METHODS FOR THE ANALYSIS OF BREAST CANCER DISORDERS

FIELD OF THE INVENTION

The present invention relates to methods for analysis of breast cancers using methylation patterns.

BACKGROUND OF THE INVENTION

Currently there are epigenetic studies available that show the relationship between gene promoter methylation and cancer. The promoter regions of most housekeeping genes and about 40% of tissue specific genes are characterized by such CpG-islands. Methylation in these CpG islands is generally associated with gene silencing. Programmed DNA methylation plays an important role in normal embryonic development where waves of global demethylation followed by de novo methylation characterize the early pre-implantation development. During tumorigenesis global DNA hypomethylation has also been reported, which results in chromosomal instability and expression of some repeat elements (such as transposons). Hormonal influence is reported as common to all women's related cancers including breast cancer. The research focus lately has shifted from genetic to epigenetic factors as potential biological mechanisms. This in turn makes these epigenetic mechanisms conducive to being explored as potential diagnostic biomarkers. Tumor suppressors, oncogenes, and other cell signalling genes have already been studied individually for promoter methylation. In these studies, there are different levels of sensitivity and specificity reported for various genes.

WO 2009/037633 discloses method for the analysis of ovarian cancer disorders comprising determining the genomic methylation status of one or more CpG dinucleotides.

The inventor of the present invention has appreciated that an improved method for classifying a breast cancer disorder is of benefit, and has in consequence devised the present invention.

SUMMARY OF THE INVENTION

It would be advantageous to achieve an improved classification of breast cancer disorders based on determining the methylation status of one or more DNA sequences. It would also be desirable to enable improved classification of breast cancers by further determining methylation status of one or more DNA sequences and the expression levels of one or more proteins. In general, the invention preferably seeks to mitigate, alleviate or eliminate one or more of the above mentioned disadvantages singly or in any combination. In particular, it may be seen as an object of the present invention to provide a method that solves the above mentioned problems, or other problems, of the prior art.

To better address one or more of these concerns, in a first aspect of the invention a method is presented that relates to analysis of a breast cancer disorder in a subject, said method comprising determining the methylation status of one or more sequences selected from the group consisting of SEQ ID NO: 1-111.

In the present context the phrase “methylation status” is to be understood as the extent of presence (hypermethylated) or absence (hypomethylated) of methyl (CH3) group on carbon number 5 of pyrimidine ring of cytosine base in DNA.

The one or more sequences according to the invention may be positioned in or on a composition or array. Thus, in another aspect the invention relates to a composition or array comprising nucleic acids with sequences which are identical to at least 10 of the sequences according to SEQ ID NO: 1-111.

In the present context the phrase “composition or array” is to be understood as also encompassing University Healthcare Network (UHN) Toronto human CpG island 12 k microarray chip (HCGI12K). The methods according to the invention may be performed by a computer. Thus, in a further aspect the invention relates to a computer program product being adapted to enable a computer system comprising at least one computer having a data storage means associated therewith to operate a processor arranged for carrying out a method according to the invention.

In general the various aspects of the invention may be combined and coupled in any way possible within the scope of the invention. These and other aspects, features and/or advantages of the invention will be apparent from and elucidated with reference to the embodiments described hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention will be described, by way of example only, with reference to the drawings, in which

FIG. 1 shows workflow of the Breast Cancer Study

FIG. 2 shows the steps involved in designing the CpG island arrays (From the original UHN Toronto paper).

FIG. 3 shows Volcano plot after t-test against zero mean null hypothesis for IDC vs normal.

FIG. 4 shows Volcano plot of T-test results IDC vs. benign with fold change above 1.5.

FIG. 5 shows Analysis on IDCvsNormal samples where p-value cut off <=0.05 relating to pre- and post menopause status.

FIG. 6 shows Fold change between Her2− against Her2+ samples in IDC vs. normal.

FIG. 7 shows Fold change of 44 loci between post and pre menopausal cases in IDC vs. normal.

FIG. 8 shows Fold change of between ER− against ER+ samples in IDC vs. normal.

FIG. 9 shows Fold change of between PR− against PR+ samples.

FIG. 10 shows Fold change of between ER−/PR−/Her2− against ER+/PR+/Her2+ samples in IDC vs. normal.

FIG. 11 shows clustering on IDCvsNormal samples after t-test post vs. premenopausal status, p-value cut off <=0.05.

FIG. 12 shows 24 entities which had a fold change of >1.3 depending on the onset of breast cancer.

FIG. 13 shows a clustering analysis of the breast cancer onset of the disease.

FIG. 14 shows an overview of key modifiers in significantly changed pathways in breast cancer using differential methylation data from IDC vs. normal samples.

FIG. 15 shows differentially methylated genes CCND1, BCL2L1, ERBB4 and PARK2 as being important hubs in the gene network of key regulators and targets.

FIG. 16 shows transcription regulators where ETS1 and AHR are being active in our IDC vs. normal sample set.

DESCRIPTION OF EMBODIMENTS
Method for Analysis of a Breast Cancer Disorder

The general aim of the study was to identify novel differentially methylated genes in breast cancer. Differential Methylation Hybridization was performed using a UHN CpG 12 k DNA microarray chip with DNA from breast cancer patient biopsy material as the sample source. The genomic DNA from the biopsy material from each individual patient was coupled with its corresponding normal counterpart. The DNA fragments generated as per the protocol were enriched for methylated fragments using methylation sensitive restriction digestion and subsequently the cancerous and normal DNA was labeled with Cy5 and Cy3 respectively. After hybridization the microarray chip was scanned and data analysed to reveal genes which showed differential methylation in breast cancer.

In general the present invention relates to determining the methylation status of one more DNA sequences in a breast tissue sample obtained from a subject. Thus, in an aspect the invention relates to a method for analysis of a breast cancer disorder in a subject, said method comprising determining the methylation status of one or more sequences selected from the group consisting of SEQ ID NO: 1-111.

The number of sequences to be determined may vary depending on the sample. Thus in an embodiment the methylation status is determined for at least 5 sequences, such as at least 10 sequences, such as at least 20 sequences, such as at least 40 sequences, such as at least 80 sequences, or such as at least 100 sequences.

In a further embodiment the invention relates to a method, wherein the analysis comprises assisting in classifying a breast cancer disorder, wherein the following steps are performed,

- providing a sample from a subject to be analyzed,
- determining the methylation status for one or more sequences according to SEQ ID NO:1-111.

The sample may be obtained from a human such as a female. In an embodiment the methylation status is determined for at least 10 sequences from SEQ ID NO: 1-75.

Classification

The classification may be divided based on a multi variate model. Thus, in another embodiment the invention relates to a method, further comprising

- a) the one or more results from the methylation status test is input into a classifier that is obtained from a Multi Variate Model,
- b) calculating a likelihood as to whether the sample is from a normal breast tissue, infiltrating ductal carcinoma (IDC) or a benign breast tumor.

In the present context the wording “Multi Variate Model” is to be understood as models defined in terms of several (more than one) parameters.

In a specific embodiment the multivariate model used is Principle Component Analysis (PCA). It is a mathematical algorithm which reduces the dimensionality of the data while retaining most of the variation in the data set. It accomplishes this reduction by identifying directions called principle components along which the variation in the data is maximum. By using a few components each sample can be represented by relatively few numbers instead of by values for thousands of variables. By assisting in determining whether the sample is a normal breast tissue, infiltrating ductal carcinoma (IDC) or a benign breast tumor, a better therapy, diagnosis and prognosis may be obtained. By having a decision supported by multiple methylation patterns a stronger correlation may be obtained

Data Analysis Using Clinical Parameters

The method according to the invention may take further into account the expression level of different proteins. Thus, in yet an embodiment the invention relates to a method, further comprising determining at least one parameter in a sample obtained from said subject, said parameter being the expression level of at least one of the following proteins selected from the group consisting of Estrogen Receptor (ER), Progesterone receptor (PR) and Herceptin (HER2) in said sample. The person skilled in the art would know that such expression may be determined at e.g. the protein level and/or the RNA level.

By combining both protein expression and methylation status a stronger probability for making correct classification is obtained.

HER2 Status

To determine which sequences are relevant based on expression levels is not obvious. Thus, in an embodiment the invention relates to a method for assisting in the determining whether a sample is an infiltrating ductal carcinoma or a normal sample,

wherein the HER2 status is determined in a sample, and

wherein the methylation status is determined for at least LRRC4C, HSPA2, ROBO3, AF271776, DFNB31, PGD ((SEQ ID NO: 93, 94, 95, 100, 96, and 97).

Example 7 illustrates how these specific sequences were determined The above sequences had a Fold change (FC) of >1.25 with respect to Her2 status in IDCvsNormal experiments. Fold Change experiments measure the ratio of methylation levels between the case and control (Her2− against Her2+) that are outside of a given cutoff or threshold. The fold change value is the absolute ratio of normalized intensities between the average intensities of all the samples in each group.

From Example 7 it can be seen that SEQ ID NO 93 and 94 which are close to the genes: LRRC4C HSPA2 are likely to be more methylated in Her2+ compared to Her2− in IDC vs. normal differentially methylated samples, while SEQ ID NO 95, 100, 96, and 97 which are close to genes ROBO3, AF271776, DFNB31 and PGD are likely to be less methylated in an IDC sample than in a Normal sample when the sample is HER2+.

ER Status

Similar as for Her2, specific sequences are found to be particular relevant when the ER status is also known. Thus in yet an embodiment the invention relates to a method for assisting in determining whether a sample is an infiltrating ductal carcinoma or a normal sample,

wherein in the ER status is determined in a sample, and

wherein the methylation status is determined for at least LRRC4C, KIAA0776, NME6, SMG6, ABCB10, MMP25 and LNPEP (SEQ. ID NO: 93, 87, 88, 89, 90, 91 and 92).

Example 5 illustrates how these specific sequences were determined

The above list shows significant loci with fold change >2 in ER+ vs ER− samples of IDCvsNormal

From Example 5 it can be seen that SEQ ID NO 93, 87 (LRRC4C, KIAA0776) are likely to be more methylated in an IDC sample than in a Normal sample and that SEQ ID NO 88, 89, 90, 91 and 92 (NME6, SMG6, ABCB10, MMP25 and LNPEP) are likely to be less methylated in an IDC sample than in a Normal sample when the sample is ER+.

Menopausal Status

For classifying the samples according to the invention, the menopausal status of the subject from which the sample was obtained may be important. In addition DNA sequences which may be important for determining when the menopausal status is known may also be important. Thus in yet an embodiment the invention relates to a method, for assisting in the determining whether a sample is an infiltrating ductal carcinoma or a normal sample,

wherein in the menopausal status of said subject is determined, and

wherein the methylation status is determined for at least TMEM117, GALNT13, BDNF, and DUSP4 [SEQ ID NO 83, 84, 85, 86].

Example 3 illustrates how said sequences are determined

From Example 3 it can be seen that in IDC vs. normal samples SEQ ID NO 83, 84, and 85 TMEM117, GALNT13 BDNF are likely to be more methylated in postmenopausal sample and that SEQ ID NO 86 DUSP4 are more likely to be methylated in premenopausal sample.

Combination of ER Status, the PR Status and the HER2

Triple negatives and triple positives are clinically important parameters to judge the efficacy of treatment. Generally triple negatives have poor prognosis and very low survival rate. Again when such triple negatives or positives are determined the classification may be further determined by knowing specific relevant methylation patterns. Thus, in another embodiment the invention relates to a method for assisting in determining whether a sample is an infiltrating ductal carcinoma or a normal sample,

wherein the ER status, the PR status and the HER2 status is determined in a sample, and

wherein the methylation status is determined for LRRC4C, PVRL3, ROBO3, AF271776 SMG6, ABCB10, PVRL3, ROBO3, AF271776, SMG6, AF271776, ABCB10 (SEQ ID NO, 93, 98, 99, 100, 101, 102, 103, and 90). Example 8 illustrates significant loci (FC>1.5) in ER+/PR+/Her2+ against ER−/PR−/Her2− in IDCvsNormal experiments.

From Example 8 it can be seen that the SEQ ID NO 93 which is close to gene LRRC4C has shown higher methylation status in ER+, PR+, Her2+ patients compared to ER−, PR− Her2− samples while Seq ID NO 98, 95, 100, 89, 90 which is close to genes: PVRL3, ROBO3 AF271776, SMG6, and ABCB10 has shown higher methylation status in ER−, PR−, Her2− patients compared to ER+, PR+ Her2+ tumor vs normal samples.

Infiltrating Ductal Carcinoma or Benign Breast Cancer Tumor

The methods of the invention may also be used for determining whether a sample is a infiltrating ductal carcinoma or benign breast cancer tumor without the use of data on protein expressions. Thus, in an embodiment the invention relates to a method for assisting in the determining whether the sample is from a infiltrating ductal carcinoma or benign breast cancer tumor, wherein the methylation status is determined for at least IFT88, SLC13A3, IREB2, RTTN, KIAA1530, PSIP1, CR601508, BANK1, JAK2 (SEQ ID NO: 104, 105, 106, 107, 108, 109, 110, 111 and 112 respectively).

In example 1 and Table 4 T-test results IDC vs. benign with fold change above 1.5 is shown.

From Example 1 (table 4) it can be seen that SEQ ID NO 102, 105, 107, 110 and 111 corresponding to IFT88, IREB2, KIAA1530, BANK1, JAK2 are likely to be more methylated in an IDC sample than in a benign breast cancer tumor and that SEQ ID NO 104, 106, 108, 109 which correspond to SLC13A3, RTTN, PSIP1 and CR601508 are likely to be less methylated in an IDC sample than in a benign breast cancer tumor.

Invasive Ductal Carcinoma Vs. Normal

The methods of the invention may also be used for determining whether a sample is a infiltrating ductal carcinoma or normal without the use of data on protein expressions. Thus, in an embodiment the invention relates to a method for assisting in the determining whether a sample is an invasive ductal carcinoma or normal, wherein the methylation status is determined for at least ddb1 (SEQ ID NO: 4), DDB1 (SEQ ID NO: 44), DAP (SEQ ID NO:14), TBX3 (SEQ ID NO:29), LRP5 (SEQ ID NO:19) and PCGF2 (SEQ ID NO:24).

We consider five loci which may be very important in distinguishing invasive ductal carcinoma vs. normal: DDB1, DAP and TBX3 (hypermethylated) and LRP5 and PCGF2 (hypomethylated).

SEQ ID NO 4, 44, 14, 29 are likely to be more methylated in an IDC sample than in a normal sample and SEQ ID NO 19 and 24 are likely to be less methylated in an IDC sample than in a normal sample.

By using an even higher number of data points an even more reliable classification may be obtained. Thus, in yet a further embodiment the invention relates to a method for assisting in determining whether a sample is an invasive ductal carcinoma or a normal sample, wherein the methylation is determined for at least 10 sequences selected from the group consisting of: SEQ ID NO: 15 (DUS4L), 27 (SLC17A5), 21 (NR4A2), 20 (NCKIPSD), 57 (PARK2), 2 (CYP26A1), 44(DDB1), 58(PDE4DIP), 14(DAP), 29 (TBX3), 19 (LRP5), 16 (GULP1), 64 (TJP1), 25 (PDE6A), 67 (ZCSL2), 22 (NUP93), 12 (CR596143), 24 (PCGF2), 3 (SNRPF), 18 (L0051057), and 8 (C10orf11). SEQ ID NO. 27, 21, 20, 57, 2, 44, 53, 58, 23, 14, 1, 30, 5, 13, 68, 11, 28, 17, 62, 42, 36, 50, 35, 58, 59, 32, 29, 69, 38, 37, 49, 54, 31, 56, 40, 61, 48, 43, 46, 26, 41, 55, (corresponding to genes: DUS4L, SLC17A5, NR4A2, NCKIPSD, DKFZp7621137, CYP26A1, DDB1, LOC440925, PDE4DIP, OTX1, DAP, BDNF, TRUB2, AB032945, CYP39A1, ZDHHC20, CEP350, SMARCA2, HADHA, SYK, CHD2, ANKHD1, GADD45A, ALG2, PDE4DIP, POLI, ACBD3, TBX3, ZHX2, APOLD1, ANKMY2, FLYWCH1, MALT1, UCK2

NPY1R, BC040897, SIX3, FLRT2, CPEB1, FAM70B, RBPMS2, C6orf155 MORC2) are likely to be more methylated in an IDC sample than in a normal sample and SEQ ID NO 9, 34, 7, 51, 47, 63, 65, 66, 52, 19, 6, 33, 16, 64, 25, 67, 22, 12, 24, 3, 18, 8 (corresponding to genes: PSMB7, C1QTNF8, C17orf41, BC005991, GPR89A, FBXL10, TES, TNFRSF13B, TTC23, HAND2, LRP5, ASNSD1, ACSL3, GULP1, TJP1, PDE6A, ZCSL2, NUP93, CR596143, PCGF2, SNRPF, L0051057, C10orf11) are likely to be less methylated in an IDC sample than in a normal sample.

Pathways

Thus, in yet an embodiment the invention relates to a method for assisting in determining whether a sample is an invasive ductal carcinoma or a normal sample, wherein the methylation status is determined for at least PCNA, CCND1 MAPK1, SYK (SEQ ID NO 71, 72, 73, 74, 62), BCL2L1, ERBB4 and PARK2 (SEQ ID NO 73,78,79-82, 57), ETS1 and AHR (SEQ ID NO: 75, 76).

SEQ ID NO 73, 74, 62, 57, 78 are likely to be more methylated in an IDC sample than in a normal sample and SEQ ID NO 71, 72, 75, 76, 79, 80, 81, 82 are likely to be less methylated in an IDC sample than in a normal sample.

Determination of Methylation Status

The methylation status of a sample may be determined by different means. Thus, in an embodiment the methylation status is determined by means of one or more of the methods selected form the group of,

a. bisulfite sequencing

b. pyrosequencing

c. methylation-sensitive single-strand conformation analysis(MS-SSCA)

d. high resolution melting analysis (HRM)

e. methylation-sensitive single nucleotide primer extension (MS-SnuPE)

f. base-specific cleavage/MALDI-TOF

g. methylation-specific PCR (MSP)

h. microarray-based methods and

i. msp I cleavage.

j. Methylation sensitive sequencing

In addition to the described method in our patent disclosure, there is a variety of methods for determining the methylation status of a DNA molecule. It is preferred that the methylation status is determined by means of one or more of the methods selected form the group of, 10arkinson sequencing, methylation-sensitive single-strand conformation analysis(MS-SSCA), high resolution melting analysis (HRM), methylation-sensitive single nucleotide primer extension (MS-SnuPE), base-specific cleavage/MALDI-TOF, methylation-specific PCR (MSP), methyl-binding protein immunoprecipitation, microarray-based methods, enzymatic assays involving McrBc and other enzymes such as Msp I. An overview of the known methods of detecting 5-methylcytosine may be found from the following review paper: Rein, T., DePamphilis, M. L., Zorbas, H., Nucleic Acids Res. 1998, 26, 2255. Further methods are disclosed in US 2006/0292564A1.

Sample Type

The samples according to the invention may be obtained from different types of sample material. Thus, in an embodiment the sample to be analyzed is from a tissue type selected from the group of tissues such as, a tissue biopsy from the tissue to be analyzed, tumor tissue, body fluids, blood, serum, saliva and urine. In a specific embodiment the sample is tissue biopsy such as a breast tissue biopsy. In another embodiment the sample is provided from a human, more specifically the subject is a female.

Prediction of the Therapeutic Response

The methods according to the invention may also be used for evaluate the efficiency of a treatment. Thus in an embodiment the methylation pattern obtained, is used to predict the therapeutic response to the treatment of a breast cancer. This may be done by measuring the methylation pattern before or after a treatment is initiated or during a treatment. Thus, it may be possible to determine whether the subject receives correct treatment.

Composition or Array

The present invention also relates to composition or arrays comprising 10 or more sequences according to the invention. Thus, in an aspect the invention relates to a composition or array comprising nucleic acids with sequences which are identical to at least 10 of the sequences according to SEQ ID NO: 1-111. Similar, in an embodiment the invention relates to a composition or arrays comprising nucleic acids with sequences which are identical to at least 20, such as at least 40 such as at least 60 of the sequences according to SEQ ID NO: 1-111.

It is of course also to be understood that the composition or array may comprise at least one or more of the specific subset of sequences listed in tables and claims.

In another embodiment the invention relates to a composition or array, comprising nucleic acids with sequences which are identical to ddb1 (SEQ ID NO:4), DDB 1 (SEQ ID NO 44), DAP (SEQ ID NO:14), TBX3 (SEQ ID NO:29), LRP5 (SEQ ID NO:19) and PCGF2 (SEQ ID NO:24).

Computer Program

The methods according to the invention may also be performed by a computer program. Thus, in an aspect the invention relates to a computer program product being adapted to enable a computer system comprising at least one computer having a data storage means associated therewith to operate a processor arranged for carrying out a method according to the invention.

EXAMPLES
Example 1
Description of the CpG Island Arrays

The CpG arrays used in our experiments are special ordered arrays, offered by University Health Network Microarray centre, Toronto, Canada. Each array consists of 12192 spotted clones. All clones were sequenced originally at Sanger, with further verification performed at the British Columbia Genome Sciences Centre and internally at the UHN Microarray Centre. The library was made by cutting genomic DNA with Msel enzyme, which cuts at AATT points. Methylated fragments, i.e. those that are not being protected and therefore probably not a CpG island, are then pulled out on a column and discarded. The remaining fragments are artificially methylated and then this is run through a column which pulls out those methylated fragments which represent CpG islands. These DNA segments are then cloned into vectors, grown on plates, picked, amplified and spotted onto the array.

Here is a summary of the clones on the array: there is an annotation file Cpgdump which provides information such as the genomic location of each clone, its sequence, overlapping transcript IDs, nearest upstream and downstream transcript IDs and so forth

- No. of Clones for which Sequence is present: 11539
- No. of clones with Forward sequence—10216
- No. of clones with Reverse Sequence—10458
- Number of clones that are associated with a gene: 5530. This means that the clone is either in the promoter region of a gene (less than a 2000 base pairs of a transcription start site), within the boundaries of a gene, or up to 2000 bases downstream of the 3′ end of the gene.
- Max. length of Sequence—991
- Average Length of Sequence—326.19

Experimental Protocol for Array Hybridization

At the time of surgery one sample of fresh tissue and another in 10% formalin were collected. Fresh frozen tissue is used for subsequent DNA extraction and hybridization experiments. The sample collected in 10% formalin is processed to make a formalin fixed paraffin embedded block for histopathological and hormone receptor studies. Slides from these blocks were stained with Hematoxylin & Eosin and reviewed by pathologists for classification and grading of tumors. Immumunohistochemistry for ER, PR, HER2, was done on each set of formalin-fixed, paraffin-embedded tissue slides using the primary antibodies from DAKO and secondary as Envision™ method with 3, 3diaminobenzidine chromogen. Biomarker expression from immunohistochemical assays were scored independently by two pathologists, using previously established scoring methods. ER and PR stains were considered positive if immune-staining was seen in >1% of tumor nuclei. For HER2 status, tumors were considered positive if scored as 3+ according to HercepTest™ criteria.

The following steps are performed by the hybridization protocol:

1. Collect Sample

2. Extract DNA (24 hrs)

3. Check for Concentration and quality (4 hrs)

4. Digest with Msel (16 hrs)

5. Purify and Precipitate (24 hrs)

6. Check Concentration (4 hrs)

7. Anneal Primers (14 hrs)

8. Ligate to DNA (24 hrs)

9. Perform PCRs (qualitative and quantitative (24 to 7 hrs)

10. Purify DNA (24 hrs)

11. Label with Dyes (24 hrs)

12. Check for labelling (2 hrs)

13. Purify DNA and quantify (24 hrs)

14. Hybridize to Chips

Clinical Data Description

The prospective study cohort consists of 51 female primary breast cancers. All patients had been undergoing treatment in a tertiary care hospital and its associated centres in Southern part of India between 2007 and 2009. Information pertaining to age, menopausal status, staging, histopathological type, hormonal receptor status of the patients was collected after patient consent and ethical committee approval. Limited follow-up data was available considering the first sample collection was only 2 years ago and extrapolating this information to outcomes is not justified. The study cohort underwent mastectomy with or without chemo and radio therapy.

The description of the clinical data being used is given in Table 1. The data classification has been derived after extensive discussions with multiple clinical experts. The two major categories in this sample set were IDC vs Normal and IDC vs Benign with 29 and 16 samples respectively in each category. The other categories had fewer samples and were not included for further analysis. The type of experiments for which further analysis was conducted is: infiltrating ductal carcinoma (IDC) vs. Normal and infiltrating ductal carcinoma (IDC) vs. benign condition.

In the present context “infiltrating ductal carcinoma (IDC) vs. Normal” refers to a ratio between the differential methylation status of genes present among the infiltrating ductal carcinoma (IDC) samples as well as the normal samples. Similar, in the present context the term “infiltrating ductal carcinoma (IDC) vs. benign condition” is to be understood as the differentially methylated genes among IDC samples and benign tumor samples. This comparison is of importance as the benign tumor samples are seen as being potentially premalignant.

TABLE 1

Clinical sample classification used in the data analysis.

Menopausal

ER+
ER−

status
Onset
PR+
PR−
Size

Category
Total
Pre
Post
NA
Early
Mid
Late
Her2+
Her2−
<5 cm
>5 cm

IDC vs
29
9
10
10
9
9
11
11
5
8
21

Normal

IDC vs
16
4
0
12
2
14
0
5
4
5
8

Benign

Data Analysis of Carcinoma, Normal and Benign Conditions

The experiments were conducted as paired samples of normal samples with cancer samples. As far as possible adjacent normal of the cancer sample was used. Some cases benign tumors were paired with malignant samples. Benign tumors included fibroadenoma, fibrocystic disease, adenosis and phyllodes tumour.

After the hybridization step, the microarray chips are scanned and the intensity values across the chip recorded. The proprietary feature extraction software from Agilent executes the basic image processing algorithms to quantify the intensity values at each spot while correcting for the background noise. At the end of this process, a QC report is prepared and a matrix of raw values is exported which includes the raw and minimally normalized intensity values for each gene/locus in the array.

The first step in data analysis is to carry out further normalization of the matrix data to account for intra-array and inter-array experimental deviations. The raw values at each matrix are normalized to an upper limit of 1.0 over a log scale and normalized using LOWESS (locally weighted scatter plot smoothing) method.

Pre-Processing Based on Carcinoma Subtype Classification

I. All 45 ductal carcinoma arrays were normalized prior to determining the differential gene expression between normal and ductal carcinoma samples using LOWESS method.

II. Interarray normalization is performed in several different methods: baseline to median (in GeneSpring GX 10), normalize mean to zero, and quantile normalization (in R/Bioconductor).

III. Correlation assessment among all the experiments is then computed to get a picture of the similarity in the array data among the samples in the set.

We used R/Bioconductor and GeneSpring v10 for statistical analysis of the breast cancer data.

IDC Vs. Normal Statistical Analysis with Outer Loop Validation

We also performed analysis using only the promoter probes (modified files) which gives 71 significant loci in total. Here is a table with all the probes that actually have “survived” the following steps:

- 1. The raw matrix is taken from the corrected signal where features are extracted (normalized) using only 5530 probes—not all probes.
- 2. Further, the obtained microarray data is preprocessed with Lowess intra-array normalization
- 3. Quantile inter-array normalization is performed on MA matrix. For further processing M is used. (log ratio)
- 4. Fold change is greater than 0.7 (or less than −0.7) in at least 14 out of the 29 IDC vs. normal samples
- 5. The p-value is less than 0.05 in a leave one out procedure (29 repeats where one sample is left out from the t-test). The final result table has 71 UHN ids (with gene symbols included).
- 6. With the adjusted p-values obtained from the Bayesian statistical analysis also in a leave one out fashion, we exclude 7 probes, which leave 64 probes as the final result.

Results are shown in Table 3. It is important to note that these loci are obtained with a leave one out validation and should be more stable and less sensitive to noise. The p-values shown in the table are obtained using all samples. Also, due to the Quantile normalization, the values of around 1 should be considered extremely high. In Table 15, we present the most significant of these loci with SEQ ID: 15, 27, 21, 20, 57, 2, 44, 58, 14, 29, 19, 16, 64, 25, 67, 22, 12, 24, 3, 18, and 8, which correspond to genes: DUS4L, SLC17A5, NR4A2, NCKIPSD, PARK2, CYP26A1, DDB1, PDE4DIP, DAP, TBX3, LRP5, GULP1, TJP1, PDE6A, ZCSL2, NUP93, CR596143, PCGF2.

TABLE 3

Results of IDC vs. normal t-testing from a leave one out validation

loop.

SEQ ID

Adjusted

NO
ID
Gene symbol
p-value
Mean

68
UHNhscpg0007132
ZDHHC20
4.87E−05
0.822711

1
UHNhscpg0003204
BDNF
4.87E−05
0.87014

21
UHNhscpg0006767
NR4A2
6.90E−05
1.033697

20
UHNhscpg0009447
NCKIPSD
0.000101
1.011746

57
UHNhscpg0008659
PARK2
0.00015
1.002518

14
UHNhscpg0005129
DAP
0.0002
0.881149

36
UHNhscpg0003749
ANKHD1
0.000238
0.797185

32
UHNhscpg0006074
ACBD3
0.000292
0.759773

53
UHNhscpg0010276
LOC440925
0.000335
0.927716

8
UHNhscpg0005168
C10orf11
0.000403
−1.11219

15
UHNhscpg0004955
DUS4L
0.000462
1.202454

11
UHNhscpg0007121
CEP350
0.000496
0.822555

38
UHNhscpg0001556
APOLD1
0.000516
0.749436

58
UHNhscpg0007517
PDE4DIP
0.000528
0.905226

62
UHNhscpg0004894
SYK
0.00053
0.810273

2
UHNhscpg0000746
CYP26A1
0.000555
0.934528

70
UHNhscpg0003020
DKFZp762I137
0.000555
0.946523

27
UHNhscpg0006718
SLC17A5
0.000693
1.076886

49
UHNhscpg0007607
FLYWCH1
0.000796
0.742613

40
UHNhscpg0006298
BC040897
0.000915
0.683741

29
UHNhscpg0006737
TBX3
0.001042
0.754758

17
UHNhscpg0011146
HADHA
0.001147
0.810381

44
UHNhscpg0008660
DDB1
0.001158
0.928127

50
UHNhscpg0007178
GADD45A
0.001258
0.79172

13
UHNhscpg0007485
CYP39A1
0.001296
0.850419

23
UHNhscpg0002087
OTX1
0.001316
0.889817

5
UHNhscpg0007521
AB032945
0.001624
0.856789

59
UHNhscpg0007487
POLI
0.001624
0.770442

35
UHNhscpg0008517
ALG2
0.001708
0.785926

10
UHNhscpg0007200
FLJ10996
0.001999
0.771389

31
UHNhscpg0008746
UCK2
0.001999
0.714308

6
UHNhscpg0005119
ASNSD1
0.002328
−0.6714

9
UHNhscpg0003195
C1QTNF8
0.002422
−0.5403

43
UHNhscpg0007469
CPEB1
0.002422
0.637375

16
UHNhscpg0000358
GULP1
0.002478
−0.7189

67
UHNhscpg0000299
ZCSL2
0.002814
−0.84025

22
UHNhscpg0000109
NUP93
0.002828
−0.87988

69
UHNhscpg0007446
ZHX2
0.003114
0.750184

42
UHNhscpg0009610
CHD2
0.003212
0.800779

60
UHNhscpg0009180
PSMB7
0.003593
−0.43153

3
UHNhscpg0000390
SNRPF
0.00439
−1.00775

37
UHNhscpg0001513
ANKMY2
0.004468
0.743584

58
UHNhscpg0007602
PDE4DIP
0.00455
0.777924

41
UHNhscpg0006075
C6orf155
0.005387
0.505702

4
UHNhscpg0003291
SULF1
0.005914
0.684412

18
UHNhscpg0000591
LOC51057
0.006152
−1.02894

28
UHNhscpg0007553
SMARCA2
0.006152
0.814892

54
UHNhscpg0005089
MALT1
0.006747
0.729116

61
UHNhscpg0003180
SIX3
0.006956
0.666075

12
UHNhscpg0000322
CR596143
0.007368
−0.93453

30
UHNhscpg0005296
TRUB2
0.008113
0.857046

56
UHNhscpg0007104
NPY1R
0.010879
0.70281

19
UHNhscpg0000038
LRP5
0.013234
−0.66959

24
UHNhscpg0000193
PCGF2
0.015044
−0.99558

26
UHNhscpg0004952
RBPMS2
0.016904
0.519043

45
UHNhscpg0007159
MGC23280
0.018887
0.765995

34
UHNhscpg0000043
AKT1S1
0.021285
−0.63249

63
UHNhscpg0000364
TES
0.021557
−0.64469

51
UHNhscpg0000037
GPR89A
0.025007
−0.64381

48
UHNhscpg0000429
FLRT2
0.027045
0.642276

25
UHNhscpg0005166
PDE6A
0.028382
−0.74392

55
UHNhscpg0007662
MORC2
0.033752
0.487627

46
UHNhscpg0000452
FAM70B
0.043458
0.565759

7
UHNhscpg0005159
BC005991
0.048081
−0.64101

IDC Vs. Benign Statistical Analysis

Using GeneSpring 10, we performed T-test against zero-mean hypothesis on the IDC vs. benign experiments. We used total of 16 experiments and performed t-test without multiple testing correction and obtained 160 significant loci. Out of that, we have 155 entities with fold change greater or equal to 1.1. The significant differentially methylation loci between IDC vs. benign are shown in Table 4. Volcano plot is shown in FIG. 4. Differentially methylated sequences are close to genes: IFT88, SLC13A3, IREB2, RTTN, KIAA1530, PSIP1, CR601508, BANK1, JAK2 (SEQ ID NO: 103, 104, 105, 106, 107, 108, 109, 110, 111 respectively). The sequences 102, 105, 107, 110 and 111 corresponding to IFT88, IREB2, KIAA1530, BANK1, JAK2 are methylated more in IDC than in benign tumor while sequence numbers: 104, 106, 108, 109 which correspond to SLC13A3, RTTN, PSIP1 and CR601508 are methylated more in benign than in IDC samples.

TABLE 4

T-test results IDC vs. benign with fold change above 1.5.

SEQ

ID

Fold

Gene

NO
UHNID
Change
Change
symbol
Description

103
UHNhscpg0007777
1.5708911
up
IFT88
intraflagellar transport 88

homolog isoform 1

104
UHNhscpg0000501
1.5785927
down
SLC13A3
solute carrier family 13

member 3 isoform a

105
UHNhscpg0007046
1.8579512
up
IREB2
Iron responsive element

binding protein 2

106
UHNhscpg0008329
1.5022352
down
RTTN
rotatin

107
UHNhscpg0000211
1.5032853
up
KIAA1530
KIAA1530 protein

108
UHNhscpg0002300
1.5540606
down
PSIP1
PC4 and SFRS1

interacting protein 1

isoform 2

109
UHNhscpg0004523
1.5321043
down
CR601508
OTTHUMP00000016614.

110
UHNhscpg0009237
1.6035372
up
BANK1
Hypothetical protein

FLJ34204.

111
UHNhscpg0006618
1.5664941
Up
JAK2
Janus kinase 2

Example 2
Data Analysis Using Clinical Parameters

It is very important for clinical decision making to more accurately decide if a patient has differentially methylated loci that correspond more to the IDC vs. normal based on the menopausal status or based on the onset of the disease which could be early or late.

- I. Out of 29 samples of infiltrating ductal carcinoma that were matched with normals for experimentation, 9 were found to be in premenopausal women and 10 were in post-menopausal women.
- II. The two sub groups were defined as a particular interpretation. All entities that passed the student's t test with a confidence of 99.95% were first selected.
- III. Fold Change Analysis is used to identify genes with expression ratios or differences between a treatment and a control that are outside of a given cut-off or threshold. Fold change gives the absolute ratio of normalized intensities (no log scale) between the average intensities of the samples grouped. The results were filtered on fold change >=1.75 and >=2.
- IV. The data was also filtered by expression. In this process, all entities that satisfy the top 30 percentile in the normalized data in majority of the samples are selected and verified.

Example 3
Menopause Status Based Classification

- I. 109 out of 5530 entities were found to be significant when passed through the student t-test (unpaired, asymptotic, no correction).
- II. Following fold change on Post vs. Pre Menopausal status of all entities, 4 entities loci were found to be significantly differentiated with a fold change of >=1.3
- III. The most significant UHN loci were picked by passing them through a filter for expression of the loci in the top 10 percentile of the data in majority of the samples.

TABLE 6

List of genes with significant changes in methylation between post

menopausal vs. premenopausal tumor patients.

SEQ

ID

Gene

NO
UHNID
Fold Change
Change
Description
symbol

83
UHNhscpg0007411
1.3591343
up
hypothetical protein
TMEM117

LOC84216

84
UHNhscpg0008515
1.3944643
up
UDP-N-acetyl-alpha-D-
GALNT13

galactosamine:polypeptide

85
UHNhscpg0008264
1.4317298
up
brain-derived neurotrophic
BDNF

factor isoform b

86
UHNhscpg0002632
1.6052125
down
dual specificity phosphatase
DUSP4

4 isoform 1

In FIG. 11 Clustering on IDCvsNormal samples after t-test post vs. premenopausal status, p-value cut off <=0.05.

FIG. 7: Fold change of 4 loci between post and pre menopausal cases with a fold change >1.3.

As can be seen from the FIG. 7, SEQ ID NO 83, 84, 85 TMEM117, GALNT13 BDNF and are likely to be more methylated in postmenopausal sample and that SEQ ID NO DUSP4 is more likely to be methylated in premenopausal sample when the methylation status of tumor vs. normal is examined.

Example 4
Estrogen Receptor (ER), Progesterone Receptor (PR) and Herceptin (Her2)

Another important set of parameters to consider while screening for differentiators between tumor and normal is the Hormone receptors status. We analysed the presence or absence of Estrogen Receptor (ER), Progesterone Receptor (PR) and Herceptin (Her2) in all the tumor samples. The experiments were classified based on the status of these three parameters and the significant differences in these tumor types were noted.

TABLE 7

Categories of Hormone receptor status

ER
PR
Her2
ER/PR/Her2

Positive
19
16
17
11

Negative
8
11
10
5

Fold change analysis and clustering was done on the above categories using the significant entities within IDCvsNormal (p<0.05) as the input data set.

Example 5
ER Status Based Classification

a. 72 out of 5053 entities were found to be significant when passed through the student t-test for IDCvsNormal (unpaired, asymptotic, no correction).

b. Fold change on ER+ vs ER− status samples classified based on clinical data from patients into ER+ vs. ER− ve for all entities resulted in 6 entities loci which were significantly differentiated with a difference of >=2.0 (listed in table 8)

c. The most significant UHN loci were picked by passing them through a filter for expression of the loci in the top 10 percentile of the data in majority of the samples.

d. Clustering analysis was also done on the significant loci to look for patterns of hyper/hypo methylation across the samples. The results are displayed in FIG. 9

FIG. 8: Fold change of between ER+ against ER− samples

TABLE 8

Significant loci with fold change >2 in ER+ vs ER− samples of

IDC vs Normal

SEQ
UHNhscpg0000636
down
Netrin-G1 ligand

ID NO 93

87
UHNhscpg0006957
down
hypothetical protein LOC23376

88
UHNhscpg0008950
up
“non-metastatic cells 6, protein

expressed in (nucleoside-

diphosphate kinase)”

89
UHNhscpg0000024
up
Est1p-like protein A

90
UHNhscpg0010841
up
“ATP-binding cassette,

sub-family B, member 10”

91
UHNhscpg0010601
up
matrix metalloproteinase 25

preproprotein

92
UHNhscpg0011399
up
leucyl/cystinyl aminopeptidase

isoform 1

SEQ ID NO 93 and 87 (LRRC4C and KIAA0776) have higher methylation in ER+ when compared to ER− samples when IDC is compared to normal sample, while SEQ ID NO 88, 89, 90, 91 and 92 have higher methylation status in ER− compared to ER+ samples.

Example 6
PR Status Based Classification

- a. Fold change on PR+ vs PR− ve [samples classified based on clinical data from patients into] status of all entities resulted in 13 entities loci which were significantly differentiated with a difference of >=2.0 (listed in table 9).
- b. The most significant UHN loci were picked by passing them through a filter for expression of the loci in the top 10 percentile of the data in majority of the samples.
- c. Clustering analysis reveals the presence of two main classes of groups as shown in FIG. 11.

FIG. 10: Fold change of between PR− against PR+ samples

TABLE 9

Significant loci with fold change >2.0 with respect to PR+ against PR−

in IDCvsNormal experiments

SEQ ID NO
UHNhscpg0004504
down
Glyceraldehyde-3-phosphate

999

dehydrogenase(EC1.2.1.12)

(Fragment).

93
UHNhscpg0000636
down
netrin-G1 ligand

102
UHNhscpg0000230
up
distal-less homeobox 6

98
UHNhscpg0004672
up
PVRL3 protein.

87
UHNhscpg0006957
down
hypothetical protein

LOC23376

95
UHNhscpg0001461,
up
“roundabout, axon guidance

UHNhscpg0001274

receptor, homolog 3”

100
UHNhscpg0000914,
up
ATP synthase a chain

UHNhscpg0002255,

(EC 3.6.3.14) (ATPase

UHNhscpg0002136,

protein 6).

UHNhscpg0002944

89
UHNhscpg0000024
up
Est1p-like protein A

96
UHNhscpg0005839
up
OTTHUMP00000021976.

That SEQ ID NO 99, 93, 87, GAPDH and LRRC4C, KIAA0776 are methylated more in PR+ and SEQ ID NO 102, 98, 95, 100, 89, 96 DLX6, PVRL3, ROBO3, AF271776, SMG6, DFNB31, are methylated more in PR− in differentially methylated tumor vs. Normal samples.

Example 7
Her2 Status Based Classification

Fold change on Her2+ vs. Her2− [samples classified based on clinical data from patients into Her2+ and Her2− status of all entities resulted in 6 entities loci which were significantly differentiated with a difference of >=1.25 (listed in table 10).

TABLE 10

Fold change of >1.25 with respect to Her2 status in IDCvsNormal

experiments

SEQ ID NO
UHNhscpg0000636
down
netrin-G1 ligand

93

94
UHNhscpg0007219
down
heat shock 70 kDa protein 2

95
UHNhscpg0001461
Up
“roundabout, axon guidance

receptor, homolog 3”

100
UHNhscpg0000914
Up
ATP synthase a chain

(EC 3.6.3.14) (ATPase

protein 6).

96
UHNhscpg0005839
Up
OTTHUMP00000021976.

97
UHNhscpg0010619
Up
phosphogluconate

dehydrogenase

The plot in FIG. 6 shows that the overall ratio of the methylation status changes between IDC and Normal for the above six sequences with respect to the HER2 status.

In conclusion what can be seen in table 10 and FIG. 6 is that for the respective loci: SEQ ID NO 93 and 94 which are close to the genes: LRRC4C HSPA2 is higher in Her2+ compared to Her2− tumor vs. normal differentially methylated samples while SEQ ID NO 95, 100, 96, and 97 which are close to genes ROBO3, AF271776, DFNB31, and PGD methylation is higher in Her2− samples compared to Her2+.

Example 8
ER/PR/Her2 Status Based Classification

Triple negatives and triple positives are clinically important parameters to judge the efficacy of treatment. Generally triple negatives have poor prognosis and very low survival rate.

- I. Fold change on ER, PR, Her2, samples classified based on clinical data from patients into ER+/PR+/Her2+ against ER−/PR−/Her2− status of all entities resulted in 8 entities loci which were significantly differentiated with a difference of >=1.5 (listed in table 11)
- II. The most significant UHN loci were picked by passing them through a filter for expression of the loci in the top 10 percentile of the data in majority of the samples.
- III. Clustering of the loci with respect to triple positives against triple negatives yielded three clearly distinguishable clusters of genes (FIG. 14).

FIG. 13: Fold change of between ER−/PR−/Her2− against ER+/PR+/Her2+ samples.

TABLE 11

Significant loci (FC > 1.5) in ER+/PR+/Her2+ against ER−/PR−/Her2−

in IDCvsNormal experiments.

SEQ ID NO
UHNhscpg0000636
down
netrin-G1 ligand

93

98
UHNhscpg0004672
up
PVRL3 protein.

95
UHNhscpg0001274
up
“roundabout, axon guidance

receptor, homolog 3”

100
UHNhscpg0000914,
up
ATP synthase a chain

UHNhscpg0002255,

(EC 3.6.3.14) (ATPase

UHNhscpg0002136

protein 6).

89
UHNhscpg0000024
up
Est1p-like protein A

90
UHNhscpg0010847
up
“ATP-binding cassette,

sub-family B, member 10”

The SEQ ID NO 93 which is close to gene LRRC4C has shown higher methylation status in ER+, PR+, Her2+ patients compared to ER−, PR− Her2− samples. Whereas SEQ ID NO 98 95 100 89 90 which is close to genes: PVRL3, ROBO3, AF271776 SMG6, ABCB10 has shown higher methylation status in ER−, PR−, Her2− patients compared to ER+, PR+Her2+ tumor vs normal samples.

Example 9
Onset

The methylation patterns at the onset of breast cancer can be used to differentiate between groups of women who would respond to therapy differently. The significant loci were screened for strong differentiators with respect to methylation levels between a set of samples from early onset patients (<40) and a set of samples for late onset patients (>50). 24 entities had a fold change of >1.3 (FIG. 12). Clustering analysis was also conducted with respect to this classification (FIG. 13).

Example 10
Important Pathways in Breast Cancer

We also conducted analysis to detect significant pathways using only the promoter probes (modified files) based on the 312 significant loci in total. As input, we use a table with all the probes that actually have survived the following the following steps:

- 1. The raw matrix is taken from the corrected signal where features are extracted (normalized) using only 5530 probes—not all probes.
- 2. Further, the obtained microarray data is pre-processed with Lowess intra-array normalization.
- 3. Quantile inter-array normalization is performed on MA matrix. For further processing M is used. (log ratio).
- 4. Fold change is greater than 0.7 (or less than −0.7) in at least 10 out of the 29 IDC vs. normal samples.
- 5. The p-value is less than 0.05 in a leave one out procedure (29 repeats where one sample is left out from the t-test). The final result table has 312 UHN ids.

These candidate loci serve as input to the pathway analysis module in GeneSpring 10. We present the results of this analysis showing PCNA, CCND1 MAPK1, SYK as the key modifiers in our dataset FIG. 14. In FIG. 15 we show CCND1, BCL2L1, ERBB4 and PARK2 as being important hubs in the network of key regulators and targets. In FIG. 16 we see additional transcription regulators prominently showing ETS1 and AHR as being active in our sample set.

We should note that all these views can be made available in a clinical study to a clinical scientist as well as to a clinician practitioner to make an assessment of the levels of these genes in these networks so that he/she can make further decisions about the therapy plan for the patient.

TABLE 15

Sequences important in pathway analysis

Gene

Seq ID
ID
Symbol
State
FC
Mean

71
UHNhscpg0000434
PCNA
down
−0.072
8.319

72
UHNhscpg0005318
PCNA
down
−0.75932
7.092748

73
UHNhscpg0005042
CCND1
up
0.513348
7.585013

74
UHNhscpg0007998
MAPK1
up
0.116532
7.999638

62
UHNhscpg0004894
SYK
up
0.810273
7.966379

57
UHNhscpg0008659
PARK2
up
1.002518
8.169452

75
UHNhscpg0000233
ETS1
down
−0.57184
8.788014

76
UHNhscpg0005090
AHR
down
−0.45214
8.273254

79
UHNhscpg0004815
ERBB4
down
−0.08746
8.51624

80
UHNhscpg0005000
ERBB4
down
−0.36086
8.728778

81
UHNhscpg0007314
ERBB4
down
−0.02541
8.036166

82
UHNhscpg0002306
ERBB4
down
−0.0647
8.92377

78
UHNhscpg0005109
BCL2L1
up
0.455158
7.859656

We present a list of these important pathway regulators in Table 15, where we include the fold change between IDC vs. normal and the mean value for each respective probe (ID) covering a CpG island near its respective gene. For example, SEQ ID NO 71, 72, 75, 76, 79, 80, 81, 82 which are near genes: ETS1, AHR, ERBB4 are less methylated in normal when compared to IDC (tumor), while SEQ ID NO 73, 74, 62, 57, 78 which are near genes CCND1, MAPK1, SYK, PARK2, BCL2L1 are methylated more in normal when compared to IDC (tumor).

Applications of the Invention

The methylation status of these genes may be used for assisting in classifying infiltrating ductal carcinomas and potentially classifying them depending on their predicted prognosis.

Complete sequence list with data and SEQ ID NO's

SEQ

ID

GENE
CHROMOSOME

NO
UHNID
SYMBOL
LOCATION
STRAND
DESCRIPTION

1
UHNhscpg0003204
BDNF
chr11: 27696550-27696943
−
brain-derived

neurotrophic factor

2
UHNhscpg0000746
CYP26A1
chr10: 94823545-94824498
+
cytochrome p450,

family 26,

subfamily a,

polypeptide 1

3
UHNhscpg0000390
SNRPF
chr12: 94777118-94777283
+
small nuclear

ribonucleoprotein

polypeptide f

4
UHNhscpg0003291
ddb1
chr8: 70681084-70681132
+
sulfatase 1

5
UHNhscpg0007521
AB032945
chr18: 45975419-45975817

hypothetical genes

6
UHNhscpg0005119
ASNSD1
chr2: 190234117-190234855
+
asparagine

synthetase domain

containing 1

7
UHNhscpg0005159
BC005991
chr6: 100069473-100070296
−
ubiquitin specific

peptidase 45

8
UHNhscpg0005168
C10orf11
chr10: 77556552-77556940
+
chromosome 10

open reading frame

11

9
UHNhscpg0003195
C1QTNF8
chr16: 1078385-1078623
−
c1q and tumor

necrosis factor

related protein 8

10
UHNhscpg0007200
CCDC93
chr2: 118488594-118488880

coiled coil domain

containing 93

11
UHNhscpg0007121
CEP350
chr1: 178190354-178191398
+
centrosomal

protein 350 kda

12
UHNhscpg0000322
CR596143
chr13: 47472800-47473674
−
succinate-CoA

ligase, ADP-

forming, beta

subunit

13
UHNhscpg0007485
CYP39A1
chr6: 46728050-46729246
−
cytochrome p450,

family 39,

subfamily a,

polypeptide 1

14
UHNhscpg0005129
DAP
chr5: 10814631-10814861

death-associated

protein

15
UHNhscpg0004955
DUS4L
chr7: 107007599-107008461
+
dihydrouridine

synthase 4-like (s. cerevisiae)

16
UHNhscpg0000358
GULP1
chr2: 189015381-189015526
+
gulp, engulfment

adaptor ptb domain

containing 1

17
UHNhscpg0011146
HADHA
chr2: 26321685-26321954
+
hydroxyacyl-

coenzyme a

dehydrogenase/3-

ketoacyl-coenzyme

a thiolase/enoyl-

coenzyme a

hydratase

(trifunctional

protein), alpha

subunit

18
UHNhscpg0000591
LOC51057
chr2: 63269457-63269746
−
hypothetical

protein loc51057

19
UHNhscpg0000038
LRP5
chr11: 67836747-67837638
+
low density

lipoprotein

receptor-related

protein 5

20
UHNhscpg0009447
NCKIPSD
chr3: 48697708-48698578
−
nck interacting

protein with sh3

domain

21
UHNhscpg0006767
NR4A2
chr2: 156896978-156897265
−
nuclear receptor

subfamily 4, group

a, member 2

22
UHNhscpg0000109
NUP93
chr16: 55413184-55413324
+
nucleoporin 93 kda

23
UHNhscpg0002087
OTX1
chr2: 63139415-63140244

orthodenticle

homolog 1

(drosophila)

24
UHNhscpg0000193
PCGF2
chr17: 34157389-34157723
−
polycomb group

ring finger 2

25
UHNhscpg0005166
PDE6A
chr5: 149248278-149248379
−
phosphodiesterase

6a, cgmp-specific,

rod, alpha

26
UHNhscpg0004952
RBPMS2
chr15: 62855175-62855414

rna binding protein

with multiple

splicing 2

27
UHNhscpg0006718
SLC17A5
chr6: 74420105-74420758
−
solute carrier

family 17

(anion/sugar

transporter),

member 5

28
UHNhscpg0007553
SMARCA2
chr9: 2004804-2005843
+
swi/snf related,

matrix associated,

actin dependent

regulator of

chromatin,

subfamily a,

member 2

29
UHNhscpg0006737
TBX3
chr12: 113591376-113592025

t-box 3 (ulnar

mammary

syndrome)

30
UHNhscpg0005296
TRUB2
chr9: 130124151-130125468
−
trub pseudouridine

(psi) synthase

homolog 2 (e. coli)

31
UHNhscpg0008746
UCK2
chr1: 164064063-164064435
+
uridine-cytidine

kinase 2

32
UHNhscpg0006074
ACBD3
chr1: 224441249-224441525

acyl-coenzyme a

binding domain

containing 3

33
UHNhscpg0007805
ACSL3
chr2: 223506688-223507101
+
acyl-CoA

synthetase long-

chain family

member 3

34
UHNhscpg0000043
AKT1S1
chr19: 55071651-55072027
−
akt1 substrate 1

(proline-rich)

35
UHNhscpg0008517
ALG2
chr9: 101024654-101024883
+
asparagine-linked

glycosylation 2

homolog (yeast,

alpha-1,3-

mannosyltransferase)

36
UHNhscpg0003749
ANKHD1
chr5: 139760854-139761285

ankyrin repeat and

kh domain

containing 1

37
UHNhscpg0001513
ANKMY2
chr7: 16651378-16651766
−
ankyrin repeat and

mynd domain

containing 2

38
UHNhscpg0001556
APOLD1
chr12: 12830839-12832152
+
apolipoprotein 1

domain containing 1

39
UHNhscpg0000419
ATAD5
chr17: 26182896-26183794
+
chrom17 origin of

replication

40
UHNhscpg0006298
BC040897
chr9: 113433078-113433972
−
—

41
UHNhscpg0006075
C6orf155
chr6: 72186425-72187545
−
chromosome 6

open reading frame

155

42
UHNhscpg0009610
CHD2
chr15: 91248245-91248931
+
chromodomain

helicase dna

binding protein 2

43
UHNhscpg0007469
CPEB1
chr15: 81113126-81113438
−
cytoplasmic

polyadenylation

element binding

protein 1

44
UHNhscpg0008660
DDB1
chr11: 60856386-60857783
−
damage-specific

dna binding

protein 1, 127 kda

45
UHNhscpg0007159
DHRS13
chr17: 24253500-24254168
−
dehydrogenase/reductase

(SDR

family) member 13

46
UHNhscpg0000452
FAM70B
chr13: 113650943-113651734
−
family with

sequence similarity

70, member b

47
UHNhscpg0000221
FBXL10
chr12: 120502364-120502883
−
F Box like protein

48
UHNhscpg0000429
FLRT2
chr14: 85069930-85070453
+
fibronectin leucine

rich

transmembrane

protein 2

49
UHNhscpg0007607
FLYWCH1
chr16: 2901699-2902102
+
zinc finger protein

50
UHNhscpg0007178
GADD45A
chr1: 67923138-67923396

growth arrest and

dna-damage-

inducible, alpha

51
UHNhscpg0000037
GPR89A
chr1: 144537481-144538576
−
similar to g

protein-coupled

receptor 89

52
UHNhscpg0006529
HAND2
chr4: 174688217-174688450
+
basic helix-loop-

helix transcription

factor

53
UHNhscpg0010276
LOC440925
chr2: 171276912-171277222
−
hypothetical gene

supported by

ak123485

54
UHNhscpg0005089
MALT1
chr18: 54489095-54489924
+
mucosa associated

lymphoid tissue

lymphoma

translocation gene 1

55
UHNhscpg0007662
MORC2
chr22: 29695224-29695365

morc family cw-

type zinc finger 2

56
UHNhscpg0007104
NPY1R
chr4: 164473405-164473726

neuropeptide y

receptor y1

57
UHNhscpg0008659
PARK2
chr6: 162819158-162819373
−
parkinson disease

(autosomal

recessive, juvenile)

2, parkin

58
UHNhscpg0007517,
PDE4DIP
chr1: 143643834-143644076
−
phosphodiesterase

UHNhscpg0007602

4d interacting

protein

(myomegalin)

59
UHNhscpg0007487
POLI
chr18: 50049552-50050313
+
polymerase (dna

directed) iota

60
UHNhscpg0009180
PSMB7
chr9: 126217209-126217803
−
proteasome

(prosome,

macropain)

subunit, beta type, 7

61
UHNhscpg0003180
SIX3
chr2: 45020740-45020934

sine oculis

homeobox

homolog 3

(drosophila)

62
UHNhscpg0004894
SYK
chr9: 92603346-92603864

spleen tyrosine

kinase

63
UHNhscpg0000364
TES
chr7: 115637345-115637985
+
testis derived

transcript (3 lim

domains)

64
UHNhscpg0000227
TJP1
chr15: 28270526-28271354
−
tight junction

protein

65
UHNhscpg0000085
TNFRSF13B
chr17: 16802068-16802226
−
tumor necrosis

factor receptor

superfamily 13 B

66
UHNhscpg0000204
TTC23
chr15: 97608595-97609633
−
Hypothetical

protein FLJ13168.

67
UHNhscpg0000299
ZCSL2
chr3: 16281447-16281734
+
DPH3, KTI11

homolog (S. cerevisiae)

68
UHNhscpg0007132
ZDHHC20
chr13: 20930805-20931472
−
zinc finger, dhhc-

type containing 20

69
UHNhscpg0007446
ZHX2
chr8: 123862942-123863095
+
zinc fingers and

homeoboxes 2

70
UHNhscpg0003020
ZNF786
chr7: 148418255-148419867
−
zinc finger protein

ZNF786

71
UHNhscpg0000434
PCNA
chr20: 5048602-5049085
−
proliferating cell

nuclear antigen

72
UHNhscpg0005318
PCNA
chr20: 5055093-5055277
−
proliferating cell

nuclear antigen

73
UHNhscpg0005042
CCND1
chr11: 69162738-69163538
+
cyclin D1

74
UHNhscpg0007998
MAPK1
chr22: 20551323-20552175
−
mitogen-activated

protein kinase 1

75
UHNhscpg0000233
ETS1
chr11: 127896681-127897162
−
ETS1 protein.

76
UHNhscpg0005090
AHR
chr7: 17326397-17326537
+
arylhydrocarbon

receptor repressor

77
UHNhscpg0003170
ESR2
chr14: 63831062-63831529
−
3pv2.

78
UHNhscpg0005109
BCL2L1
chr20: 29774490-29774701
−
BCL2-like 12

isoform 1

79
UHNhscpg0004815
ERBB4
chr2: 212526356-212526416
−
v-erb-a

erythroblastic

leukemia viral

oncogene

80
UHNhscpg0005000
ERBB4
chr2: 212552939-212553004
−
v-erb-a

erythroblastic

leukemia viral

oncogene

81
UHNhscpg0007314
ERBB4
chr2: 212713502-212713610
−
v-erb-a

erythroblastic

leukemia viral

oncogene

82
UHNhscpg0002306
ERBB4
chr2: 213109241-213109694
−
v-erb-a

erythroblastic

leukemia viral

oncogene

83
UHNhscpg0007411
TMEM117
chr12: 42519746-42519891
+
hypothetical

protein LOC84216

84
UHNhscpg0008515
GALNT13
chr2: 154892928-154892960
+
UDP-N-acetyl-

alpha-D-

galactosamine:poly

peptide

85
UHNhscpg0008264
BDNF
chr11: 27700616-27701448
−
brain-derived

neurotrophic factor

isoform b

86
UHNhscpg0002632
DUSP4
chr8: 29265449-29265864
−
dual specificity

phosphatase 4

isoform 1

87
UHNhscpg0006957
KIAA0776
chr6: 96969405-96969504
+
hypothetical

protein LOC23376

88
UHNhscpg0008950
NME6
chr3: 48342609-48343351
−
“non-metastatic

cells 6, protein

expressed in

(nucleoside-

diphosphate

kinase)”

89
UHNhscpg0000024
SMG6
chr17: 2125839-2125862
−
Est1p-like protein A

90
UHNhscpg0010841
ABCB10
chr1: 229693478-229694354
−
“ATP-binding

cassette, sub-

family B, member

10”

91
UHNhscpg0010601
MMP25
chr16: 3095712-3095935
+
matrix

metalloproteinase

25 preproprotein

92
UHNhscpg0011399
LNPEP
chr5: 96352319-96352368
+
leucyl/cystinyl

aminopeptidase

isoform 1

93
UHNhscpg0000636
LRRC4C
chr11: 40283867-40284519
−
netrin-G1 ligand

94
UHNhscpg0007219
HSPA2
chr14: 65006815-65006989
+
heat shock 70 kDa

protein 2

95
UHNhscpg0001461
ROBO3
chr11: 124736261-124736800
+
“roundabout, axon

guidance receptor,

homolog 3”

96
UHNhscpg0005839
DFNB31
chr9: 117261407-117261543
−
OTTHUMP00000021976.

97
UHNhscpg0010619
PGD
chr1: 10458486-10458639
+
phosphogluconate

dehydrogenase

98
UHNhscpg0004672
PVRL3
chr3: 110789616-110790285
+
PVRL3 protein.

99
UHNhscpg0004504
GAPDH
chr12: 6519633-6520564
+
Glyceraldehyde-3-

phosphate

dehydrogenase(EC

1.2.1.12)

(Fragment).

100
UHNhscpg0000914
AF271776
chrM: 7586-8094
+
ATP synthase a

chain (EC

3.6.3.14) (ATPase

protein 6).

101
UHNhscpg0000024
SMG6
chr17: 2125839-2125862
−
Est1p-like protein A

102
UHNhscpg0000230
DLX6
chr7: 96477436-96477749
+
distal-less

homeobox 6

103
UHNhscpg0007777
IFT88
chr13: 21140610-21140861
−
intraflagellar

transport 88

homologue

isoform 1

104
UHNhscpg0000501
SLC13A3
chr20: 45204611-45205384
−
solute carrier

family 13 member

3 isoform A

105
UHNhscpg0007046
IREB2
chr15: 78730311-78731340
+
iron responsive

element binding

protein 2

106
UHNhscpg0008329
RTTN
chr18: 67872498-67872926
−
rotatin

107
UHNhscpg0000211
KIAA1530
chr4: 1340633-1341615
+
KIAA1530 protein

108
UHNhscpg0002300
PSIP1
chr9: 15509859-15509960
−
PC4 and SFRS1

interacting protein

1 isoform 2

109
UHNhscpg0004523
CR601508
chr6: 52761939-52762111
−
OTTHUMP00000016614

110
UHNhscpg0009237
BANK1
chr4: 102711507-102712443
+
hypothetical

protein FLI34204

111
UHNhscpg0006618
JAK2
chr9: 4984202-4984895
+
janus kinase 2

While the invention has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive; the invention is not limited to the disclosed embodiments. Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims. In the claims, the word “comprising” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. A computer program may be stored/distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems. Any reference signs in the claims should not be construed as limiting the scope.

METHODS FOR THE ANALYSIS OF BREAST CANCER DISORDERS

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