WO2014129975A1

WO2014129975A1 - Identification of circulating microrna signatures for breast cancer detection

Info

Publication number: WO2014129975A1
Application number: PCT/SG2014/000073
Authority: WO
Inventors: Kok Leong Maurice CHAN; Siew Gek Ann LEE-LIM
Original assignee: Singapore Health Services Pte Ltd
Current assignee: Singapore Health Services Pte Ltd
Priority date: 2013-02-20
Filing date: 2014-02-20
Publication date: 2014-08-28
Anticipated expiration: 2015-08-20

Abstract

The present invention relates to a method for detecting breast cancer in a subject. More particularly, the invention relates to a method of detecting the presence of differentially expressed microRNAs in the blood or tissue of a subject that are indicative of the subject having breast cancer. The present invention also provides a method of detecting whether a subject with breast cancer has clinicopathological features of ER positivity, Her2 positivity and/or node positivity. The present invention also includes kits for use in the methods of the invention.

Description

IDENTIFICATION OF CIRCULATING MICRORNA SIGNATURES FOR BREAST CANCER DETECTION

FIELD OF THE INVENTION

The present invention relates to methods for screening subjects for breast cancer. More particularly, the present invention relates to screening subjects for the presence of particular microRNA's which have diagnostic efficacy.

BACKGROUND OF THE INVENTION

Breast cancer remains the leading cause of mortality in women, despite improvements in cancer screening and treatment strategies. Mammography is the current gold standard for breast cancer detection, but can have false negative rates of up to 20% (NCI data). The diagnosis of breast cancer relies on the histological examination of tissue biopsies, or cytology of fine-needle aspirates, which are both invasive procedures. Known serum-based tumour markers, such as CA15.3 or BR27.29, cannot be used for breast cancer detection due to their low sensitivity (1 ). There is thus a need to develop novel markers that are minimally invasive, for the improved detection, diagnosis, and molecular understanding of breast cancer.

MicroRNAs are approximately 22 nt long non-coding RNAs that can base pair specifically with target mRNAs to induce gene silencing through specific mechanisms involving translational repression or transcript degradation. Since their discovery in 1993, microRNAs have been estimated to regulate more than 60% of all human genes, with many microRNAs identified as key players in critical cellular functions such as proliferation and apoptosis. The current database of microRNAs, MirBase release 19, has >2000 entries of human microRNAs, constituting a major class of regulatory molecules. lorio et al. provided the earliest observation that microRNAs are differentially expressed in breast cancer tumors as compared to normal breast tissue (2). Analysis of 76 breast cancer tumours and 10 normal samples (non-cancerous breast tissues) using microarrays which probed for 386 microRNAs, identified 29 dysregulated microRNAs. In particular, miR-21 and miR-155 were up regulated while miR-10b, miR-125b, and miR-145 were down regulated. To identify dysregulated microRNAs, Persson and co-workers (3) performed extensive next-generation microRNA sequencing of paired tumour and normal tissue from 5 breast cancer patients, and detected more than 500 microRNAs, including a novel microRNA (miR-4728) encoded within the human epidermal growth factor receptor 2 (Her2) gene, which was overexpressed in Her2 amplified tumours. A plethora of studies have led to the identification of microRNAs that were differentially expressed depending on breast cancer subtype, histological grade, cancer aggressiveness (4), metastasis-free survival (5), as well as estrogen receptor (ER) (6, 7), Her2 (6, 7), or triple-negative status (4, 6).

Circulating microRNAs have been suggested to be able to distinguish breast cancer samples from healthy controls. These studies have usually involved targeted analyses of only 4 to 6 microRNAs by RT-PCR (8, 9). However, comparisons between these studies may not be straightforward as they were carried out under diverse experimental conditions. For example, circulatory microRNAs may have been extracted from serum (8, 9), plasma (11 ), circulating tumour cells, or even whole blood (12, 13). Further, while most studies employed serum samples collected pre-operatively as it has been suggested that microRNA levels may return to baseline within 2 weeks after tumour resection, one other study utilized postoperative sera (8). Circulating microRNAs may also exhibit racial differences, as the microarray profiling of microRNAs in the plasma of 10 cases each from Caucasian and African breast cancer patients resulted in only 2 common dysregulated microRNAs between these groups (10). In contrast to targeted studies involving specific microRNAs, there are few comprehensive profiling studies of circulatory microRNAs in breast cancer (10, 14), and a consistent diagnostic signature for circulatory microRNAs is not yet available. Few studies have attempted to compare the circulatory microRNA profile to that within the breast cancer tumour, such that the relationship between these two profiles of microRNAs is not clear. One study assessed a panel of seven microRNAs while another analyzed five microRNAs. In a third study, four most discriminating microRNAs, selected from discovery profiling of breast cancer tumours (n=84) and normal tissue samples (n=8), were validated using serum samples from breast cancer patients (n=75) and healthy volunteers (n=20) (15). Of these four microRNAs, which were repressed in breast cancer tumours as compared to normal breast tissues, three were also repressed in the sera of breast cancer patients. A recent study (16) investigated the status of four plasma-derived microRNAs in matched tumours, and concluded that microRNAs generally displayed opposite expression patterns in tissue and plasma. However, these comparisons between circulating and tumour microRNA profiles were not comprehensive, as microRNA profiling of the serum or plasma samples were not done.

In view of the above deficiencies, it is desirable to provide a more robust set of diagnostic markers for the non-invasive or minimally invasive detection of breast cancer.

SUMMARY OF THE INVENTION

The novel microRNA expression signatures identified in this study had sufficient diagnostic efficacy for development into blood-based biomarkers for breast cancer detection.

Accordingly, in a first aspect, the present invention provides a method of detecting whether a subject has breast cancer, comprising screening a blood sample from the subject for the presence of at least one microRNA differentially expressed in the diseased state compared to a reference sample representing a subject absent the disease, wherein the at least one microRNA is selected from the group

comprising the microRNAs in Table 3, and wherein detection of one or more of said microRNAs indicates breast cancer in the subject.

In a preferred embodiment of the invention the at least one differentially expressed microRNA is selected from the group comprising the microRNAs in Table 2.

More preferably, the at least one differentially expressed microRNA is selected from the group comprising the microRNAs miR-1 , miR-92a, miR-133a and miR-133b.

According to another preferred embodiment of the invention, the subject's blood sample is screened for the differential expression of at least two microRNAs selected from the group comprising: a) miR-92a and miR-1 , b) miR-92a and miR-133a, and c) miR-92a and miR-133b.

According to a preferred embodiment of the invention, the microRNA detection method involves using a screening platform selected from, for example, the group comprising cDNA microarrays, oligonucleotide microarrays, microRNA

(miRNA) arrays, high throughput quantitative polymerase chain reaction (qPCR), and solution hybridization/ribonuclease digestion kit and probes.

Another aspect of the invention provides a method of detecting whether a subject has breast cancer with clinicopathological features of one or more of estrogen receptor (ER) positivity, human epidermal growth factor receptor 2 (Her2) positivity and Node positivity, comprising screening a blood sample from the subject for the presence of at least one microRNA differentially expressed in the diseased state compared to a reference sample representing a subject absent the disease, wherein the at least one microRNA is selected from the group comprising: a) microRNAs in Table 6 for ER, b) microRNAs in Table 6 for Her2, and c) microRNAs in Table 6 for node positivity, and wherein detection of differential expression of one or more of said microRNAs indicates breast cancer with said one or more clinicopathological features in the subject.

In a preferred embodiment of the method, the at least one microRNA is selected from the group comprising: a) microRNAs in Table 4 for ER, b) microRNAs in Table 4 for Her2, and c) microRNAs in Table 4 for node positivity.

In a preferred embodiment, the subject has already been, or is to be, screened for breast cancer by the method of the invention described supra.

Another aspect of the invention provides a kit for screening a blood sample for the presence of microRNA indicative of breast cancer wherein the microRNA is differentially expressed in the diseased state compared to a reference sample representing a subject absent the disease, comprising oligonucleotide primers and/or probes capable of binding to and/or amplifying at least a portion of the nucleic acid sequence, or cDNA derived therefrom, of at least one differentially expressed microRNA selected from the group comprising the microRNAs in Table 3.

In another preferred embodiment of the kit, the at least one differentially expressed microRNAs are selected from the group comprising the microRNAs in Table 2.

In another preferred embodiment of the kit, the at least one differentially expressed microRNAs are selected from the group comprising the microRNAs miR- 1 , miR-92a, miR-133a and miR-133b.

According to another preferred embodiment of the kit, the kit comprises primers and/or probes to detect at least two microRNAs selected from the group comprising: a) miR-92a and miR-1 , b) miR-92a and miR-133a, and c) miR-92a and miR-133b.

According to a preferred embodiment of the invention, the kit comprises a screening platform selected from, for example, the group comprising cDNA microarrays, oligonucleotide microarrays, microRNA (miRNA) arrays, high

throughput quantitative polymerase chain reaction (qPCR), and solution

hybridization/ribonuclease digestion kit and probes.

In a preferred embodiment the kit comprises a MicroRNA array, a LNA RT- PCR panel, or a solution hybridization kit specifically directed to the differentially expressed microRNAs of the invention.

Another aspect of the invention provides a kit for screening a blood sample for the presence of microRNA indicative of breast cancer which is clinicopathologically ER positive, Her2 positive and/or Node positive, wherein the microRNA is

differentially expressed in the diseased state compared to a reference sample representing a subject absent the disease, comprising oligonucleotide primers and or probes capable of binding to and/or amplifying at least a portion of the nucleic acid sequence, or cDNA derived therefrom, of at least one microRNA selected from the the group comprising: a) microRNAs in Table 6 for ER, b) microRNAs in Table 6 for Her2, and c) microRNAs in Table 6 for node positivity, and wherein detection of differential expression of one or more of said microRNAs indicates breast cancer with said one or more clinicopathological features in the subject.

In a preferred embodiment of the kit, said at least one microRNA is selected from the group comprising: a) microRNAs in Table 4 for ER, b) microRNAs in Table 4 for Her2, and c) microRNAs in Table 4 for node positivity.

According to a preferred embodiment of the invention, the kit comprises a screening platform selected from, for example, the group comprising cDNA

microarrays, oligonucleotide microarrays, microRNA (miRNA) arrays, high

throughput quantitative polymerase chain reaction (qPCR), and solution

hybridization/ribonuclease digestion kit and probes.

The kit of the invention may further comprise a nucleotide polymerizing enzyme and a reagent buffer.

In a preferred embodiment of the kit, the blood sample is from a subject that has already been, or is to be, screened for breast cancer by the methods described supra. In a preferred embodiment of the kit, the blood sample is a serum sample.

Another aspect of the invention provides a microarray for detecting breast cancer in a subject, comprising a substrate and a set of genetic marker molecules attached to the substrate, wherein the marker molecules are oligonucleotide probes which can identify at least one of the differentially expressed microRNAs of the invention defined in Table 3.

Preferably, the at least one microRNAs are those defined in Table 2.

More preferably, the at least one microRNAs are selected from the group comprising miR-1 , miR-92a, miR-133a and miR-133b.

According to another preferred embodiment of the microarray, the microarray comprises primers or probes to detect at least two microRNAs selected from the group comprising: a) miR-92a and miR-1, b) miR-92a and miR-133a, and c) miR-92a and miR-133b.

Another aspect of the invention provides a microarray for detecting breast cancer in a subject which is clinicopathologically ER positive, Her2 positive and/or Node positive, comprising a substrate and a set of genetic marker molecules attached to the substrate, wherein the marker molecules are oligonucleotide probes which can identify at least one differentially expressed microRNA selected from the the group comprising: a) microRNAs in Table 6 for ER, b) microRNAs in Table 6 for Her2, and c) microRNAs in Table 6 for node positivity, and wherein detection of differential expression of one or more of said microRNAs indicates breast cancer with said one or more clinicopathological features in the subject. Preferably, said at least one differentially expressed microRNA is selected from the group comprising: a) microRNAs in Table 4 for ER, b) microRNAs in Table 4 for Her2, and c) microRNAs in Table 4 for node positivity.

In another preferred embodiment of the invention, the subject's blood sample is a serum sample.

Another aspect of the invention provides a method of detecting whether a subject has breast cancer, comprising screening a suspected breast cancer tissue sample from the subject for the presence of at least one microRNA differentially expressed in the diseased state compared to a reference sample representing a subject absent the disease, wherein the at least one microRNA is selected from the group comprising the microRNAs miR-720, miR-1274b, miR-1260, miR-30c, miR- 376c and miR-4324, and wherein detection of increased levels of one or more of miR-720, miR-1274b and miR-1260 or detection of decreased levels of one or more of miR-30c, miR-376c and miR-4324 indicates breast cancer in the subject.

Another embodiment of the invention provides a method of detecting whether a subject has breast cancer, comprising screening a suspected diseased breast tissue sample from the subject for the presence of at least one microRNA

differentially expressed in the diseased state compared to a reference sample representing an adjacent tissue considered to be absent the disease, wherein the at least one microRNA is selected from the group comprising the microRNAs miR-720, miR-1274b, miR-1260, miR-30c, miR-376c and miR-4324, and wherein detection of increased levels of one or more of miR-720, miR-1274b and miR-1260 or detection of decreased levels of one or more of miR-30c, miR-376c and miR-4324 in the suspected diseased tissue relative to the adjacent tissue indicates breast cancer in the subject.

Another aspect of the invention provides a kit for screening a breast tissue sample for the presence of microRNA indicative of breast cancer wherein the microRNA is differentially expressed in the diseased state compared to a reference sample representing a subject absent the disease, comprising oligonucleotide primers or probes capable of binding to and/or amplifying at least a portion of the nucleic acid sequence, or cDNA derived therefrom, of at least one differentially expressed microRNA selected from the group comprising the microRNAs miR-720, miR-1274b, miR-1260, miR-30c, miR-376c and miR-4324.

In a further embodiment of the invention, the subject is of Chinese ancestry.

BRIEF DESCRIPTION OF THE FIGURES

Figure 1 : Three components principal component analysis of (a) tissue and (b) serum samples. Breast cancer tumour tissues (a) and serum from breast cancer patients (b) are indicated in red T,). Adjacent normal tissues (a) and serum from healthy individuals (b) are indicated in blue (B).

Figure 2: Hierarchical clustering of (a) tissues and (b) serum samples, (a) breast cancer tumour and adjacent normal tissues are indicated with red (®) and blue (·) circles, respectively; (b) Serum from breast cancer patients and healthy individuals are indicated with red (it) and blue (©) circles, respectively. Figure 3: Correlation plots from inter-platform comparison studies, (a) same breast cancer tumour sample extracted by mirVana and miRNeasy; (b) same breast cancer tumour sample ran on microarray and RT-PCR panels.

Figure 4: Test for collinearity by Variance Inflation Factor (VIF) computation, and derivation of diagnostic models and significant markers by logistic regression (LR). Odds ratio (OR), which is also the exponentiation of the B coefficient. Statistical significance is represented by the P-value.

Figure 5: Validation of significant microRNAs by RT-PCR using 132 cases and 101 controls, (a) - (d) show box-and-whisker plots (generated by PASW) representing RT-PCR results for miR-1 , miR-92a, miR- 33a, and miR-133b respectively. The y-axis depicts log2 fold change. The lines inside the boxes denote the medians. The boxes mark the interval between the 25^th and 75^th percentiles. The whiskers denote the interval between the maximum and minimum values. Filled circles indicate outliers, defined as values beyond one and a half box lengths from either end of the box. Statistical significance was determined using the Mann- Whitney test; (e) ROC curves plotted using the microRNA combinations derived by logistic regression.

DESCRIPTION OF PREFERRED EMBODIMENTS

In the following discussion, embodiments of the invention will be described mostly by reference to examples employing the Agilent human microRNA microarray and the LNA RT-PCR human microRNA panels (Exiqon). However, it will be understood by the skilled person that the methods and systems described herein may be readily adapted for use with other types of microarray, or other

measurement/detection platforms. As used herein, the term 'differentially expressed' refers to increased (up regulated) or decreased (down regulated) expression of microRNA in a diseased subject relative to the expression level in the respective normal disease-free state. For example, the microRNA expression levels in the serum of a test subject may be compared to that in the serum of a subject absent the disease or cohort of subjects absent the disease. The microRNA expression levels in a suspected cancer tissue of a test subject may be compared to that in an adjacent 'disease-free' tissue sample of the test subject or to a corresponding tissue of a different subject absent the disease or cohort of subjects absent the disease.

As used herein, the term 'a subject absent the disease' refers to a subject who is considered to be free of breast cancer for the purpose of the invention.

The terms "gene", "probe" and "genetic marker molecule" are used

interchangeably for the purposed of the preferred embodiments described herein, but are not to be taken as limiting on the scope of the invention.

It is intended that both the nucleotide sequences of the microRNAs and their reverse complementary nucleotide sequences fall within the scope of embodiments of the invention.

Accordingly, in a first aspect, the present invention provides a method of detecting whether a subject has breast cancer, comprising screening a blood sample from the subject for the presence of at least one microRNA differentially expressed in the diseased state compared to a reference sample representing a subject absent the disease, wherein the at least one microRNA is selected from the group comprising the microRNAs in Table 3, and wherein detection of differential expression of one or more of said microRNAs indicates breast cancer in the subject.

An advantage of using a blood sample from the subject is that it is not a very invasive procedure on the body compared to, for example, the removal of tissue specimens by biopsy for analysis. Moreover, blood samples are often taken for other tests so the inconvenience to the subject can be minimised.

More preferably, the at least one differentially expressed microRNA is selected from the group comprising the microRNAs miR-1 , miR-92a, miR-133a and miR-133b. In particularly preferred embodiments, the nucleotide sequences of miR- 1 , miR-92a, miR-133a and miR-133b are represented by SEQ ID NOS: 1 , 2, 3 and 4, respectively. We have found that for each of the four microRNAs individually, the areas under the curves (AUCs) were in the range of 0.78 to 0.87.

The resultant ROC curves plotted using these at least two microRNA combinations derived by logistic regression showed AUCs of 0.90 to 0.91.

(miRNA) arrays, high throughput quantitative polymerase chain reaction (qPCR), and solution hybridization/ribonuclease digestion kit and probes. MicroRNA arrays and LNA RT-PCR panels such as those described in the Examples herein may be suitable for the methods of the invention. Solution hybridization kits such as the m/^'rVana™ miRNA Detection Kit manufactured by Life Technologies Corporation may also represent a suitable screening platform. It is to be understood by a person skilled in the art that the screening platform used in the methods of the invention is not to be limited to a selection from those described above.

Another aspect of the invention provides a method of detecting whether a subject has breast cancer with clinicopathological features of one or more of estrogen receptor (ER) positivity, human epidermal growth factor receptor 2 (Her2) positivity and Node positivity comprising screening a blood sample from the subject for the presence of at least one microRNA differentially expressed in a diseased state compared to a reference sample representing a subject absent the disease, wherein the at least one microRNA is selected from the group comprising: a) microRNAs in Table 6 for ER, b) microRNAs in Table 6 for Her2, and c) microRNAs in Table 6 for node positivity, and wherein detection of differential expression of one or more of said microRNAs indicates breast cancer with said one or more clinicopathological features in the subject.

In a preferred embodiment of the method, said at least one microRNA is selected from the group comprising: a) microRNAs in Table 4 for ER, b) microRNAs in Table 4 for Her2, and c) microRNAs in Table 4 for node positivity.

In particularly preferred embodiments, the nucleotide sequences of the microRNAs listed in Table 4 are represented in Table 8 by SEQ ID NOS: 5 to 31. In a preferred embodiment, the subject has already been, or is to be, screened for breast cancer by the method described supra.

Another aspect of the invention provides a kit for screening a blood sample for the presence of microRNA indicative of breast cancer wherein the microRNA is differentially expressed in the diseased state compared to a reference sample representing a subject absent the disease, comprising oligonucleotide primers or probes capable of binding to and/or amplifying at least a portion of the nucleic acid sequence, or cDNA derived therefrom, of at least one differentially expressed microRNA selected from the group comprising the microRNAs in Table 3.

In another preferred embodiment of the kit, the at least one differentially expressed microRNAs are selected from the group comprising the microRNAs miR- 1 , miR-92a, miR-133a and miR-133b. In particularly preferred embodiments, the nucleotide sequences of miR-1 , miR-92a, miR-133a and miR-133b are represented by SEQ ID NOS: 1 , 2, 3 and 4, respectively. We have found that for each of the four microRNAs individually, the areas under the curves (AUCs) were in the range of 0.78 to 0.87.

According to another preferred embodiment of the kit, the kit comprises primers or probes to detect at least two microRNAs selected from the group comprising: a) miR-92a and miR-1 , b) miR-92a and miR-133a, and c) m^'iR-92a and m^'iR-133b.

throughput quantitative polymerase chain reaction (qPCR), and solution

hybridization/ribonuclease digestion kit and probes.

In a preferred embodiment the kit comprises a MicroRNA array, a LNA RT- PCR panel, qPCR primers, or a solution hybridization kit specifically directed to the differentially expressed microRNAs of the invention. The kit may also comprise software/protocol for data interpretation.

In particularly preferred embodiments, the nucleotide sequences of the microRNAs listed in Table 4 are represented in Table 8 by SEQ ID NOS: 5 to 31. According to a preferred embodiment of the invention, the kit comprises a screening platform selected from, for example, the group comprising cDNA microarrays, oligonucleotide microarrays, microRNA (miRNA) arrays, high throughput quantitative polymerase chain reaction (qPCR), and solution

hybridization/ribonuclease digestion kit and probes.

In a preferred embodiment the kit comprises a MicroRNA array, a LNA RT- PCR panel, qPCR primers, or a solution hybridization kit specifically directed to the differentially expressed microRNAs of the invention.

In a preferred embodiment of the kit, the blood sample is from a subject that has already been, or is to be, screened for breast cancer by the methods described supra.

Preferably, the at least one microRNAs are those defined in Table 2.

More preferably, the at least one microRNAs are selected from the group comprising miR-1 , miR-92a, miR-133a and miR-133b. In particularly preferred embodiments, the nucleotide sequences of miR-1 , miR-92a, miR-133a and miR- 133b are represented by SEQ ID NOS: 1 , 2, 3 and 4, respectively.

According to another preferred embodiment of the microarray, the microarray comprises primers or probes to detect at least two microRNAs selected from the group comprising: a) miR-92a and miR-1, b) miR-92a and miR-133a, and c) miR-92a and miR-133b. Another aspect of the invention provides a microarray for detecting breast cancer in a subject which is clinicopathologically ER positive, Her2 positive and/or Node positive, comprising a substrate and a set of genetic marker molecules attached to the substrate, wherein the marker molecules are oligonucleotide probes which can identify at least one differentially expressed microRNA selected from the the group comprising: a) microRNAs in Table 6 for ER, b) microRNAs in Table 6 for Her2, and c) microRNAs in Table 6 for node positivity, and wherein differential expression of one or more of said microRNAs indicates breast cancer with said one or more clinicopathological features in the subject.

Preferably, said at least one differentially expressed microRNA is selected from the group comprising: a) microRNAs in Table 4 for ER, b) microRNAs in Table 4 for Her2, and c) microRNAs in Table 4 for node positivity.

In particularly preferred embodiments, the nucleotide sequences of the microRNAs listed in Table 4 are represented in Table 8 by SEQ ID NOS: 5 to 31.

Another aspect of the invention provides a kit for screening a tissue sample for the presence of microRNA indicative of breast cancer wherein the microRNA is differentially expressed in the diseased state compared to a reference sample representing a subject absent the disease, comprising oligonucleotide primers or probes capable of binding to and/or amplifying at least a portion of the nucleic acid sequence, or cDNA derived therefrom, of at least one differentially expressed microRNA selected from the group comprising the microRNAs miR-720, miR-1274b, miR-1260, miR-30c, miR-376c and miR-4324.

In particular, the nucleotide sequences of these miRNAs are represented in Table 8 by SEQ ID NOS: 32-37.

In a further embodiment of the invention, the subject is of Chinese ancestry.

Having now generally described the invention, the same will be more readily understood through reference to the following examples which are provided by way of illustration, and are not intended to be limiting of the present invention.

EXAMPLES Experimental design MicroRNAs from paired breast cancer tumours, normal tissue and serum samples derived from 32 patients were comprehensively profiled using microarrays (1300 microRNAs profiled for tumour and normal tissues) or LNA RT-PCR panels (742 microRNAs profiled for serum samples). Serum samples from healthy individuals (n=22) were also employed as normal controls. Significant serum microRNAs, identified by logistic regression, were validated in an independent set of serum samples from patients (n=132) and healthy controls (n=101 ).

Materials and methods

Patients

Patients and healthy volunteers were Singaporeans of Chinese ancestry.

Written informed consent was obtained from all contributing patients and volunteers, and ethics approval for this study was obtained from the Centralized Institutional Review Board of SingHealth (Singapore Health Services Pte Ltd, Singapore).

Histopathological records (ER, Her2, and lymph node status) were obtained from SingHealth Tissue Repository.

Tissue and serum samples for the profiling stage

Matched fresh frozen breast cancer tumours, adjacent normal tissues, and pre-operative sera from 32 breast cancer patients were obtained from the SingHealth Tissue Repository. Control serum samples were recruited from 22 healthy female volunteers. The mean age ± standard deviation for the patients at diagnosis, and healthy volunteers at time of recruitment, were 50 ± 13 years and 47 ± 6 years, respectively. Of the 32 breast cancer patients employed for the profiling stage, 3 (9%), 15 (46%), 9 (28%), or 2 (6%), were diagnosed with stage 1 , 2, 3, or 4 cancer, respectively.

All tissue samples were histologically confirmed by a pathologist using hematoxylin and eosin staining of cryosectioned specimens. One tumour sample was rejected due to failure to detect any tumour cells. Except for two samples (with 30% and 40% tumour cells), all tumour tissues employed had a minimum of 60% tumour cells, as estimated microscopically (Table 1 ). Overall, the breast cancer tumour samples had an average of about 70% tumour cells. The criteria for adjacent normal tissue were absence of tumour cells and presence of epithelial cells. Hence, after histological confirmation, 31 breast cancer tumours and 23 matched normal tissues were employed for microRNA extraction and profiling using microarray. TABLE 1. Details of breast cancer patients employed for this study. IDC

(NOS), Invasive ductal carcinoma (Not Otherwise Specified); DCIS, Ductal

Carcinoma In-Situ; ILC, Invasive lobular carcinoma; LN, lymph node status;

not available; nd, not detected.

Patient Tumour Matched Matched Diagnosis % Age ER Her2 LN Tumour Stage ID normal Serum ^' - ^{■ !} Invasive Size

Cells . (mm) _a

Profiling Stage

p-1 · • IDC (NOS) 60% 69 + na + 45 na p-2 · • IDC (NOS) 60% 54 + na - na na p-3 · • IDC (NOS) 80% 48 - na + 30 na p-5 · • IDC (NOS) 70% 41 + na + 50 3 p-7 · • • Mucinous CA 30% 47 + - - 33 2 p-8 · • • IDC (NOS) 60% 59 - na + 40 3 p-9 · • • IDC (NOS) 60% 68 + - - 7 1 p-10 · • IDC (NOS) 95% 36 - + - 30 2 p-11 · • IDC (NOS) 90% 43 + - - 35 2 p-12 · • • IDC (NOS) 80% 37 + + - 70 2 p-13 · • • IDC (NOS) 80% 44 + - + 30 2 p-14 · • • IDC (NOS) 60% 28 + - + 40 2 p-15 · • • IDC and DCIS 70% 41 + - + 25 2 p-16 · • • IDC (NOS) 80% 46 - + + 50 2 p-17 · • • IDC (NOS) 85% 48 + + + 40 2 p-18 · • • IDC (NOS) 60% 38 - + + 30 3 p-19 · • • IDC and DCIS 70% 58 - + + 41 2 p-20 · • • IDC (NOS) 70% 47 + - + 21 4 p-21 · • • IDC (NOS) 80% 39 + + - 16 1 p-22 · * • IDC (NOS) 80% 43 + + + 103 3 p-23 · • IDC (NOS) 90% 79 - na - 30 3 p-24 · · • IDC (NOS) 80% 50 + + + 65 4 p-25 · · • IDC (NOS) 70% 71 + - + 45 2 p-26 · • IDC (NOS) 60% 65 + + + 60 3 p-27 · · • IDC & DOS 90% 65 - - - 30 2 p-28 · · • IDC (NOS) 40% 49 + + + 30 3 p-29 · · • PAPILLARY 65% 49 + - + 22 2

CA p-30 · · • IDC (NOS) 60% 42 + + - 13 1 p-31 · · • IDC (NOS) 70% 81 + + - 50 2 p-32 · · • IDC (NOS) 80% 36 + + + 23 2 p-33 · · • IDC (NOS) 90% 37 - - + 50 3 p-34 • ILC nd 49 + na + 120 3

Validation Stage v-1 • IDC (NOS) 47 na na na 13 1 v-2 • IDC (NOS) 43 na na na 60 1 v-3 • IDC (NOS) 42 + + + na na v-4 • IDC (NOS) 45 + na + 30 2 v-5 • IDC (NOS) 27 + na + 40 3 v-6 • IDC (NOS) 57 na na - 35 2 v-7 • IDC (NOS) 49 - na + 50 2 v-8 • IDC (NOS) 78 + na - 45 2 v-9 • PAPILLARY 55 + na - 110 1

CA

v-10 • IDC (NOS) 53 + na - 18 3 v-11 • IDC (NOS) 57 - na - 50 2 v-12 • IDC (NOS) 48 - na - 155 4 v-13 • IDC (NOS) 54 + na - 15 2 v-14 • ILC 69 + na - 60 1 v-15 • IDC (NOS) 67 - na + 35 3 v-16 • IDC (NOS) 62 + na - na 2 v-18 • IDC (NOS) 54 + na + 35 2 v-19 • IDC (NOS) 68 + na + 35 3 v-21 • IDC (NOS) 51 + na - 17 1 v-22 • IDC (NOS) 51 + na + 30 3 v-23 • IDC (NOS) 56 + na + 25 4 v-24 • IDC (NOS) 35 - na + 23 3 v-26 • IDC (NOS) 45 - na - 30 2 v-27 • IDC (NOS) 73 + na + 35 2 v-28 • IDC (NOS) 59 + na - 45 1 v-30 • IDC (NOS) 34 na na - 33 1 v-32 • Mixed IDC 56 + na - 25 2 and ILC

v-34 • IDC (NOS) 50 + na + 25 2 v-35 • IDC (NOS) 52 - - - 22 2 v-36 • IDC (NOS) 79 na na - 35 3 v-37 • IDC (NOS) 29 - - + 37 4 v-38 • Mucinous CA 62 + na + 60 4 v-39 • IDC (NOS) 67 + na + na 4 v-40 • IDC (NOS) 57 + na + 20 1 v-41 • IDC (NOS) 54 - na + 50 4 v-43 • IDC (NOS) 70 - na -^■ 30 2 v-45 • IDC (NOS) 60 + na - na 1 v-46 • IDC and DCIS 42 - na - na 2 v-48 • IDC (NOS) 48 + na + na 2 v-49 • IDC (NOS) 75 + + + 26 2 v-52 • Medullary- 55 + na - 40 2 like CA v-53 • DCIS 46 na na na na 1 v-54 • IDC (NOS) 65 - na - 17 1 v-55 • IDC (NOS) 49 - na + 25 2 v-56 • IDC (NOS) 58 - na + 50 4 v-57 • IDC (NOS) 41 - na - 20 1 v-58 • IDC (NOS) 48 + na - 27 2 v-59 • IDC (NOS) 55 + na + 30 2 v-60 • IDC (NOS) 44 + na - 15 3 v-61 • IDC (NOS) 49 + na - 22 2 v-62 • IDC (NOS) 49 na na + na 3 v-63 • IDC (NOS) 42 + na + 25 3 v-64 • IDC (NOS) 46 + na + 3 2 v-65 • IDC (NOS) 49 + na + 60 3 v-66 • ILC 50 + na + 30 na v-67 • IDC (NOS) 50 + na + 30 3 v-68 • IDC (NOS) 39 + na + 40 3 v-69 • IDC (NOS) 40 + na - 37 2 v-70 • IDC (NOS) 33 + na + 65 4 v-71 • IDC (NOS) 42 + na + 25 2 v-72 • IDC (NOS) 48 + na na na 2 v-73 • IDC (NOS) 49 + na + 30 3 v-74 • IDC (NOS) 62 - na + 20 3 v-75 • IDC (NOS) 60 - + + 25 3 v-76 • IDC (NOS) 35 - + + 45 2 v-78 • IDC (NOS) 62 + na + 35 2 v-79 • IDC (NOS) 60 + na - 15 1 v-80 • No 45 na na na na 1 malignancy

(history of

DCIS) v-81 • ILC 25 + na - na 2 v-82 • IDC (NOS) 64 + + + 40 3 v-83 • IDC (NOS) 49 - + + na 2 v-84 • MIXED IDC 73 + na - 28 2 v-87 • IDC and DCIS 46 - + na na 2 v-89 • IDC (NOS) 55 - na na 30 2 v-91 • IDC (NOS) 56 + na - 35 2 v-92 • IDC and DCIS 60 + na + 40 2 v-94 • ILC 83 na na - 33 2 v-95 • IDC (NOS) 75 na na + 45 4 v-96 • IDC (NOS) 52 na na + 35 3 v-98 • ILC 43 + na + 40 3 v-99 • IDC (NOS) 51 - na na 20 3 v-100 • IDC (NOS) 51 - na - na 1 v-101 • IDC (NOS) 48 + na + 50 3 v-102 • IDC (NOS) 55 - na - 30 2 v-103 • IDC (NOS) 52 + na + na 4 v-104 • IDC (NOS) 53 + na - na na v-105 • IDC (NOS) 60 + na + 32 2 v-106 • IDC (NOS) 52 + na + 28 2 v-107 • IDC (NOS) 43 - na + na na v-108 • IDC (NOS) 43 - na + na na v-109 • IDC (NOS) 77 - na + 30 na v-110 • IDC (NOS) 42 - na na na 1 v-111 • IDC (NOS) 64 + na - 30 2 v-112 • IDC (NOS) 56 + na - 20 1 v-113 • IDC (NOS) 68 - na - 30 2 v-114 • IDC (NOS) 73 na na - 30 2 v-115 • IDC (NOS) 46 na na + 30 2 v-116 • IDC (NOS) 69 + na - 30 2 v-117 • IDC (NOS) 75 - na + 70 3 v-118 • IDC (NOS) 79 - na + 30 3 v-119 • IDC (NOS) 73 + na - 30 na v-120 • IDC (NOS) 80 + na - 40 2 v-121 • IDC (NOS) 66 - na + 30 3 v-122 • IDC (NOS) 56 + na + 32 3 v-123 • IDC (NOS) 61 - na + 60 na v-124 • IDC (NOS) 59 + na - 17 1 v-125 • IDC (NOS) 43 na na - 20 1 v-126 • IDC (NOS) 66 - na - 20 1 v-127 • IDC (NOS) 50 - na + 60 3 v-128 • IDC (NOS) 67 + na + na na v-129 • IDC (NOS) 50 + na + 30 na v-130 • IDC (NOS) 51 + na + 16 4 v-131 • IDC (NOS) 41 + na + 38 3 v-132 • IDC (NOS) 70 + na + 90 na v-133 • IDC (NOS) 54 na na + 55 3 v-134 • IDC (NOS) 58 + na - 40 na v-135 • IDC (NOS) 44 - na - 40 na v-136 • IDC (NOS) 66 na na - 45 2 v-137 • IDC (NOS) 44 + na + 40 na v-138 • IDC (NOS) 71 + na + 35 2 v-139 • IDC (NOS) 46 + na - 25 na v-140 • IDC (NOS) 61 + na + 40 na v-141 • IDC (NOS) 56 - na + 45 2 v-142 • IDC (NOS) 59 + na + 50 2 v-143 • IDC (NOS) 56 + na + 35 2 v-144 • IDC (NOS) 47 - na - 40 na v-145 • IDC (NOS) 68 - na + 40 na v-146 • IDC (NOS) 58 - na + 55 na v-147 • IDC (NOS) 43 + na + 60 na v-148 • IDC (NOS) 74 + na - 37 2 v-149 • IDC (NOS) 42 + na - 54 2 v-150 • IDC (NOS) 44 - na + 75 3 Blood samples were collected in Becton Dickinson (Franklin Lakes, NJ) Vacutainer™ SST™ tubes. Serum was harvested by centrifugation at 2200g after allowing blood to clot for 30mins. Thirty-two matched serum samples from breast cancer patients and 22 samples from healthy controls were obtained for profiling. Sera samples were stored at -80°C.

Serum samples for the validation stage

Additional serum samples from breast cancer patients (n= 32) were obtained from the SingHealth Tissue Repository (Table 1), and additional control serum samples (n=101) were recruited from healthy female volunteers. The mean age ± standard deviation for the breast cancer patients at diagnosis, and healthy volunteers at time of recruitment, were 54 ± 11 years and 48 ± 7 years, respectively. Of the 132 breast cancer patients employed for the validation stage, 20 (15%), 52 (39%), 29 (21%), or 11 (8%), were diagnosed with stage 1 , 2, 3, or 4 cancer, respectively, and staging information was not available for 20 patients.

MicroRNA extraction

MicroRNAs were extracted from tissue or serum samples using the miRVana™ (Life Technologies, Carlsbad, CA) or miRNeasy (Qiagen, Hilden, Germany) kits respectively, according to manufacturers' instructions. For miRNeasy, the standard protocol was modified based on Exiqon's application note "RNA Purification from Blood Plasma & Serum" (located on their web site), which used MS2 (Roche, Basel, Switzerland) as a carrier. MicroRNA extraction was carried out using 6 to 10 pieces of tissue (approximately 1x1x1 mm) or 250ul of serum as the starting material. Quality control (QC) of RNA from tissue samples was carried out using the Agilent Bioanalyzer (Santa Clara, CA). QC of serum samples was carried out using single-plex LNA™ RT-PCR (Exiqon, Vedbaek, Denmark), and LNA primers for serum markers (miR-16 and miR-20a).

Reverse transcription and RT-PCR

For QC and individual LNA™ RT-PCR assays, reverse transcription was carried out using the Universal cDNA Synthesis kit (Exiqon), employing 4ul of microRNA-containing total RNA, 2ul of enzyme mix, and 4ul of 5x reaction buffer, made up to a 20ul reaction volume using nuclease-free water. Reverse transcription was carried out at 42°C for 60 min, followed by inactivation at 95°C for 5 mins. Every RT-PCR experiment included no reverse transcription controls. For RT-PCR, 10ul reactions were prepared in the following proportions: 5ul of SYBR Green master mix, 1ul of LNA primer mix, and 4ul of cDNA template (55x dilution). RT-PCR was performed at 95°C for 10min; followed by 40 cycles of 95°C for 10s/60°C for 1 min using an Applied Biosystems™ 7500 Real-Time PCR System (Life Technologies). MicroRNA microarray and LNA RT-PCR panels

The Agilent human microRNA microarray was based on miRBase Release 16.0, with probes for about 1300 microRNAs. The microarray is based on a direct labeling (Cy3) chemistry, and was carried out according to the manufacturer's standard protocol. Each microarray experiment employed 200ng of microRNA-containing total RNA.

The LNA™ RT-PCR human microRNA panels (Exiqon) comprised of two 384- well plates for the detection of 742 microRNAs. Reverse transcription was performed using the Universal cDNA Synthesis kit (Exiqon) in 40ul reactions per panel, employing 8ul of microRNA-containing total RNA, 4ul of enzyme mix, and 8ul of 5x reaction buffer, made up to 40ul using nuclease-free water. For each 384-well plate, the cDNA was diluted 55x (using 2160ul of nuclease-free water). Two ml of the diluted cDNA was combined with an equal volume of 2x SYBR Green Master Mix (Exiqon) and dispensed at 10ul per well. The RT-PCR was executed according to Exiqon's protocol for serum and plasma on an Applied Biosystems™ 7900HT Real- Time PCR System (Life Technologies) which was set using run templates (SDS files) downloaded from Exiqon's website.

The GEO accession number for the microRNA expression profiles from the microarray and RT-PCR panels reported in this study is GSE42128.

Biocomputational analysis

Microarray expression data was imported into the GeneSpring software (Agilent). Global normalization was carried out based on 90 percentile shift followed by log2 transformation. Principal component analysis (PCA), paired and unpaired t- test, and cluster analysis were computed using the GeneSpring software.

Ct values from RT-PCR were imported into the GenEx software (Exiqon). The analysis workflow included (i) QC using no reverse transcription controls, (ii) interplate calibration, (iii) selection of reference genes using NormFinder and GeNorm, and (iv) normalization and log2 transformation. PCA, cluster analysis, f-test (unpaired, 2-tailed), Mann-Whitney test (2-sided), and Kolmogorov-Smirnov test (for normal distribution) were done using the GenEx software where appropriate. To derive the most important serum microRNA species for the validation stage, breast cancer associated serum microRNAs that remained significant after Bonferroni correction (n=21), were employed for analysis by collinearity statistics so as to obtain sets of non-collinear microRNA markers suitable for logistic regression (17). MicroRNAs showing evidence of collinearity are not desirable as diagnostic markers because collinearity will amplify errors in the subsequent regression analysis. Variance Inflation Factor (VIF) scores of ≥5 was taken as indicative of collinearity, and thus only microRNAs with VIF <5 were employed for logistic regression. Sets of microRNAs were derived with VIF<5 were subjected to binary logistic regression (18, 19). Binary logistic regression was carried out using the PASW software (IBM Corporation, Armonk, NY; version 18) using the Forward: LR (likelihood ratio) method. This is a stepwise selection method with entry testing based on the significance of the score statistic, and removal testing based on the probability of a likelihood-ratio statistic derived from the maximum partial likelihood estimates. Receiver operating characteristic (ROC) curves were plotted using PASW. Data reproducibility

To verify the reproducibility of the microarray platform, technical replicates were performed for four samples. The R² values obtained from the correlation plots between replicates ranged from 0.96 to 0.99 (data not shown), confirming the technical reproducibility of the platform. Three other tissue samples were extracted twice using the miRVana™ kit and subjected to microarray analysis. The R² values ranged from 0.89 to 0.96 for the correlation plots between the duplicate samples (data not shown), validating the consistency of the miRVana™ extraction method.

Similarly, to validate the consistency and reliability of the LNA™ RT-PCR platform, one sample was reversed transcribed twice and run on the LNA™ RT-PCR panels, with R²=0.97 obtained on the correlation plot, confirming the reproducibility of this platform (data not shown). A no reverse transcription control was also run on a complete set of the LNA™ RT-PCR panels. Input of the resultant background Ct values into the QC workflow in the GenEx program did not identify any problematic microRNA with Ct values considered too close (within 3 cycles) to background values.

Results

MicroRNA profiling of tumor and adjacent normal tissue samples Significant differentially expressed microRNAs were identified by applying the paired i-test (23 pairs of breast cancer tumours vs. adjacent normal tissues) or the unpaired i-test (31 breast cancer tumours vs. 23 adjacent normal tissues). This resulted in 73 microRNAs that were significant (p<0.05) after correction for multiple testing by Benjamini-Hochberg FDR (false discovery rate) in both paired as well as unpaired f-tests. The 20 most significant microRNAs, with corrected P values ranging from 1.6E-06 to 8.0E-09, are shown in Table 2.

TABLE 2. Twenty most significant microRNAs differentially expressed in breast cancer tumours vs adjacent normal tissues, and in breast cancer sera vs sera from healthy individuals. Other studies which have also reported the microRNAs in relation to breast cancer are referenced.

WVM^' , FDR Corrected '!iik

Systematic Name P-value ation Fold Change References

Breast cancer tumours vs adjacent normal tissues

miR-145 8.04E-09 down 2.48 (2, 3)

miR-21 1.23E-07 up 1.95 (2, 3)

miR-497 1.76E-07 down 2.02 (24)

miR-720 2.16E-07 up 2.05

miR-1274b 2.16E-07 up 1.92

miR-99a 2.47E-07 down 2.33 (3)

miR-195 3.16E-07 down 1.69 (22)

miR-143 3.42E-07 down 1.61 (2, 3)

miR-1260 4.15E-07 up 1.84

miR-30c 4.43 E-07 down 1.73

miR-125b 6.88E-07 down 1.66 (2)

miR-140-5p 6.96E-07 down 1.29 (2, 3)

miR-lOb 6.96E-07 down 1.45 (2, 3)

miR-376c 1.02E-06 down 2.08

miR-103 1.02E-06 up 0.78 (25)

miR-100 1.04E-06 down 1.67 (3)

miR-4324 1.19E-06 down 2.19

miR-93 1.19E-06 up 1.32 (26)

miR-140-3p 1.30E-06 down 1.34 (2, 3)

miR-107 1.63E-06 up 0.72 (3)

Breast cancer sera vs sera from healthy female controls

miR-1 1.77E-07 up 3.59

miR-133b 4.04E-07 up 3.41

miR-133a 8.61E-07 up 3.29

miR-92a 8.97E-07 up 1.34

miR-lOb 1.67E-06 up 1.77 (12)

miR-486-5p 3.36E-06 up 1.65

miR-423-5p 6.38E-06 up 1.10

miR-7 6.38E-06 up 1.64

miR-223 1.80E-05 down 0.98

A complete list of significant microRNAs is provided in Table 3. Seven out of 20 dysregulated microRNAs were overexpressed.

TABLE 3. Complete list of significant microRNAs identified from breast

. cancer tumour and serum samples.

Systematic Name FDR Corrected P-value Regulati on Fold Change miR-331-3p 1.24E-04 up 0.54 mi^'R-142-3p 1.45E-04 up 1.23 let-7c 1.5 E-04 down 0.94 miR-638 1.84E-04 down 1.18 miR-30a 1.84E-04 down 1.56 miR-30e^* 2.26E-04 down 0.88 miR-424 2.60E-04 down 1.08 mi -25 3.01 E-04 up 0.62 miR-16 4.76E-04 up 0.51 miR-451 1.01 E-03 down 1.78 miR-17 1.14E-03 up 0.78 miR-200b 1.33E-03 up 1.32 miR-324-3p 1.53E-03 down 0.49 miR-1280 2.48E-03 up 0.68 miR-342-3p 4.28E-03 up 0.79 miR-2861 4.77E-03 down 1.22 let-7b 4.77E-03 down 0.64 miR-301a 5.30E-03 up 0.88 miR-361-5p 7.52E-03 down 0.38 miR-4299 8.04E-03 down 0.93 miR-1202 8.71 E-03 down 1.19 miR-625 9.12E-03 up 0.70 miR-3679-5p 1.02E-02 down 1.06 mi^'R-4306 1.41E-02 up 0.68 miR-199a-3p 1.41 E-02 down 0.50 miR-3656 1.99E-02 down 0.93 miR-1275 2.10E-02 down 0.81 miR-199a-5p 2.56E-02 down 0.58 miR-4310 2.61 E-02 up 0.40 miR-1915 2.63E-02 down 0.54

\e\-7\ 2.78E-02 down 0.29 let-7a 2.92E-02 down 0.38 miR-23c 3.13E-02 up 0.39 miR-4313 3.14E-02 up 0.38 miR-425^* 3.58E-02 up 0.35 miR-574-5p 3.94E-02 down 0.52 miR-214 3.95E-02 down 0.45 miR-3162 4.45E-02 down 0.75 miR-181a 4.45E-02 up 0.43

Breast cancer sera vs sera from healthy female controls mi^'R-1 1.77E-07 up 3.59 miR-133b 4.04E-07 up 3.41 miR-133a 8.61 E-07 up 3.29 miR-92a 8.97E-07 up 1.34 miR-10b 1.67E-06 up 1.77 miR-486-5p 3.36E-06 up 1.65 miR-423-5p 6.38E-06 up 1.10 miR-7 6.38E-06 up 1.64 miR-223 1.80E-05 down 0.98 miR-20a 1.80E-05 up 0.92 miR-185 2.06E-05 up 1.04 miR-338-3p 2.06E-05 down 1.02 let-7i 2.06E-05 up 0.64 miR-16 2.68E-05 up 1.30 miR-214 5.27E-05 up 1.80 let-7b 8.04E-05 up 0.85 miR-144^* 1.18E-04 up 1.00 miR-16-2* 1.53E-04 up 1.08

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Systematic Name FDR Corrected P-va!ue Regulation , Fold Change

miR-627 3.00E-02 up 0.98

mi^'R-18a 3.45E-02 up 0.34

miR-145* 3.60E-02 up 0.93

miR-181c 3.82E-02 down 0.86

miR-877 4.34E-02 up 1.00

miR-134 4.43E-02 up 0.96

miR-210 4.43E-02 up 0.72

miR-93^* 4.45E-02 up 0.56

miR-664 4.98E-02 down 0.45

Three component PCA (Figure 1a) was able to cluster 84.4% of the samples into tumour and normal tissue groups. Non-supervised hierarchical clustering of the expression profiles of breast cancer tumours and adjacent normal tissues based on Euclidean distance using the 20 most significant microRNAs in a self-organizing map was able to cluster the majority of breast cancer tumours from the adjacent normal tissues (Figure 2a).

Table 4 lists the microRNAs that were significantly associated with ER, Her2, and lymph node positivity, as determined using the unpaired Student's f-test, without correction for FDR. Interestingly, almost all of the differentially expressed microRNAs were novel insofar as being associated with breast cancer, with the majority being unique from those identified in other studies (4-7). Notably, these previous studies did not share common significant microRNAs between each other.

A complete list of microRNAs that are associated with ER, Her2 and lymph node positivity is provided in Table 5.

MicroRNA profiling of serum samples

Among the 6 suggested reference gene candidates provided in the LNA™ RT-PCR panels, both the geNorm and NormFinder algorithms identified miR-103 and miR-191 as the most stably expressed, best gene combination for use as reference genes for normalizing the RT-PCR data. Statistical analysis of the serum microRNA profiles led to the identification of 85 microRNAs that were significant (p<0.05) after FDR correction for multiple testing. The most significant 20 microRNAs are shown in Table 2, and 18 of these were up regulated in breast cancer. Most of these microRNAs appeared to be novel and have not been reported in the context of circulating microRNA in breast cancer. A complete list of significant microRNAs identified from serum is provided in Table 3. TABLE 4. Ten most significant breast cancer tumour microRNAs and serum microRNAs associated with clinicopathological features.

TABLE 5. Complete list of si breast cancer tumour microRNAs associated with clinicopathological features.

ER.positivity Her2 positivity Node positivity

Systematic P- , RegFold ■ Systematic P- Reg- Fold- P- Reg- Fold name value ulation . chang ^• name value ulation change Systematic name value„ ulation change miR-622 3.3E- up m^'iR-361-5p 4.15E- down miR-181d 0.025 up

04 1.26 04 0.86 0.77 miR-342-3p 0.0013 up 1.44 m^'iR-503 0.0010 up 1.96 miR-24 0.025 up 0.39 miR-25 0.0022 down 0.61 miR-143* 0.0011 up 1.35 miR-452 0.001 up 2.25 miR-29a* 0.0036 down 1.01 miR-1260b 0.0061 up 1.09 miR-582-5p 0.015 up 1.51 miR-1274a 0.0096 down 1.14 miR-3613-3p 0.008 up 0.91 miR-605 0.039 up 0.93 miR-590-5p 0.011 down 0.47 miR-221 0.009 down 0.84 miR-625 0.032 down 0.83 miR-1290 0.012 down 2.06 miR-4284 0.013 up 1.20 miR-636 0.006 up 0.75 miR-106b 0.012 down 0.50 miR-1181 0.017 up 1.47 miR-877* 0.010 down 1.80 miR-1181 0.013 down 1.35 miR-335* 0.018 down 1.62 miR-95 0.018 down 1.03 miR-93 0.015 down 0.68 miR-15b* 0.018 up 0.90 miR-514b-5p 0.045 up 1.42 miR-3648 0.017 down 1.15 miR-4286 0.018 up 1.58

miR-199b-5p 0.018 up 0.85 miR-98 0.018 down 1.40

miR-3663-3p 0.018 down 1.03 miR-194 0.019 up 1.93

miR-203 0.018 down 1.60 miR-484 0.019 up 1.81

miR-1470 0.019 down 0.75 miR-2278 0.022 down 2.49

miR-10a 0.019 up 1.17 miR-1306 0.023 down 2.08

miR-4271 0.019 down 0.89 miR-324-5p 0.025 down 1.45

miR-652 0.020 up 0.78 miR-129^* 0.025 up 1.50

miR-342-5p 0.020 up 1.14 miR-374c 0.028 up 1.83

miR-4284 0.020 down 0.96 miR-629* 0.030 up 3.63

miR-18b 0.020 down 1.03 miR-4323 0.031 up 1.46

miR-29b-1* 0.020 down 0.62 miR-16-2* 0.035 up 1.55

miR-3646 0.021 down 0.76 miR-33b 0.039 up 2.02

miR-494 0.023 down 1.11 miR-4274 0.043 up 1.81

miR-1280 0.023 down 0.55

miR-214 0.024 up 0.68

miR-143^* 0.030 down 0.72

miR-4274 0.031 down 0.79

miR-1469 0.033 down 0.90

miR-642b 0.034 down 1.12

miR-629^* 0.036 down 1.53

miR-505 0.041 down 0.84

PCA (Figure 1 b) and cluster analysis using the 20 most significant microRNAs (Figure 2b), were able to cluster the breast cancer sera from those belonging to healthy controls.

Further, serum microRNAs differentially expressed according to ER, Her2 and lymph node status could also be identified (Table 4), using the unpaired f-test without correction for FDR. A complete list of serum microRNAs differentially expressed according to ER, Her2 and lymph node status is shown in Table 6, with their nucleotide sequences, obtained from the miRBase (microRNA data base) website, in Table 8.

Inter-platform comparison

Since the serum and tissue samples were extracted and profiled using different kits and platforms, we sought to ascertain that the breast cancer serum and tumour datasets are comparable. Hence, the correlation between the miRVana™ and miRNeasy extraction methods, and the correlation between the Agilent microRNA microarray and LNA™ RT-PCR panels, was examined. All the 742 microRNA detected by LNA™ RT-PCR panels were also included in the microRNA microarray (n=1300). Profiling of the same breast cancer tumour, extracted by mirVana™ or miRNeasy, on the LNA™ RT-PCR panels showed a high degree of correlation between these extraction methods (R²=0.96; Figure 3a). Profiling of the same breast cancer tumour sample on microarray and RT-PCR showed appreciable correlation for the 742 microRNAs common between these platforms (R²=0.61 ; Figure 3b), suggesting that they have comparable dynamic ranges, and that the microarray and RT-PCR datasets are comparable.

Comparison between the breast cancer serum and breast cancer tumour profiles

Interestingly, there were only seven common significant microRNAs that were overexpressed in both breast cancer tumours and sera from breast cancer patients, and one microRNA that was down-regulated in both sample types (Table 7). Another 13 microRNAs were dysregulated in breast cancer sera and tumours, but in opposite directions. Hence, circulating microRNAs are not highly similar to those within breast cancer cells, suggesting that some microRNAs are released into the circulation selectively. TABLE .6. Comp■lete list of significant serum microRNAs associated with clinicopathological features.

ER positivity Her2 positivity Node positivity

Systematic P- RegFold . Systematic P- RegFold . p- * Reg- , Fold name value ulation change name _t value ulation change Systematic name value ulatiqrr. change miR-134 0.0037 up 1.11 miR-1908 0.0049 down 1.01 miR-30d 0.00041 down 0.90 miR-555 0.0047 up 1.05 miR-450a 0.010 down 0.89 miR-29b-1^* 0.0013 down 1.09 mi^'R-596 0.0048 up 1.17 miR-340 0.026 down 1.06 let-7c 0.0014 down 0.71 let-7g^* 0.0056 up 0.97 miR-206 0.029 down 1.01 miR-551b 0.0019 down 1.53 miR-671-5p 0.0079 up 1.82 miR-28-3p 0.031 down 0.70 miR-505 0.0020 down 1.42 miR-602 0.0086 up 1.10 miR-551a 0.038 down 0.98 miR-379 0.0024 down 1.25 miR-579 0.0088 up 1.09 miR-223* 0.042 down 0.82 miR-98 0.0027 down 0.77 miR-181a* 0.0091 up 0.99 miR-671-5p 0.0031 down 1.89 miR-124 0.0094 up 1.34 miR-485-3p 0.0043 down 1.06 miR-23a* 0.0 1 up 0.89 miR-15b 0.0068 down 0.61 miR-576-3p 0.011 up 0.87 miR-622 0.0078 down 1.09 miR-212 0.012 up 0.84 miR-30e 0.0084 down 1.09 let-7a* 0.014 up 0.92 miR-10a 0.0090 down 0.97 miR-188-3p 0.014 up 1.03 miR-124 0.0096 down 1.27 miR-218 0.015 up 0.82 miR-423-5p 0.010 down 0.77 miR-100 0.015 up 1.04 miR- 0b 0.010 down 1.03 miR- 1255b 0.015 up 0.81 m^'iR-30c 0.011 down 0.49 miR-487b 0.015 up 1.17 miR-423-5p 0.0 1 down 0.66 miR-130b^* 0.016 up 0.91 miR-19b 0.012 down 1.06 miR-30e^* 0.016 down 0.79 miR-625* 0.012 down 0.78 miR-577 0.017 up 0.89 miR-615-3p 0.013 down 1.20 miR-362-5p 0.017 up 0.80 miR-411 0.014 down 0.88 miR-221^* 0.017 up 0.85 miR-574-3p 0.016 down 0.62 miR- 1908 0.017 up 0.92 miR-146b-5p 0.016 down 0.63 miR-338-5p 0.018 up 0.99 miR-7 0.016 down 0.86 miR-181d 0.018 up 1.05 let-7a 0.016 down 0.50 miR-517c 0.019 up 1.08 miR-766 0.017 down 0.84 miR-877 0.019 up 0.80 miR-16 0.018 down 0.96 m^'iR-769-5p 0.019 up 0.88 miR-340 0.019 down 1.00 miR-760 0.021 up 1.11 miR-346 0.020 down 0.91 let-7d 0.021 down 0.31 miR-584 0.020 down 0.80 miR-450b-3p 0.021 up 0.96 miR- 197 0.020 down 0.61

ER positivity Her2 positivity ; Node positivity

- Systematic P- RegFold Systematic P- Reg- , _.t Fold V P- RegFold name . value ulation , chanqe ^; name ° value . ulation chanqe ' Systematic name value ¹ ulation change miR-33a^* 0.021 up 0.89 miR-425^* 0.022 down 0.60 miR-593 0.022 up 0.88 miR-628-3p 0.022 down 0.60 miR-92a-1^* 0.022 up 0.80 miR- 179 0.024 down 1.01 miR-548o 0.023 up 0.86 miR-17 0.025 down 0.64 let-7g 0.023 down 0.45 miR-1913 0.025 down 0.85 miR-379* 0.024 up 0.91 miR-342-3p 0.026 down 0.58 miR-21* 0.024 up 0.68 miR-326 0.027 down 1.03 miR-452 0.024 up 1.05 miR-92b 0.028 down 0.69 miR-889 0.025 up 0.96 miR-301a 0.030 down 0.60 miRPIus-A1027 0.025 up 0.90 miR-421 0.030 down 0.60 miR-543 0.026 up 0.96 miR-33b 0.031 down 0.95 miR-1269 0.027 up 0.86 miRPIus-A1031 0.031 down 1.11 miR-30b 0.027 down 0.39 miR-382 0.032 down 1.03 miR-147b 0.028 up 0.83 miR-20b^* 0.032 down 0.78 miR-524-5p 0.028 up 0.82 miR-19a 0.033 down 0.90 miR-371-3p 0.028 up 0.87 miR-185 0.035 down 0.65 miR-888 0.029 up 0.81 miR-502-3p 0.035 down 0.80 miR-937 0.029 up 0.90 miR-23b 0.036 down 0.51 miR-7-r 0.030 up 0.85 miR-493 0.040 down 0.91 miR-185* 0.030 up 0.83 miR-24 0.041 down 0.45 miR-654-3p 0.030 up 0.79 let-7b 0.041 down 0.55 miR-196b* 0.032 up 0.84 miR-140-5p 0.042 down 0.57 miR-642 0.032 up 0.86 miR-27b 0.045 down 0.67 miR-297 0.032 up 0.86 miR-320a 0.045 down 0.63 miR-661 0.033 up 0.83 miR-148a 0.045 down 0.83 miR-671-3p 0.033 up 0.79 miR-122 0.045 down 1.48 miR-1271 0.034 up 0.73 miR-18 a 0.046 down 0.34 miR-93^* 0.035 down 0.57 miR-361-3p 0.046 down 0.65 miR-299-5p 0.036 up 0.85 miR-186 0.046 down 0.44 miR-1539 0.036 up 0.82 miR-760 0.047 down 0.92 miR-595 0.036 up 1.27 miR-126 0.049 down 0.39 miR-650 0.037 up 0.88

miR-421 0.037 up 0.61

miR-29b-r 0.038 up 0.78

miR-643 0.038 up 0.83

TABLE 7. MicroRNAs differentially expressed in both breast cancer sera and breast cancer tumour tissue.

MicroRNA jlation

Serum Tumor

Dysregulated in the same direction

miR-15b up up

miR-16 up up

miR-17 up up

miR-25 up up

miR-93 up up

miR-107 up up

miR-185 up up

miR-199a-5p down

Dysregulated in opposite directions

let-7a up down

let-7b up down

let-7c up down

let-7i up down

miR-lOb up down

m^'iR-130a up down

miR-143 up down

miR-195 up down

miR-214 up down

miR-30a up down

miR-451 up down

miR-142-3p down up

miR-181a down up

Validation of miR-1 , miR-92a, miR-133a and miR-133b

Twenty-three breast cancer associated serum microRNAs, with P-values that remained significant after Bonferroni correction (P≤1.3E-04), were selected for analysis by collinearity statistics. As a result, three sets of microRNAs were derived, in which each set comprised of ten microRNAs with VIF<5 and were hence not impeded by collinearity (Figure 4). Logistic regression was carried out to identify microRNA signatures with the highest diagnostic efficacy for further validation. As a result, three models were identified (Figure 4), which comprise miR-1 , miR-92a, miR- 133a, and miR-133b as the most important diagnostic microRNA markers.

The nucleotide sequences of these miRNAs can be found, for example, in the miRBase website and are represented in Table 8. TABLE 8. Nucleotide sequence listing of microRNAs

The four significant microRNAs identified were then subjected to validation by LNA™ RT-PCR using additional breast cancer sera (n=132) and healthy control sera (n=101). MiR-103 and miR-191 , identified earlier by GenEx software as the best reference genes, were employed for data normalization. Validation results were consistent with data from the sera profiling experiments. As expected, all the four microRNAs were overexpressed in breast cancer sera (Figure 5a-d). The log2-fold changes for miR-1 , miR-92a, miR-133a, and miR-133b were 2.67, 1.32, 2.52, and 2.41 respectively, comparable to those from the sera profiling experiments (3.59, 1.34, 3.29 and 3.41 respectively). The P-values were highly significant (p<1 E-8) for all the four microRNAs (the Mann-Whitney test was used for calculating statistical significance as the Ct values did not follow normal distribution). The resultant ROC curves plotted using the microRNA combinations derived by logistic regression showed areas under the curves (AUCs) of 0.90 to 0.91 (Figure 5e), confirming the diagnostic efficacies of the microRNA models.

Discussion

Among the twenty most significant microRNAs that are differentially expressed in breast cancer tumours identified in this study, several have also been reported to be similarly dysregulated in other studies (Table 2), attesting to the ability of our approach to isolate known differentially expressed microRNAs associated with breast cancer. Among known tumour-derived microRNAs, mir-145 and miR-21 are amongst the most consistently detected (2, 3) and are hence very attractive candidates for clinical application. Furthermore, the observation from this study that among the 20 most significant differentially expressed microRNAs in breast cancer tumours, 13 were down regulated while only 7 were up regulated, is consistent with the notion that tumorigenesis is apparently more associated with down-regulation of tumour-derived microRNAs (2).

Six out of the 20 most significant tumour-derived microRNAs have not been previously reported in literature in association with breast cancer, suggesting that novel microRNA biomarkers of breast cancer can still be identified. The in vitro functionality of these novel microRNAs should be investigated. For example, microRNAs that were up regulated (miR-720, miR- 274b and miR- 260), or down regulated (miR-30c, miR-376c and miR-4324), in breast cancer tumours will be likely candidates for novel oncomirs or tumor suppressors, respectively. The nucleotide sequences of these miRNAs can be found, for example, in the miRBase website and are represented in Table 8 by SEQ ID NOS: 32-37. Published studies on circulating microRNAs have identified a wide diversity of microRNAs between studies. This is not surprising, considering the wide variation of sample types (plasma, serum, or whole blood) (9, 13, 16) and experimental approaches (next generation microRNA sequencing, RT-PCR profiling, or targeted analysis of specific microRNAs) (8, 16) employed in these studies. The use of whole blood will lead to the isolation of microRNAs from many cell types including those within the blood cells, and not just circulating microRNAs, warranting caution when comparing microRNA profiles derived from blood with those from sera or plasma. Serum and plasma are considered equivalent, although microRNA concentration appeared to be higher in serum (20).

To our knowledge this study represents the largest serum and tumour cohort in terms of extensive profiling of microRNAs. In this study, a total number of 108 samples, including 54 sera samples, were profiled. By comparison, two other studies (11 , 16) profiled 20 samples in their marker discovery stage. In addition, the use of appropriate normalization controls is a well-known crucial issue for RT-PCR experiments. The use of a larger profiling cohort in this study facilitated the selection of reference microRNAs empirically. Conversely, the use of a spike-in or a small RNA for data normalization in similar studies ( 1, 16) have sometimes been considered to be problematic due to their suspected instability (21). Since the histopathological records for the samples employed in this study were available, we were also able to identify microRNA signatures that were associated with ER, Her2 or lymph node metastasis. Such signatures may have the potential to be developed as tools to substantiate histological tests in breast cancer. Interestingly, we also identified significant serum microRNAs that were indicative of the tumour's ER, Her2, or lymph node status. Circulating microRNAs associated with ER, progesterone receptor, and Her2 status have been reported in one other study (14). The possibility of a serological test that can augment histological information of a tumour without the need for biopsy is exciting.

In this study, the microRNA profiles between sera and the corresponding matched tumour were largely dissimilar. Notably, miR-195, miR- 43, and miR-10b, observed to be down regulated in breast cancer tumours in this study as well as others (2, 3, 22), were overexpressed in the sera of breast cancer patients. Similarly, 4

Wu et al. (2011) observed that out of 19 microRNAs that were up regulated in breast cancers, only 2 were also up regulated in sera. Studies on breast cancer cell lines have shown that the extracellular and cellular microRNA profiles differ, thus suggesting that microRNAs are selectively released, and do not reflect their abundance in the malignant cells. Furthermore, Cookson et al. (23), upon investigating microRNA changes in plasma after tumour resection, concluded that circulating microRNA profiles reflected the presence of breast cancers but not the profiles of microRNAs within the tumours.

In this study, we employed ROC curve analysis to demonstrate the diagnostic utility of three diagnostic models which were derived from two-marker combinations of miR-1 , miR-92a, miR- 33a and miR- 33b. In a study by Cuk et al, the diagnostic efficacy of four microRNAs (miR-148b, miR-376c, miR-409-3p and miR-801), and that of a three-marker combination (miR-148b, miR-409-3p and miR-801) were evaluated (16). Individually, the microRNAs had AUCs of 0.64 to 0.66 while the three-marker combination had an AUC of 0.69. Relatively higher AUCs of 0.90- 0.91 were obtained for the three diagnostic models evaluated in this study, as well as for each of the four microRNAs individually (AUCs of 0.78 to 0.87; ROC curves not shown).

Intracellular^, miR-1 , miR-92a, miR- 33a and miR- 33b appear to play tumour suppressor roles in cancer cells. It is not known whether these microRNAs have anti-tumorigenic properties in their circulating forms. The presence of circulating microRNAs has only been recognized over the last few years, and the understanding of their biological roles is just emerging. Circulating microRNAs have been proposed to play either oncogenic or tumour suppressive roles (21). For example, exosomes containing microRNAs derived from human melanomas and colorectal carcinomas were able to promote tumour growth and immune escape. Alternatively, immunocytes may secrete tumour suppressive microRNAs so as to block tumor proliferation or promote apoptosis (21 ).

The 20 most significant microRNAs differentially expressed in breast cancer tumours included miR-21 , miR-10b, and miR-145, previously shown to be dysregulated in breast cancer. Only seven microRNAs were overexpressed in both tumours and serum, suggesting that microRNAs may be released into the serum _4g selectively. Interestingly, 16 of the 20 most significant microRNAs differentially expressed in serum samples were novel. MiR-1 , m^"iR-92a, miR-133a and miR- 33b were identified as the most important diagnostic markers, and were successfully validated; receiver operating characteristic curves derived from combinations of these microRNAs exhibited areas under the curves of 0.90-0.91.

As used herein, the term 'comprising' does not preclude the presence of additional steps or substances in the methods and compositions, respectively, of the invention, and is understood to include within its scope the terms 'consisting of and 'consisting essentially of features defined in the claimed invention.

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Claims

CLAIMS:

1. A method of detecting whether a subject has breast cancer, comprising

screening a blood sample from the subject for the presence of at least one microRNA differentially expressed in the diseased state compared to a reference sample representing a subject absent the disease, wherein the at least one microRNA is selected from the group comprising the microRNAs in Table 3, and wherein detection of differential expression of one or more of said microRNAs indicates breast cancer in the subject.

2. The method according to claim 1 , wherein the at least one microRNA is

selected from the group comprising the microRNAs in Table 2.

3. The method according to claim 1 , wherein the at least one microRNA is

selected from the group comprising miR-1, miR-92a, miR-133a and miR- 133b.

4. The method according to claim 3, wherein the nucleotide sequences of the microRNAs are represented by SEQ ID NOS: 1-4, respectively.

5. The method according to any one of claims 1 to 4, wherein the subject's blood sample is screened for the differential expression of at least two microRNAs selected from the group comprising: a) miR-92a and miR-1 , b) miR-92a and miR- 33a, or c) miR-92a and miR-133b.

6. The method according to any one of claims 1 to 5, wherein the expression data are generated using a platform selected from the group comprising cDNA microarrays, oligonucleotide microarrays, microRNA (miRNA) arrays and high throughput quantitative polymerase chain reaction (qPCR), and solution hybridisation/ribonuclease digestion kit.

7. A method of detecting whether a subject has breast cancer with

clinicopathological features of one or more of estrogen receptor (ER) positivity, human epidermal growth factor receptor 2 (Her2) positivity and node positivity comprising screening a blood sample from the subject for the presence of at least one microRNA differentially expressed in the diseased state compared to a reference sample representing a subject absent the disease, wherein the at least one microRNA is selected from: a) microRNAs in Table 6 for ER positivity, b) microRNAs in Table 6 for Her2 positivity, c) microRNAs in Table 6 for node positivity, and wherein differential expression of one or more of said microRNAs indicates ER positivity, Her2 positivity and/or node positivity in the subject.

8. The method according to claim 7, wherein the at least one microRNA is

selected from the group comprising: a) microRNAs in Table 4 for ER positivity, b) microRNAs in Table 4 for Her2 positivity, c) microRNAs in Table 4 for node positivity.

9. The method according to claim 7 or 8, wherein the subject has already been determined to be positive for breast cancer.

10. A kit for screening a blood sample for the presence of microRNA indicative of breast cancer wherein the microRNA is differentially expressed in the diseased state compared to a reference sample representing a subject absent the disease, comprising oligonucleotide primers and/or probes capable of binding to and/or amplifying at least a portion of the nucleic acid sequence, or cDNA derived therefrom, of at least one differentially expressed microRNA selected from the group comprising the microRNAs in Table 3. 1.The kit according to claim 10, wherein the at least one differentially expressed microRNAs are selected from the group comprising the microRNAs in Table 2.

12. The kit according to claim 10, wherein the at least one differentially expressed microRNAs are selected from the group comprising the microRNAs miR- , miR-92a, miR-133a and miR-133b.

13. The kit according to claim 12, wherein the nucleotide sequences of the

microRNAs are represented by SEQ ID NOS: 1-4, respectively.

14. The kit according to claim 10, wherein the kit comprises primers and/or

probes to detect at least two microRNAs selected from the group comprising: a) miR-92a and miR-1, b) miR-92a and miR- 33a, and c) miR-92a and miR-133b.

15. A kit for screening a blood sample for the presence of microRNA indicative of breast cancer which is clinicopathologically estrogen receptor (ER) positive, human epidermal growth factor receptor 2 (Her2) positive and/or node positive, wherein the microRNA is differentially expressed in the diseased state compared to a reference sample representing a subject absent the disease, comprising oligonucleotide primers and/or probes capable of binding to and/or amplifying at least a portion of the nucleic acid sequence, or cDNA derived therefrom, of at least one differentially expressed microRNA selected from the group comprising: a) microRNAs in Table 6 for ER positivity, b) microRNAs in Table 6 for Her2 positivity, c) microRNAs in Table 6 for node positivity, and wherein differential expression of one or more of said microRNAs indicates breast cancer in the subject.

16. The kit according to claim 5, wherein the at least one microRNA is selected from the group comprising: a) microRNAs in Table 4 for ER positivity, b) microRNAs in Table 4 for Her2 positivity, c) microRNAs in Table 4 for node positivity.

17. The kit according to any one of claims 10 to 16, further comprising a nucleotide polymerizing enzyme and a reagent buffer.

18. The kit according to any one of the preceding claims, wherein the kit

comprises a screening platform selected from the group comprising cDNA microarrays, oligonucleotide microarrays, microRNA (miRNA) arrays, high throughput quantitative polymerase chain reaction (qPCR), and solution hybridization/ribonuclease digestion kit and probes.

19. A microarray comprising a substrate and a set of genetic marker molecules attached to the substrate, wherein the marker molecules are polynucleotides represented by oligonucleotide probes which can identify the microRNAs defined in any one of claims 1 to 5.

20. A microarray comprising a substrate and a set of genetic marker molecules attached to the substrate, wherein the marker molecules are polynucleotides represented by oligonucleotide probes which can identify the microRNAs defined in claim 7 or 8.

21.The method or kit according to any one of the preceding claims, wherein the blood sample is a serum sample.

22. The method or kit of any one of the previous claims, wherein the blood

sample is from a subject of Chinese ancestry.

23. A kit for screening a tissue sample for the presence of microRNA indicative of breast cancer wherein the microRNA is differentially expressed in the diseased state compared to a reference sample representing a subject absent the disease, comprising oligonucleotide primers or probes capable of binding to and/or amplifying at least a portion of the nucleic acid sequence, or cDNA derived therefrom, of at least one differentially expressed microRNA selected from the group comprising the microRNAs miR-720, miR-1274b, miR-1260, miR-30c, miR-376c and miR-4324.