WO2009055480A2

WO2009055480A2 - Tgf-beta gene expression signature in cancer prognosis

Info

Publication number: WO2009055480A2
Application number: PCT/US2008/080802
Authority: WO
Inventors: Snorri S. Thorgeirsson; Cedric Coulouarn
Original assignee: The United States Of America, As Represented By The Secretary, Department Of Health And Human Services
Priority date: 2007-10-22
Filing date: 2008-10-22
Publication date: 2009-04-30
Also published as: WO2009055480A3

Abstract

The invention provides methods and kits for determining a prognosis of cancer in a subject by providing a sample taken from a subject, determining a TGF-β signature of the sample using high throughput analysis, and comparing the TGF-β signature of the sample with a control. The aberration of the TGF-β signature of the sample as compared to the control indicates a prognosis of aggressively malignant disease or minimally malignant disease.

Description

TGF-BETA GENE EXPRESSION SIGNATURE IN CANCER PROGNOSIS

BACKGROUND OF THE INVENTION

[0001] Hepatocellular carcinoma (HCC) is one of the most common cancers in the world. HCC, like other cancers, can be reliably diagnosed using well-known methods. Patients with HCC have a highly variable clinical course, and for any given patient, prognostication of disease progression is difficult.

[0002] Improved prognostic indicators for HCC as well as other cancers, such as cancers of the lung, colon, pancreas, and breast, are desired.

BRIEF SUMMARY OF THE INVENTION

[0003] Transforming Growth Factor-β (TGF-β) is a pleiotropic cytokine that controls many aspects of cell behavior. TGF-β is thought to act as a tumor suppressor in early stages of tumor development by its abilities to inhibit proliferation and induce apoptosis, but it is also thought to harbor oncogenic potential by enhancing tumor progression in late stages of carcinogenesis. It has been surprisingly found that the TGF-β signature, a compilation of expression levels of TGF-β responsive genes, can provide insight into the invasiveness of cancerous tumor cells.

[0004] The invention provides methods of determining a prognosis of cancer in a subject by (a) providing a sample taken from a subject; (b) determining a TGF-β signature of the sample using high throughput analysis; and (c) comparing the TGF-β signature of the sample with a control, wherein aberration of the TGF-β signature of the sample against the control indicates a prognosis of aggressively malignant disease or minimally malignant disease. [0005] The invention also provides a kit for determining a prognosis of cancer in a subject comprising (a) a reagent for assaying a TGF-β signature of a sample taken from a subject using high throughput analysis; and (b) instructional material for interpreting the TGF-β signature of the sample as compared to a control, wherein aberration of the TGF-β signature of the sample against the control indicates a prognosis of aggressively malignant disease or minimally malignant disease.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S) [0006] Figure 1 includes bar graphs showing temporal organization of 314 genes included in the TGF-β signature derived from mouse hepatocytes, with each time-point bar representing the mean +/- s.e.m. of fold difference between treated vs. untreated WT hepatocytes.

[0007] Figure 2 is a cluster analysis of the mouse dataset integrated with 139 cases of human HCC, based on the expression of 249 orthologous genes, including clustering of mouse samples (upper portion) corresponding to WT and KO hepatocytes challenged (+) or not (-) with TGF-β for the time indicated at the beginning of each row, and also including clustering of human HCC samples (lower portion) distributed among early, late, and negative

TGF-β signature subgroups.

[0008] Figure 3 A is a Kaplan-Meier plot and log-rank statistics for survival of individuals with subtypes of HCC (early, late, negative) based on TGF-β signature.

[0009] Figure 3B is a Kaplan-Meier plot and log-rank statistics for recurrence of HCC in individuals with subtypes of HCC based on TGF-β signature.

[0010] Figure 3 C is a Kaplan-Meier plot and log-rank statistics for vascular invasion rate in the three HCC subgroups.

[0011] Figure 4 provides bar graphs showing expression of selected exemplary genes

(transforming growth factor beta 1 (TGFBl), SMAD family member 2 (SMAD2), TGFB- induced factor homeobox 2 (TGIF2), snail homolog 1 (SNAIl), Twist homolog 1 (TWISTl), vimentin (VIM), matrix metallopeptidase 1 (MMPl), CD44 molecule (CD44), metastasis suppressor 1 (MTSSl)) in cells harboring early as opposed to late TGF-β signatures, with gene expression reported as a percentage over the cell lines harboring an early TGF-β signature (Mean +/- s.e.m.).

[0012] Figure 5 A is a dendrogram overview of early and late mouse TGF-β signatures integrated with the gene expression profiles of human HCC and lung adenocarcinomas showing early (0.5-2 hrs) and late (4-24 hrs) TGF-β signatures

[0013] Figure 5B provides Kaplan-Meier plots and log-rank statistics on survival rates of individuals harboring early or late TGF-β signatures.

DETAILED DESCRIPTION OF THE INVENTION

[0014] The invention provides a method of determining a prognosis of cancer in a subject. The method comprises: (a) providing a sample taken from a subject; (b) determining a TGF-β signature of the sample using high throughput analysis; and (c) comparing the TGF- β signature of the sample with a control. Aberration of the TGF-β signature of the sample against the control indicates a prognosis of aggressively malignant disease or minimally malignant disease. [0015] The TGF-β signature of the sample is the expression profile of a set of TGF-β responsive genes such as those listed in Tables IA-D herein. In some embodiments, the TGF-β signature will include expression levels of about 150 of the genes listed in Tables IA- D. In other embodiments, the TGF-β signature will include expression levels of more than about 150 of the listed genes, such as about 160, 170, 180, 190, 200, 210, 220, 230, 240, or 250 or intervening numbers of the listed genes. In still other embodiments, the TGF-β signature will include expression levels of fewer than about 150 of the listed genes, such as about 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140 or intervening numbers of the listed genes. In some preferred embodiments, the TGF-β signature includes expression levels of about 50 to about 150 of the genes listed in Tables IA-D. However, one of ordinary skill in the art can select any representative number of genes from Tables IA-D. [0016] The TGF-β signature of the sample can be an early or late TGF-β signature. Genes associated with an early TGF-β signature are expressed relatively soon after exposure to TGF-β, i.e., typically within two hours after exposure. Genes associated with a late TGF- β signature are delayed in expression after TGF-β exposure; they are typically expressed more than four hours after exposure.

[0017] Genes associated with an early TGF-β signature are listed in Tables IA and IB, while genes associated with a late TGF-β signature are listed in Tables 1C and ID. If the TGF-β signature is an early TGF-β signature, an aberrant TGF-β signature indicates a prognosis of minimally malignant disease. If the TGF-β signature of the sample is a late TGF-β signature, and aberrant TGF-β signature indicates a prognosis of aggressively malignant disease. For either early or late TGF-β signatures, tumors having an aberrant TGF- β signature are thought to have a different prognosis from tumors that do not have an aberrant TGF-β signature. A non-aberrant TGF-β signature, i.e., a negative TGF-β signature, is a signature in which expression of genes associated with TGF-β are not significantly different from a control.

[0018] The prognosis of aggressively malignant disease or minimally malignant disease refers to the clinical course of a patient's cancer. Aggressively malignant disease is characterized by an invasive phenotype including decreased mean survival time and an increased risk of recurrence and vascular invasion rate. Conversely, minimally malignant disease is characterized by an increased mean survival time and a decreased risk of recurrence and vascular invasion rate. [0019] Aberration of the TGF-β signature of the sample as compared to the control can include increased or decreased expression of the genes selected for analysis. The overall aberration of the TGF-β signature from the control can be determined by standard gene expression analysis methods including cluster analysis. See, e.g., Novak et al., Genomics 79: 104 -113 (2002); Ihaka et al., J. Comput. Graph Stat. 5: 299 -314 (1996); Eisen et al., Proc. Natl. Acad. Sci. 95: 14863-14868 (1998).

[0020] The sample can be any sample of cancerous tissue, such as a sample taken from a cancerous tumor. The tumor can be any cancerous tumor such as hepatocellular carcinoma, a lung tumor, a pancreatic tumor, a colon tumor, and a breast tumor. In some preferred embodiments, the tumor is hepatocellular carcinoma. In other embodiments, the tumor is a lung tumor.

[0021] The subject can be a human or any suitable non-human mammal such as a mouse, rat, rabbit, cat, dog, pig, sheep, cow, or primate. In some embodiments, the subject is a non- human experimental animal model. In a preferred embodiment, the subject a primate. In a more preferred embodiment, the subject is a human.

[0022] The control can be any suitable negative control. In some embodiments, the control is a non-cancerous tissue sample taken from the same species as the control subject. In other embodiments, the control is a non-cancerous tissue sample taken from the same type of organ as the tested sample. In other embodiments, the control is a standardized TGF-β profile of non-cancerous tissue.

[0023] The high throughput analysis can be executed using any suitable method, many of which are known to those of ordinary skill in the art. In some embodiments, methods such as PCR, microarray based RNA analysis, and/or DNA sequence analysis can be used to determine the TGF-β signature of the sample. In a preferred embodiment, microarray based RNA analysis is used to determine the TGF-β signature. Exemplary protocols for such analysis can be found in Quackenbush et al., N. Eng. J. Med. 354(23): 2463-72 (2006). [0024] In another aspect, the invention provides a kit for determining a prognosis of cancer in a subject comprising (a) a reagent for assaying a TGF-β signature of a sample taken from a subject using high throughput analysis; and (b) instructional material for interpreting the TGF-β signature of the sample as compared to a control, wherein aberration of the TGF-β signature of the sample against the control indicates a prognosis of aggressively malignant disease or minimally malignant disease. [0025] The reagent can be any reagent(s) for use in performing high throughput analysis by any suitable method, many of which are known to those of ordinary skill in the art. In some embodiments, one or more reagents are provided for a method such as PCR, microarray based RNA analysis, and DNA sequence analysis, any of which can be used to determine the

TGF-β signature of the sample. In a preferred embodiment, microarray based RNA analysis is used to determine the TGF-β signature.

[0026] The kit of the present invention can be used in determining the prognosis of any subject, particularly a mammal. Preferably the mammal is a human. In other embodiments, the mammal can be a mouse, rat, rabbit, cat, dog, pig, sheep, cow, primate, or another mammal.

[0027] The sample and the control can be any sample or control suitable for use in the methods of the invention as described above.

[0028] The following examples further illustrate the invention but, of course, should not be construed as in any way limiting its scope.

EXAMPLE 1

[0029] This example demonstrates the preparation of a temporal TGF-β gene expression signature in mouse primary hepatocytes.

[0030] Gene expression profiling was performed on primary hepatocytes isolated from Tgfbr2^+/+/AlbCre^+/~ (WT) mice. Conditional knock-out (KO) mice (Tgfb^^/AlbCre^) were used as a control for TGF-β dependency. Hepatocytes from KO mice, which lack exon 4 of Tgfbr2 gene, are thought to be refractory to TGF-β stimulation. Specific disruption of Tgfbr2 was assessed as described in Oe et al, Hepatology 40: 1098-1105 (2004). Deficient intact TGF-β signaling in KO hepatocytes was confirmed by resistance to TGF-β induced growth inhibition.

[0031] Hepatocytes were isolated from WT and KO mice and paired primary cultures were exposed to 1 ng/mL of recombinant TGF-β (R&D Systems, Minneapolis, MN) or vehicle alone for 0.5, 1, 2, 4, 12, and 24 hours. Messenger RNA abundance was quantified using genome-wide mouse microarrays (38,000 probes) as described in Coulouarn et al., Hepatology 44: 103-1011 (2006). At each time point, genes in which expression was significantly altered by TGF-β in WT but not KO hepatocytes (P< 0.01, random- variance t- test) were included as part of the TGF-β signature. Excluding genes differentially expressed between the two genotypes at basal conditions, the TGF-β signature included a total of 314 genes (Tables IA-D). Up and down regulated genes (52% vs. 48% respectively) initiated after a short term (0.5, 1, or 2 hours) or long-term (>4 hours) of TGF-β treatment were then divided into early and late clusters (Fig. 1).

[0032] Ingenuity Pathway Analysis (Ingenuity Systems, Inc., Redwood City, CA) was used to detect and confirm the functional association among genes included in the TGF-β signature, both early (0.5, 1, or 2 hours) and late (>4 hours). Genes thereby determined to be included in the early TGF-β signature have previously been associated with functions such as transcription activation of inducers of cell cycle arrest and apoptosis (see, e.g., Siegel et al., Nat. Rev. Cancer 3: 807-821 (2003)). Genes thereby determined to be included in the late TGF-β signature have previously been associated with functions such as lipid homeostasis, cellular redox status, cholesterol biosynthesis, glutathione metabolism, cytoskeleton organization, cell adhesion, and matrix remodeling (see, e.g., Derynck et al., Nat. Genet. 29: 117-129 (2001); Thiery et al., Nat. Rev. MoL Cell Biol. 7: 131-142 (2006); Brown et al., Cell 89: 331-340 (1997); Giudice et al., Bioessays 28: 169-181 (2006)). The pathways thus found to be regulated by TGF-β are considered to show that TGF-β has a role in the modification of the cellular microenvironment and characteristics such as the epithelial-mesenchymal transition, which are factors in cancer development.

[0033] These results indicate that the TGF-β signature provides information useful to determining a prognosis or phenotype of malignant tumors.

EXAMPLE 2

[0034] This example demonstrates the characterization of human and mouse tumor type based on TGF-β signature.

[0035] The murine TGF-β signature as prepared in Example 1 was integrated with 139 cases of human HCC. Of the 314 mouse genes included in the TGF-β signature, 249 human orthologs were determined (Jackson Laboratory, Bar Harbor, Maine). Gene expression values from human and mouse datasets were independently standardized by adjustment of mean and standard deviation (SD) to 0 and 1, respectively, as described in Lee et al., Nat. Genetics 36: 1306-1311 (2004). Based on the expression of the 249 orthologous genes, hierarchical clustering of the integrated dataset established the degree of similarity between human and mouse samples. This procedure identified two major clusters (Fig. 2). [0036] Cluster 1, identified as TGF-β positive, included all mouse samples corresponding to WT hepatocytes challenged with TGF-β, except for the 0.5 hour data points. Cluster 2, identified as TGF-β negative, included all samples derived from KO mice (treated and untreated) as well as all untreated WT samples. Human liver tumors were divided into the positive and negative TGF-β clusters. Two homogeneous groups of TGF-β positive human HCC were then refined and noted as Positive-Early and Positive-Late. [0037] To test the predictive value of the temporal TGF-β signature in the identification of specific HCC subtypes, six different prediction algorithms (SVM, CCP, INN, 3NN, NC, and LDA) were applied to train and test a set of classifier genes, as described generally in Lee et al, Nat. Genetics 36: 1306-1311(2004). Human HCC with early or late TGF-β signatures were randomly divided into training and testing sets. When the trained classifier genes were applied to the testing set, all 6 algorithms predicted HCC with late or early TGF-β signatures, with a prediction rate ranging from 93% to 100%.

[0038] Expression profiles were then analyzed for 104 HCC and 7 liver metastases uploaded from the Stanford Microarray Database (genome-www5.stanford.edu, Stanford University, Stanford, CA). Cluster analysis based on the expression of 176 orthologous genes present in the mouse and human datasets further demonstrated that the temporal TGF-β gene expression signature could successfully discriminate significant subtypes of HCC. Moreover, all seven liver metastases exhibited a late TGF-β signature. When the prediction models were applied to this independent dataset with the HCC training set, all six algorithms associated the metastatic liver lesions with the late TGF-β signature, with a prediction rate of 100% (see Table 2).

[0039] These results indicate that the late TGF-β signature genes are linked to an invasive tumor phenotype.

EXAMPLE 3

[0040] This example further demonstrates the characterization of human and mouse tumor type based on TGF-β signature.

[0041] Early and late mouse TGF-β signatures are integrated with the gene expression profiles of 19 human HCC-derived cell lines known to exhibit variable tumorigenic and invasive phenotypes (see, e.g., Lee et al., Hepatology 35: 1134-1143 (2002)). Some cell lines are found to have an early TGF-β signature (Hep3B, Hep3B-TR, Hep40, HepG2, HUHl, HUH6, HUH7, and PLC/PRF/5). Other cell lines are found to have a late TGF-β signature (7703, Focus, HLE, HLF, SK-Hepl, SNU182, SNU387, SNU398, SNU423, SNU449, and SNU475). Statistical models of the TGF-β signature used as described above are able to accurately predict early or late categorization as shown in Table 3.

[0042] A functional invasion assay performed using a protocol such as found in Hendrix et al., Cancer Lett. 38: 137-147 (1987), indicates that cell lines having a late TGF-β signature (e.g. SK-Hep-1) exhibited a more invasive phenotype than those having an early TGF-β signature (e.g. HepG2).

[0043] Gene expression patterns of selected genes associated with an invasive phenotype

(TGFBl, SMAD2, TGIF2, SNAIl, TWISTl, VIM, MMPl, CD44, and MTSSl) are evaluated in early TGF-β signature cell lines as compared to late TGF-β cell lines. As shown by the exemplary comparisons provided in Figure 4, cell lines having late TGF-β signatures overexpress the selected genes, which are associated with increased cell motility, metastasis, and epithelial-mesenchymal transition (EMT).

[0044] These results confirm that the late TGF-β signature genes are linked to an invasive tumor phenotype.

EXAMPLE 4

[0045] This example demonstrates the clinical significance of TGF-β in the molecular classification of HCC.

[0046] The distribution of several clinical and pathological variables was examined in HCC patients whose tumors harbor early, late, or negative TGF-β signatures. The three subtypes of HCC (early, late, or negative) were similar with respect to the patients' gender, age, presence of cirrhosis in surrounding tissues, tumor size, Edmondson grade and plasma level of α- fetoprotein (Table 4). Kaplan-Meier plots and log-rank statistics indicated that patients with a late TGF-β signature showed a significantly (P<0.005) shortened mean survival time (16.2 +/- 5.3 months) compared to the patients with an early (60.7 +/- 16.1 months) or a negative (37.2 +/- 4.3 months) TGF-β signature (Table 4 and Fig. 3A). [0047] Data from a previous study regarding patient survival (Le et al, Hepatology 40: 667-676 (2004)) was integrated with the data of Example 1, as shown in Fig. 2B. Patients having a late TGF-β signature (Survival Group A) had a worse prognosis than patients having an early or negative TGF-β signature (Survival Group B). Patients having a late TGF-β signature recurred significantly earlier (18.1 +/- 2.7 months) than patients with a negative (43.2 +/- 16.6 months) TGF-β signature (Table 4 and Fig. 3B). In contrast, patients having an early TGF-β signature recurred later (68.2 +/- 17.6 months) than patients with a negative TGF-β signature. Additionally, clinical data indicate a trend toward more advanced Edmondson differentiation of grade 3-4 within the group of HCC having a late TGF-β signature.

[0048] Integration of data from Kaposi-Novak, J. Clin. Invest. 116: 1582-1595 (2006), with the data of Example 1 indicated that HCC patients with a late TGF-β signature shared a MET/HGF signature (Fig. 2B). Kaposi-Novak showed that a MET/HGF signature is associated with increased vascular invasion rate and microvessel density, as well as decreased mean survival time of patients. The vascular invasion rate in the subgroups of HCC defined by TGF-β signatures indicate that the late TGF-β is also associated with an invasive phenotype (Fig 3C).

[0049] These results demonstrate that early and late TGF-β signatures are associated with differences in clinical outcome as compared with negative TGF-β signatures.

EXAMPLE 5

[0050] This example demonstrates that TGF-β expression signature can be used in prognosis of human lung adenocarcinomas.

[0051] Early and late mouse TGF-β signatures prepared as described in Example 1 were integrated with gene expression profiles of human lung adeoncarcinomas as described in Kaposi-Novak et al, J. Clin. Invest. 116: 1582-1595 (2006).

[0052] Based on the expression of 174 orthologous genes, hierarchical clustering analysis successfully identified two distinct subtypes of lung tumors which harbored early and late mouse TGF-β signatures (Figs. 5A-B). Kaplan-Meier plots and log-rank statistics recapitulated the results obtained for liver tumors. Additionally, liver cancer patients showed shortened mean survival time compared to patients with an early TGF-β signature. [0053] These results demonstrate that TGF-β signature is not limited to use in determining prognosis of HCC, but also can be used in determining prognosis of lung adenocarcinoma.

[0054] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.

[0055] The use of the terms "a" and "an" and "the" and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The terms "comprising," "having," "including," and "containing" are to be construed as open-ended terms (i.e., meaning "including, but not limited to,") unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., "such as") provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.

[0056] Preferred embodiments of this invention are described herein, including the best mode known to the inventors for carrying out the invention. Variations of those preferred embodiments may become apparent to those of ordinary skill in the art upon reading the foregoing description. The inventors expect skilled artisans to employ such variations as appropriate, and the inventors intend for the invention to be practiced otherwise than as specifically described herein. Accordingly, this invention includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the above-described elements in all possible variations thereof is encompassed by the invention unless otherwise indicated herein or otherwise clearly contradicted by context.

Claims

CLAIM(S):

1. A method of determining a prognosis of cancer in a subject comprising:

(a) providing a sample taken from a subject;

(b) determining a TGF-β signature of the sample using high throughput analysis; and

(c) comparing the TGF-β signature of the sample with a control, wherein aberration of the TGF-β signature of the sample as compared to the control indicates a prognosis of aggressively malignant disease or minimally malignant disease.

2. The method of claim 1, wherein the TGF-β signature of the sample is an early TGF-β signature.

3. The method of claim 2, wherein the prognosis is minimally malignant disease.

4. The method of claim 1, wherein the TGF-β signature of the sample is a late TGF-β signature.

5. The method of claim 4, wherein the prognosis is aggressively malignant disease.

6. The method of claim 1 , wherein the sample is taken from a cancerous tumor.

7. The method of claim 6, wherein the tumor is selected from the group consisting of hepatocellular carcinoma, a lung tumor, a pancreatic tumor, a colon tumor, and a breast tumor.

8. The method of claim 7, wherein the tumor is a hepatocellular carcinoma.

9. The method of claim 7, wherein the tumor is a lung tumor.

10. The method of claim 1 , wherein the subject is a mammal.

11. The method of claim 1 , wherein the subject is a human.

12. The method of claim 1, wherein the high throughput analysis is executed using a method selected from the group consisting of PCR, microarray based RNA analysis, and DNA sequence analysis.

13. The method of claim 1, wherein the control is a non-cancerous tissue sample.

14. A kit for determining a prognosis of cancer in a subject comprising:

(a) a reagent for assaying a TGF-β signature of a sample using high throughput analysis; and

(b) instructional material for interpreting the TGF-β signature of the sample as compared to a control, wherein aberration of the TGF-β signature of the sample as compared to the control indicates a prognosis of aggressively malignant disease or minimally malignant disease.

15. The kit of claim 14, wherein the TGF-β signature of the sample is an early TGF-β signature.

16. The kit of claim 15, wherein the prognosis is minimally malignant disease.

17. The kit of claim 14, wherein the TGF-β signature of the sample is a late TGF-β signature.

18. The kit of claim 17, wherein the prognosis is aggressively malignant disease.

19. The kit of claim 14, wherein the sample is taken from a cancerous tumor.

20. The kit of claim 19, wherein the tumor is selected from the group consisting of hepatocellular carcinoma, a lung tumor, a pancreatic tumor, a colon tumor, and a breast tumor.

21. The kit of claim 20, wherein the tumor is a hepatocellular carcinoma.

22. The kit of claim 20, wherein the tumor is a lung tumor.

23. The kit of claim 14, wherein the subject is a mammal.

24. The kit of claim 14, wherein the subject is a human.

25. The kit of claim 14, wherein the high throughput analysis is executed using a method selected from the group consisting of PCR, microarray based RNA analysis, and DNA sequence analysis.

26. The kit of claim 14, wherein the control is a non-cancerous tissue sample.