factor receptor (HER) for breast cancer, and carbohydrate mucins in numerous cancers, including breast, ovarian and colorectal. Other times, a cancer-associated antigen will be expressed primarily not on the surface of the cancer cell.
The term “differentially expressed” or “differentially regulated” refers generally to a protein or nucleic acid that is overexpressed (upregulated) or underexpressed (downregulated) in one sample compared to at least one other sample in the context of the present invention.
“ABCG1”, “PDCD4”, “KLF6” and other molecular markers recited herein, including those found in
Table, refer to nucleic acids, e.g., gene, pre-mRNA, mRNA, and polypeptides, polymorphic variants, alleles, mutants, and interspecies homologs that: (1) have an amino acid sequence that has greater than about 60% amino acid sequence identity, 65%, 70%, 75%, 80%, 85%, 90%, preferably 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% or greater amino acid sequence identity, preferably over a region of over a region of at least about 25, 50, 100, 200, 500, 1000, or more amino acids, to a polypeptide encoded by a referenced nucleic acid or an amino acid sequence described herein; (2) specifically bind to antibodies, e.g., polyclonal antibodies, raised against an immunogen comprising a referenced amino acid sequence, immunogenic fragments thereof, and conservatively modified variants thereof; (3) specifically hybridize under stringent hybridization conditions to a nucleic acid encoding a referenced amino acid sequence, and conservatively modified variants thereof; (4) have a nucleic acid sequence that has greater than about 60% nucleotide sequence identity, 65%, 70%, 75%, 80%, 85%, 90%, preferably 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% or higher nucleotide sequence identity, preferably over a region of at least about 10, 15, 20, to 25, 50, 100, 200, 500, 1000, or more nucleotides, to a reference nucleic acid sequence. A polynucleotide or polypeptide sequence is typically from a mammal including, but not limited to, primate, e.g., human; rodent, e.g., rat, mouse, hamster; cow, pig, horse, sheep, or any mammal. The nucleic acids and proteins of the invention include both naturally occurring or recombinant molecules. Truncated and alternatively spliced forms of these antigens are included in the definition.
It will be understood by the skilled artisan that markers may be used singly or in combination with other markers for any of the uses, e.g., diagnosis or prognosis of multidrug resistant cancers, disclosed herein.
“Biological sample” includes sections of tissues such as biopsy and autopsy samples, and frozen sections taken for histologic purposes. Such samples include prostate cancer tissues, blood and blood fractions or products (e.g., serum, plasma, platelets, red blood cells, and the like), sputum, tissue, cultured cells, e.g., primary cultures, explants, and transformed cells, stool, urine, etc.
A “biopsy” refers to the process of removing a tissue sample for diagnostic or prognostic evaluation, and to the tissue specimen itself. Any biopsy technique known in the art can be applied to the diagnostic and prognostic methods of the present invention. The biopsy technique applied will depend on the tissue type to be evaluated (e.g., breast, etc.), the size and type of the tumor, among other factors. Representative biopsy techniques include, but are not limited to, excisional biopsy, incisional biopsy, needle biopsy, surgical biopsy, and bone marrow biopsy. An “excisional biopsy” refers to the removal of an entire tumor mass with a small margin of normal tissue surrounding it. An “incisional biopsy” refers to the removal of a wedge of tissue that includes a cross-sectional diameter of the tumor. A diagnosis or prognosis made by endoscopy or fluoroscopy can require a “core-needle biopsy”, or a “fine-needle aspiration biopsy” which generally obtains a suspension of cells from within a target tissue.
“Nucleic acid” refers to deoxyribonucleotides or ribonucleotides and polymers thereof in either single- or double-stranded form, and complements thereof. The term encompasses nucleic acids containing known nucleotide analogs or modified backbone residues or linkages, which are synthetic, naturally occurring, and non-naturally occurring, which have similar binding properties as the reference nucleic acid, and which are metabolized in a manner similar to the reference nucleotides. Examples of such analogs include, without limitation, phosphorothioates, phosphoramidates, methyl phosphonates, chiral-methyl phosphonates, 2-O-methyl ribonucleotides, peptide-nucleic acids (PNAs).
The terms “polypeptide,” “peptide” and “protein” are used interchangeably herein to refer to a polymer of amino acid residues. The terms apply to amino acid polymers in which one or more amino acid residue is an artificial chemical mimetic of a corresponding naturally occurring amino acid, as well as to naturally occurring amino acid polymers and non-naturally occurring amino acid polymer.
The term “amino acid” refers to naturally occurring and synthetic amino acids, as well as amino acid analogs and amino acid mimetics that function in a manner similar to the naturally occurring amino acids. Naturally occurring amino acids are those encoded by the genetic code, as well as those amino acids that are later modified, e.g., hydroxyproline, γ-carboxyglutamate, and O-phosphoserine.
“Antibody” refers to a polypeptide comprising a framework region from an immunoglobulin gene or fragments thereof that specifically binds and recognizes an antigen. The recognized immunoglobulin genes include the kappa, lambda, alpha, gamma, delta, epsilon, and mu constant region genes, as well as the myriad immunoglobulin variable region genes. Light chains are classified as either kappa or lambda. Heavy chains are classified as gamma, mu, alpha, delta, or epsilon, which in turn define the immunoglobulin classes, IgG, IgM, IgA, IgD and IgE, respectively. Typically, the antigen-binding region of an antibody will be most critical in specificity and affinity of binding.
Exemplary Molecular Markers:
ATP-Binding Cassette, Sub-Family G, Member 1 (ABCG1)
The human ATP-binding cassette, sub-family G, member 1 (ABCG1) gene (NCBI Entrez Gene 9619) is located on chromosome 21 at gene map locus 21q22.3 and encodes a multi-pass membrane protein predominantly localized in the endoplasmic reticulum (ER) and Golgi membranes. Six alternative splice variants have been identified. Exemplary ABCG1 sequences are publically available, for example from GenBank (e.g., accession numbers NM—004915.3, NM—016818.2, NM—207174.1, NM—997510, NM—207628.1, and NM—207629.1 (mRNAs) and NP—004906.3, NP—058198.2, NP—997057.1, NP—997510.1, NP—997511.1, and NP—997512.1 (proteins)), or UniProtKB (e.g., P45844).
Programmed Cell Death 4 (PDCD4)
The human Programmed cell death 4 (PDCD4) gene (NCBI Entrez Gene 27250) is located on chromosome 10 at gene map locus 10q24 and encodes a nuclear and cytoplasmic shuttling protein. Three alternative splice variants have been identified. Exemplary PDCD4 sequences publically available, for example from GenBank (e.g., accession numbers NM—001199492.1, NM—014456.4, and NM—145341.3 (mRNAs), and NP—001186421.1, NP—055271.2, and NP—663314.1 (proteins)), or UniProtKB (e.g., Q53EL6).
Kruppel-Like Factor 6 (KLF6)
The human Kruppel-like factor 6 (KLF6) gene (NCBI Entrez Gene 1316) is located on chromosome 10 at gene map locus 10q15 and encodes a nuclear protein. Three alternative splice variants have been identified. Exemplary KLF6 sequences publically available, for example from GenBank (e.g., accession numbers NM—001160124.1, NM—001160125.1, and NM—001300.5 (mRNAs), and NP—001153596.1, NP—001153597.1, and NP—001291.3 (proteins)), or UniProtKB (e.g., Q99612).
In the present application, the molecular markers comprising the marker genes ABCG1, PDCD4, KLF6, ST6, BTD, BANF1, IRS1, ZNF185, ANXA11, DUSP2, KLF4, DSC2 or any combination thereof is provided to predict clinical prognosis of prostate cancer. A method and a kit based on the above molecular markers are also provided.
Being the molecular marker, the marker genes ABCG1, PDCD4, KLF6, ST6, BTD, BANF1, IRS1, ZNF185, ANXA11, DUSP2, KLF4 and DSC can be used alone or in combination. The molecular marker includes the gene, the RNA transcript, and the expression product (e.g. protein), which can be wild-type, truncated or alternatively spliced forms.
In one embodiment, a combination of at least two of the above marker genes are preferred, such as 3, 4, 5, 6, 7, 8, 9, 10, 11, or all 12 of the marker genes. In a preferred embodiment, the molecular marker is a 12-gene model, using all of the marker genes for prediction. In another preferred embodiment, the molecular marker is a 3-gene model or a 2-gene model, wherein the marker gene is selected from a group consisting of ABCG1, PDCD4 and KLF6. More particularly, the molecular marker is a combination of ABCG1, PDCD4 and KLF6, or a combination of ABCG1 and PDCD4.
The expression level of the marker gene can be determined based on a RNA transcript of the marker gene, or an expression product thereof, or their combination. In one embodiment, the means for detecting the expression level of the marker gene comprises nucleic acid probe, aptamer, antibody, or any combination thereof, which is able to specifically recognize the RNA transcript or the expression product (e.g. protein) of the marker gene. More particularly, the expression level of RNA transcript of a marker gene can be detected by polymerase chain reaction (PCR), northern blotting assay, RNase protection assay, oligonucleotide microarray assay, RNA in situ hybridization and the like, and the expression level of an expression product of a marker gene, such as protein or polypeptide, can be detected by immunoblotting assay, immunohistochemistry, two-dimensional protein electrophoresis, mass spectroscopy analysis assay, histochemistry stain and the like. The above detection means can be used alone or in combination.
The biological sample is defined as above, which can be obtained by aspiration, biopsy, or surgical resection. The biological sample can be fresh, frozen, or formalin fixed paraffin embedded (FFPE) prostate tumor specimens.
In one embodiment, nucleic acid binding molecules such as probes, oligonucleotides, oligonucleotide arrays, and primers can be used in assays to detect differential RNA expression of marker genes in patient samples, e.g., RT-PCR, qPCR and nucleic acid microarrays.
In another embodiment, the detection of protein expression level comprises the use of antibodies specific to the gene markers and immunohistochemistry staining on fixed (e.g., formalin-fixed) and/or wax-embedded (e.g., paraffin-embedded) prostate tumor tissues. The immunohistochemistry methods may be performed manually or in an automated fashion.
In another embodiment, the antibodies or nucleic acid probes can be applied to patient samples immobilized on microscope slides. The resulting antibody staining or in situ hybridization pattern can be visualized using any one of a variety of light or fluorescent microscopic methods known in the art.
In another embodiment, analysis of the protein or nucleic acid can be achieved by such as high pressure liquid chromatography (HPLC), alone or in combination with mass spectrometry (e.g., MALDI/MS, MALDI-TOF/MS, tandem MS, etc.).
In one embodiment, the clinical prognosis includes the likelihood of disease progression, clinical prognosis, recurrence, death and the like. The disease progression comprises such as classification of prostate cancer, determination of differentiation degree of prostate cancer cells and the like.
In another embodiment, the clinical prognosis can be a time interval to between the date of disease diagnosis or surgery and the date of disease recurrence or metastasis; a time interval between the date of disease diagnosis or surgery and the date of death of the subject; at least one of changes in number, size and volume of measurable tumor lesion of prostate cancer; or any combination thereof. Said change of the tumor lesion can be determined by visual, radiological and/or pathological examination of said prostate cancer before and at various time points during and after diagnosis or surgery.
In the present application, the reference level is applied as the baseline of the prediction, which can be determined based on the normalized expression level of the marker gene in a plurity of prostate cancer patients. Typically, the reference level can be a the threshold reference value, which is representative of a polypeptide or polynucleotide of the marker gene in a large number of persons or tissues with prostate cancer and whose clinical prognosis data are available, as measured using a tissue sample or biopsy or other biological sample such a cell, serum or blood. Said threshold reference values are determined by defining levels wherein said subjects whose tumors have expression levels of said markers above said threshold reference level(s) are predicted as having a higher or lower degree of differentiation or risk of poor clinical prognosis or disease progression than those with expression levels below said threshold reference level(s). Variation of levels of a polypeptide or polynucleotide of the invention from the reference range (either up or down) indicates that the patient has a higher or lower degree of differentiation or risk of poor clinical prognosis or disease progression than those with expression levels below said threshold reference level(s).
To compare the expression level of the marker gene and the reference level, statistical methods including, without limitation, class distinction using unsupervised methods (e.g., k-means, hierarchical clustering, principle components, non-negative matrix factorization, or multidimensional scaling) (Hastie et al., 2009), supervised methods (e.g., discriminant analysis, support vector machines, or k-nearest-neighbors) or semi-supervised methods, or outcome prediction (e.g., relapse-free survival, disease progression, or overall survival) using Cox regression model (Kalbfleisch and Prentice, 2002), accelerated failure time model, Bayesian survival model, or smoothing analysis for survival data (Wand, 2003) may be involved.
In one embodiment, comparing with the reference level, the increased expression level of the marker gene indicates an increased likelihood of positive clinical prognosis, such as long-term survival without prostate cancer recurrence. In another embodiment, the increased expression level of the marker gene may indicate an decreased likelihood of positive clinical prognosis, such as recurrence rate of prostate cancer.
In the present application, the kit comprises a means for detecting the expression level of the molecular marker, for example, a probe or an antibody. The kit can further comprise a control group such as a probe or an antibody specifically binding to housekeeping gene(s) or protein(s) (e.g., beta-actin, GAPDH, RPL13A, tubulin, and the likes).
In one preferred embodiment, the kit can include at least one nucleic acid probe specific for ABCG1 transcript, PDCD4 transcript or KLF6 transcript; at least one pair of primers for specific amplification of ABCG1, PDCD4 or KLF6; and/or at least one antibody specific for ABCG1 protein, PDCD4 protein or KLF6 protein. The kit further comprises a nucleic acid probe, primers, and/or an antibody specific for housekeeping gene/transcript/protein.
In one embodiments, the primary detection means (e.g., probe, primers, or antibody) can be directly labeled with a fluorophore, chromophore, or enzyme capable of producing a detectable product (e.g., alkaline phosphates, horseradish peroxidase and others commonly known in the art), or, a secondary detection means such as secondary antibodies or non-antibody hapten-binding molecules (e.g., avidin or streptavidin) can be applied. The secondary detection means can be directly labeled with a detectable moiety. In other instances, the secondary or higher order antibody can be conjugated to a hapten (e.g., biotin, DNP, or FITC), which is detectable by a cognate hapten binding molecule (e.g., streptavidin horseradish peroxidase, streptavidin alkaline phosphatase, or streptavidin QDot™). In another embodiments, the kit can further comprise a colorimetric reagent, which is used in concert with primary, secondary or higher order detection means that are labeled with enzymes for the development of such colorimetric reagents.
In one embodiment, the kit further comprises a positive and/or a negative control sample(s), such as mRNA samples that contain or do not contain transcripts of the marker genes, protein lysates that contain or do not contain proteins or fragmented proteins encoded by the marker genes, and/or cell line or tissue known to express or not express the marker genes.
In some embodiments, the kit may further comprise a carrier, such as a box, a bag, a vial, a tube, a satchel, plastic carton, wrapper, or other container. The components of the kit can be enclosed in a single packing unit, which may have compartments into which one or more components of the kit can be placed; or, the kit includes one or more containers that can retain, for example, one or more biological samples to be tested. In some embodiments, the kit further comprises buffers and other reagents that can be used for the practice the prediction method.
The combination of molecular markers of the present application can be applied to a microarray, such as nucleic acid array or protein array. The microarray comprises a solid surface (e.g., glass slide) upon which the specific binding agents (e.g., cDNA probes, mRNA probes, or antibodies) are immobilized. The specific binding agents are distinctly located in an addressable (e.g., grid) format on the array. The specific binding agents interact with their cognate targets present in the sample. The pattern of binding of targets among all immobilized agents provides a profile of gene expression.
In one embodiment, the microarray consists of binding agents specific for at least two of the marker genes, for example, an microarray consists of nucleic acid probes or antibodies specific for ABCG1, PDCD4 and KLF6. The microarray can further includes nucleic acid probes or antibodies specific for one or a plurality of housekeeping genes or gene products, such as mRNA, cDNA or protein.
The nucleic acid probes or antibodies forming the array can be directly linked to the support or attached to the support by oligonucleotides or other molecules that serve as spacers or linkers to the solid support. The solid support can be glass slides or formed from an organic polymer. A variety of array formats can be employed in accordance with the present application. For instance, a linear array of oligonucleotide bands, a two-dimensional pattern of discrete cells, and the like.
The following examples are given for illustrative purposes only and are not intended to be limiting unless otherwise specified. It should be appreciated by those of skill in the art that the techniques disclosed in the examples which follow represent techniques discovered by the inventors to function well in the practice of invention, and thus can be considered to constitute preferred modes for its practice. Those of skill in the art should appreciate that many changes can be made in the specific embodiments which are disclosed and still obtain a like or similar result without departing from the spirit and scope of the invention.
The acinar differentiation process of prostatic glands was recapitulated by culturing prostatic epithelial RWPE-1 cells (Bello et al., 1997) within a physiological relevant three-dimensional (3D) culture model, as described before (Weaver et al., 1997). RWPE-1 cells were immortalized prostate epithelial cells derived from human prostate acini and were known to retain normal cytogenetic and functional characteristics (Bello et al., 1997). RWPE-1 cells were embedded and grown within a thick layer of 3D reconstituted basement membrane gel (Matrigel, BD Biosciences). The culture was maintained in Keratinocyte-SFM (Sigma-Aldrich) supplemented with bovine pituitary extract, 10 ng/ml epidermal growth factor and antibiotics (all from to Invitrogen) (Bello et al., 1997; Liu et al., 1998).
As shown in
To dissect the gene expression alterations related to this prostatic acinar differentiation process, global gene expression profiling experiments was carried out on RWPE-1 cells clusters formed in early-stage culture and acini formed at latter stages. Briefly, total RNA samples were extracted using TRIZOL (Invitrogen) and then purified using a RNeasy mini-kit and a DNase treatment (Qiagen). Experiments were performed in triplicate. Gene expression analysis was performed on an Affymetrix Human Genome U133A 2.0 Plus GeneChip platform according to the manufacturer's protocol (Affymetrix). The hybridization intensity data was processed using the GeneChip Operating software (Affymetrix) and the genes were filtered based on the Affymetrix P/A/M flags to retain the genes that were present in at least three of the replicate samples in at least one of the culture conditions. To select differentially expressed genes within a comparison group, a false discovery rate less than 0.025 was used.
Table 1 provides a detailed list of 411 unique genes (represented by 447 Affymetrix probe sets) were identified as differential expression genes during the acinar differentiation of RWPE-1 cells. These genes were identified from the microarray experiments based on their expression levels significantly different between RWPE-1 cell clusters and acini. The genes are ranked in descending order according to the ratio between the mean hybridization intensity of each probe in RWPE-1 acini and that in RWPE-1 cell clusters.
In
This example demonstrates that prostate cancers carrying the expression profile of the 411-gene in differentiated prostatic acini link to favorable clinical prognosis.
To demonstrate if the molecular profile associated with prostatic acinar differentiation carries important prognostic information in human prostate cancer, we interrogated a published gene expression microarray data set consisting of 21 patients with localized prostate cancer who underwent radical prostatectomy at the Brigham and Woman's Hospital (Boston, Mass.; the BWH cohort) (Singh et al., 2002). We determined the degree of resemblance between the patient tumors and prostatic acini by calculating the Pearson's correlation coefficients (racini) based on the expression of the 411 acinar differentiation-related genes.
In
As shown in Table 2, in a multivariate Cox proportional-hazards analysis, the racini of the tumors was found to be the only significant predictor of relapse (hazard ratio=0.173 (0.041-0.725), P=0.016).
To assess how robustly the expression profile of prostatic acini can stratify risk of relapse in prostate cancer, we repeated the above analysis in an independent tumor transcriptome data set derived from 29 prostate cancer patients who had received radical prostatectomy and had been followed up for up to 5 years (Lapointe et al., 2004).
As shown in Table 3, multivariate Cox regression analysis confirmed that racini provided independent prognostic information in prostate cancer while the Gleason score was only marginally prognostic in this cohort.
This example describes the identification of a 12-gene prognostic model of prostate cancer based on the molecular profile related to prostatic acinar differentiation.
Having demonstrated the prognostic value of the prostatic acini-related expression profile in prostate cancer, we sought to refine this profile and identify a smaller set of genes with higher clinical utility. To this end, we mapped the 411 acini-related genes to the BWH data set (Singh et al., 2002) and constructed a “recurrence score” based on a Cox's model to predict the occurrence of tumor relapse following radical prostatectomy. We used a previously described supervised approach with modifications (Wang et al., 2005). Briefly, for each gene, univariate Cox's regression analysis was used to measure the correlation between the expression level of the gene (on a log2 scale) and the length of relapse-free survival of the PCA patients in the BWH cohort. We constructed 1000 bootstrap samples of the patients in the cohort and performed Cox's regression analysis on each of the samples. We then determined an estimated P-value and an estimated standardized Cox regression coefficient for each gene by calculating the median P-values and the median Cox's coefficient of the 1000 bootstrap samples, respectively. To ensure the consistency of our model, we selected the genes whose expressional changes during prostatic acinar differentiation were associated with the expected positive (for genes up-regulated in cell clusters) or negative risk of relapse (for genes up-regulated in prostatic acini), as determined by the estimated standardized Cox regression coefficient. The selected genes were then ranked-ordered according to the estimated P-values, and multiple sets of genes were generated by repeatedly adding one more genes each time from top of the descendingly ranked list, starting from the first three top-ranked genes. Then a “recurrence score” (Equation 1) were calculated to measure the risk of post-operative recurrence of a patient for a gene set:
Recurrence score=Σi=3kbixi (Equation 1)
where k is the number of probes in the probe set, bi is the standardized Cox regression coefficient for the ith probe and xi is the log2 expression level for the ith probe.
For each selected probe set the concordance index (C-index) was used to evaluate the predictive accuracy in survival analysis (Pencina and D'Agostino, 2004). C-index statistics analysis was conducted using the ‘survcomp’ package in the statistical programming language R (cran.r-project.org). The gene set that achieved the maximal predictive accuracy while contained the fewest number of the genes was selected as the optimized prognostic predictor.
As shown in
Table 4 shows the identities of the 12 selected genes.
As shown in Table 5, multivariate Cox regression analysis demonstrates that this 12-gene model provides strong and independent prognostic information to prostate cancer (hazard ratio=42.304, P=0.004).
Table 6 shows that the 12-gene model markedly enhanced the prognostic accuracy of a combined clinical model including clinical and pathological variables (C-index from 0.620 to 0.847) and outperformed several previously reported prognostic gene signatures of prostate cancer (Glinsky et al., 2004; Singh et al., 2002).
†The 5-gene signature includes non-imprinted in Prader-Willi/Angelman syndrome region protein 2 (NIPA2) or HGC5466, wingless-type MMTV integration site family, member 5A (WNT5A), DENN/MADD domain containing 4B (DENND4B) or KIAA0476, inositol 1,4,5-trisphosphate receptor type 1 (ITPR1) and transcription factor 2 (TCF2).
This example describes the prognostic value of the respective markers in Table 4.
Cancer biomarkers are more clinically applicable if they can be incorporated in routine pathological examinations. To determine if the prognostic correlation of the genes in the 12-gene model could be observed at the protein and the tissue levels in human prostate cancer materials, the tissue expressions of three selected markers, including PDCD4, ABCG1 and KLF6, by performing immunohistochemistry staining of the tumor tissues from an independent cohort of 61 early-stage prostate cancer patients who underwent radical prostatectomy and had been followed up for up to 11 years at Chimei Foundational Medical Center (Tainan, Taiwan; the CFMC cohort). These markers were selected as specific and pathology validated antibodies are commercially available, which included anti-ABCG1 (clone EP1366Y), anti-PDCD4 (clone EPR3431), and anti-KLF6 (all from Epitomics, Burlingame, Calif.). Briefly, formalin-fixed, paraffin-embedded tissues of human prostate cancer and the associated clinical data from 61 patients who received radical prostatectomy at Chimei Foundational Medical Center were acquired and used in conformity with Institutional Review Board-approved protocols (the CFMC cohort). Biochemical recurrence of PCA was defined as a prostate-specific antigen (PSA) of at least 0.4 ng/ml or two consecutive PSA values of 0.2 mg/ml and rising (Stephenson et al., 2006). Tissue sections were deparaffinized, hydrated, immersed in citrate buffer at pH 6.0 for epitope retrieval in a microwave. Endogenous peroxidase activity was quenched in 3% hydrogen peroxidase for 15 minutes, and slides were then incubated with 10% normal horse serum to block nonspecific immunoreactivity. The antibody was subsequently applied and detected by using the DAKO EnVision kit (DAKO). All the immunohistochemical (IHC) staining was evaluated by the same expert pathologist and the staining patterns were quantified using the histological score (H-score) (Budwit-Novotny et al., 1986).
As shown in
As shown in Table 7, multivariate Cox-regression analyses demonstrated that PCDC4, ABCG1 or KLF6 was strongly prognostic independent of clinical criteria and Gleason's score.
This example describes a three-gene prognostic model of prostate cancer based on the expression levels of PDCD4, ABCG1 and KLF6.
In Example 4, three of the gene markers in the 12-gene model of prostate cancer, including PDCD4, ABCG1 and KLF6, can be examined by immunohistochemical staining of prostate tumor tissues. The staining intensities of each of these markers showed strong negative associations with risk of post-operative biochemical recurrence (
As shown in
As shown in Table 8, multivariate Cox regression analysis demonstrates that this three-gene model provides the strongest prognostic information to prostate cancer independent of clinical criteria and Gleason score (hazard ratio=22.591, P=0.004).
Table 9 shows that, according to concordance index (C-index) values (Pencina and D'Agostino, 2004), the predictive accuracy of the three-gene model reached 0.951, which significantly (P=0.001) outperformed a combined clinical model including age, tumor stage, serum PSA, and Gleason score, which had a prediction accuracy of 0.695 by C-statistics.
Having demonstrated the outstanding performance of the three-gene prognostic model of prostate cancer, we next tested its performance in the BWH cohort. In this data set, we used the transcript abundance levels of PDCD4, ABCG1 and KLF6 to calculate the recurrence score, and stratified the patients into two subgroups with high- or low-risk of post-operative relapse with the threshold determined by the maximal Youden's index.
As shown in Table 10, multivariate Cox regression analysis demonstrates that this three-gene model provides the strongest and independent prognostic information to prostate cancer with a hazard ratio for post-operative disease relapse reaching 59.551 (P=0.006).
Table 11 shows that, according to C-index, the predictive accuracy of the three-gene model in the BWH cohort reached 0.939 (P<0.001), which markedly (P=0.002) enhanced the prognostic accuracy of a combined clinical model including age, tumor stage, serum PSA, and Gleason score, which by itself did not have significant prognostic value (C-index=0.617, P=0.113).
This example describes a two-gene prognostic model of prostate cancer based on the expression levels of PDCD4 and ABCG1.
It was demonstrated that the expression levels of PDCD4 and ABCG1 could be used to establish an effective two-gene prognostic model of prostate cancer. We calculated the recurrence score (Equation 1) based on the staining intensities, as quantified by H-score, of PDCD4 and ABCG1 in the CFMC cohort. The patients were stratified into two subgroups with high- or low-risk of post-operative biochemical relapse according to the recurrence score with the threshold determined by the maximal Youden's index.
As shown in
As shown in Table 12, multivariate Cox regression analysis demonstrates that this two-gene model provides the strongest prognostic information to prostate cancer independent of clinical criteria and Gleason score (hazard ratio=16.25, P=0.002).
Table 13 shows that, according to C-index values, the predictive accuracy of the two-gene model reached 0.915, which significantly (P=0.012) outperformed a combined clinical model including age, tumor stage, serum PSA, and Gleason score.
The performance of the two-gene prognostic model in the 21-patient BWH cohort was tested next. In this data set, we used the transcript abundance levels of PDCD4 and ABCG1 to calculate the recurrence score, and stratified the patients into two subgroups with high- or low-risk of post-operative relapse.
As shown in Table 15, multivariate Cox regression analysis demonstrates that this two-gene model provides the strongest and independent prognostic information to prostate cancer with a hazard ratio for post-operative disease relapse reaching 139.963 (P=0.048).
Table 15 shows that, according to C-index, the predictive accuracy of the two-gene model in the BWH cohort reached 0.875 (P<0.001), which significantly (P=0.022) enhanced the prognostic accuracy of a combined clinical model including age, tumor stage, serum PSA, and Gleason score.
As shown in Table 16, we compared the predictive accuracy of the 12-gene model, the three-gene model and the two-gene model for clinical prognosis of prostate cancer patients in the BWH cohort. Remarkably, the three-gene model performed equally well with the 12-gene model (C-index 0.939, P<0.001, respectively). Although the two-gene model performed slightly less well than the 12-gene or the three-gene model (C-index=0.875, P<0.001), the difference in C-index did not reach statistical significance (P=0.134).
The performances of the three-gene model and the two-gene model in the prognostic prediction of patients in the CFMC cohort were further compared. As shown in Table 17, the three-gene model performed slightly better than the two-gene model, albeit without statistically significant difference (P=0.195).
This example describes the calculation of predicted recurrence rate and expected recurrence-free survival for patients with prostate cancer based on the 12-gene prognostic model shown in Example 3.
As described in Example 3, one can measure the risk of post-operative recurrence of a given patient with prostate cancer by calculating the recurrence score based on a selected gene set (Recurrence score=Σi=3kbixi (Equation 1)). For a patient whose recurrence score is known, the hazard rate of recurrence at time t of said patient can be estimated by Cox regression, and the hazard rate can be expressed as h(t)=h0(t)exp(bx), where x is the value of recurrence score, b is the regression coefficient, and h0 (t) is the baseline hazard function. The predicted recurrence rate at time t can be estimated according to
F(t)=1−S0(t)exp(bx) (Equation 2)
Where S0(t)=exp[−∫0th0(u)du] is the baseline recurrence-free function. The calculation can be carried out by commercial software such as the SPSS software (IBM) or the like. Further, the median recurrence time can be solved by F(t)=1−S0(t)exp(bx) (Equation 2) as setting F(t)=0.5.
For example, the recurrence score of a given patient in the BWH cohort can be calculated based on the transcript abundance levels of the 12 gene markers of said subject as follows:
The estimated Cox regression is h(t)=h0(t)exp(1.490x). The recurrence function can be represented by
F(t)=1−S0(t)exp(1.490x) (Equation 4)
The values of estimated S0(t) are shown in Table 18.
Thus, given the transcript abundance levels of the 12 gene markers listed in
Table of a given patient, one can predict the recurrence rate and expected relapse-free survival of said patient by F(t)=1−S0(t)exp(bx) (Equation 2),
and Table 12. Table 19 shows the results of prediction in four patients selected from the BWH cohort.
This example describes the calculation of predicted recurrence rate and expected recurrence-free survival for patients with prostate cancer based on the 3-gene prognostic model as shown in Example 5.
The same principle in Example 7 can be used to apply the three-gene model, as shown in Example 5, to predict the recurrence rate and expected recurrence-free survival in patients in the CFMC cohort. According to Recurrence score=Σi=kxi (Equation 1), one can calculate the recurrence score of a given patient in the CFMC cohort based on the staining intensities, as represented by the H-scores, of PDCD4, ABCG1 and KLF6 in the tumor of said patient using x=7.112+(−2.771 ABCG1−2.814 KLF6−3.442 PDCD4)/3 (Equation 5).
The estimated Cox regression is h(t)=h0(t)exp(1.235x). The recurrence function can be represented by
F(t)=1−S0(t)exp(1.235x) (Equation 6).
Table 20 shows the values of the estimated S0(t).
Thus, for any patient in the CFMC cohort whose staining intensities of ABCG1, PDCD4 and ABCG1 are known, the predicted 3-year and 5-year recurrence rates and expected recurrence-free survival can be calculated according to x=7.112+(−2.771 ABCG1−2.814 KLF6−3.442 PDCD4)/3 (Equation 5), F(t)=1−S0(t)exp(1.235x) (Equation 6) and Table 20. Table 21 shows the results of the prediction in four patients selected from the CFMC cohort.
Using the same principle, one can calculate the recurrence score based on the transcript abundance levels of ABCG1, PDCD4 and KLF6 according to
x=16.682+(−1.636ABCG1−2.601KLF6−2.185PDCD4)/3 (Equation 7).
The estimated Cox regression is h(t)=h0(t)exp(0.672x) and the recurrence function can be calculated by
F(t)=1−S0(t)exp(0.672x) (Equation 8).
Table 22 shows the values of estimated S0(t).
Table 23 shows the predicted 3-year recurrence rates and recurrence-free survival in four patients selected from the BWH cohort.
According to the above results, the present application provides the combinations of molecular markers for predicting the clinical prognosis of prostate cancer. Compared with the known models, the present application shows improved accuracy and is suitable for clinical use.
This application is a divisional application of U.S. application Ser. No. 13/853,548, filed on Mar. 29, 2013, which application claims priority to U.S. Provisional Application No. 61/617,293 filed on Mar. 29, 2012, each of which is incorporated herein by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 61617293 | Mar 2012 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 13853548 | Mar 2013 | US |
| Child | 14568075 | US |
