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Definitions

• a microRNA-based method for early detection of prostate cancer in urine samples A microRNA-based method for early detection of prostate cancer in urine samples
• the present invention relates to a method for reducing the problem of over-diagnosis in prostate cancer.
• the method can preselect individuals with prostate cancer that should benefit from further invasive diagnostic procedures.
• Prostate cancer is a malignant tumor that originates in the prostate gland it is the most commonly diagnosed and treated malignancy and is one of the leading causes of cancer-related death among males in western countries (1 ). About 1 in 6 men will be diagnosed with prostate cancer over the course of their life and most elderly men eventually develop the disease.
• PC is typically diagnosed on the basis of increased serum prostate specific antigen (PSA) levels followed by histopathological inspection of needle biopsies.
• PSA serum prostate specific antigen
• the use of PSA for PC detection is associated with considerable false positive rates.
• PSA cutoff of 4,0 ng/ml for screening there is a 65% false-positive and a 20% false-negative rate (2).
• PSA levels in the intermediate range area (2-10 ng/mL) presents a gray zone area with very low predictive value.
• such rates have spurred a search for other biomarkers, in particular biomarkers that are found in easy accessible bio-fluids and which are relatively cheap to assess. Until now, we are not aware that this endeavor has been successful.
• microRNAs An emerging new class of potential biomarkers for prostate cancer is the microRNAs (miRs).
• MicroRNAs comprise a class of endogenous small non-coding regulatory RNAs (-22 nt), which control gene expression at the posttranscriptional level in diverse organisms, including mammals (3).
• MicroRNAs are transcribed as long imperfect paired stem-loop primary microRNA transcripts (pri- microRNAs) by RNA polymerase II, and further processed into hairpin precursor microRNAs (pre- microRNAs) by the nuclear RNase III endonuclease, Drosha (4). After export to the cytoplasm by Exportin-5-Ran-GTP, another RNase III endonuclease, Dicer, cleaves the pre-microRNA into a mature -22 nt microRNA duplex (4).
• Mature microRNAs mediate their function while incorporated in the microRNA-induced silencing complex (miRISC). The microRNA guides this complex to perfect/near perfect complementary target mRNAs, leading to either translational inhibition or mRNA degradation (5).
• miRISC microRNA-induced silencing complex
• MicroRNAs are one of the most abundant classes of gene regulatory molecules and the latest release of the miRBase (version 21 ) contains 2588 mature human microRNAs (1881 precursors) http://www.mirbase.org/ (6). Together microRNAs have been estimated to regulate up to two thirds of all human mRNAs. Consequently, microRNAs influence numerous processes in the cell, for instance cell differentiation, cell cycle progression and apoptosis, and deregulation of microRNAs are often connected to human pathologies, including cancer (7). Additionally, some microRNAs appear to be cell type and disease specific and deregulated microRNA expression has been associated with both development and progression of cancer (8). Thus, aberrant microRNA expression has been investigated as a promising potential source of novel biomarkers for early cancer diagnosis (8).
• microRNAs have potential to be used as targets of microRNA-based therapeutics for cancer (9).
• microRNA profiling studies have also reported aberrantly expressed microRNAs in the development and/or progression of PC (10).
• most of the microRNA biomarker studies in PC published to date have used relatively low patient sample numbers and often lack stringent independent clinical validation to confirm the biomarker potential of the identified microRNA candidates.
• Example 2 we performed miRnome profiling of more than 750 of the most abundant microRNAs and selected the 1 83 microRNAs detectable in cell free urine across different disease stages (Example 1 ). From these 183 microRNAs we identified significantly aberrant regulated microRNAs in patients with benign prostate hyperplasia vs. PC patients where urine was collected prior to prostate removal by radical prostatectomy (RP) (Example 2). From this dataset we have identified a small group of miRs which are significantly different expressed in PC relative to non-PC subjects. We furthermore identified a diagnostic classifier consisting of only 2 microRNAs in cohort 1 and evaluated its diagnostic accuracy. This 2 microRNA classifier was successfully validated in an independent cohort 2 (Example 3).
• the 2 miR diagnostic classifier (involving a ratio calculation) demonstrated improved accuracy compared to all single miRNAs tested. Interestingly, all the identified classifiers appeared to have an significantly improved accuracy compared with the total prostate specific antigen (tPSA) test. AUC of this test has been reported to be as low as 0,59, (1 1 ) or even lower (12).
• microRNA biomarkers we included an additional set of 205 PC samples and repeated the classifier building and validation in two new cohorts (cohort 4 and cohort 5, respectively), where the non-cancer samples from cohort 1 and 2 were distributed evenly and randomly between the two cohorts. Also, a different statistical method for assay selection and classifier building was applied, along with more stringent data filtering parameters.
• ratio based classifiers are attractive due to their ability to circumvent the need for normalization assays, thereby reducing the number of assays included in the test.
• the two ratio based classifiers were successfully validated in the validation cohort 5 (example 12).
• these ratio based classifiers were validated in the intended use sub population, where they proved to be even more accurate. Again, both the 3-10miR classifiers and the two ratio based classifiers demonstrated improved accuracy compared to all single miRNAs tested.
• the prostate specific antigen (PSA) method is associated with considerable false negative rates and does not distinguish well between clinically indolent or aggressive tumors, there is a need for novel markers of prostate cancer that can be used on their own or in combination with existing markers.
• the present invention present one set of markers and a method to apply them for preselection patients for PC diagnosis.
• the invention thus concerns an in vitro method for assessing the risk that a subject suffers from prostate cancer, comprising measuring the expression level of at least two miRs selected from group of miRs consisting of: hsa-let-7a-5p, hsa-let-7b-5p, hsa-let-7c-5p, hsa-let-7e-5p, hsa-let-7f- 5p, hsa-let-7g-5p, hsa-miR-100-5p, hsa-miR-106a-5p, hsa-miR-10a-5p, hsa-miR-10b-5p, hsa-miR- 1238-3p, hsa-miR-125b-5p, hsa-miR-1260a, hsa-miR-130a-3p, hsa-miR-132-3p, hsa-miR-135a-5p,
• expression level of said at least 2 miRs, as compared to healthy donors, indicates an increased probability of said subject suffering from prostate cancer.
• the most preferred embodiment of the invention is the in vitro method, wherein the at least two selected 25 miRs are hsa-miR-24-3p, hsa-miR-222-3p, and hsa-miR-30c-5p.
• An almost equally preferred embodiment is the in vitro method, wherein the at least two selected miRs are hsa-miR-24-3p, hsa-miR- 222-3p, and hsa-miR-30a-5p.
• PSA prostate specific antigen
• An vitro diagnostic method which comprise calculating the diagnostic score (S) is an useful aspect of the invention.
• the invention relates to a kit for in vitro assessment of the risk that a subject suffers from prostate cancer, comprising aliquots of the reagents needed for measuring the expression level the level of at least two miRs selected from group of miRs consisting of: hsa-let-7a-5p, hsa-let-7b-5p, hsa- let-7c-5p, hsa-let-7e-5p, hsa-let-7f-5p, hsa-let-7g-5p, hsa-miR-100-5p, hsa-miR-106a-5p, hsa-miR- 40 10a-5p, hsa-miR-10b-5p, hsa-miR-1238-3p, hsa-miR-125b-5p, hsa-miR-1260a, hsa-miR-130a-3p, hsa-miR-132-3p,
• microRNA refers to an about 1 8-25 nucleotide (nt) long, non-coding RNAs derived from endogenous genes.
• MicroRNAs are processed from longer (ca 75 nt) hairpin-like precursors termed pre-miRs.
• MicroRNAs assemble in complexes termed miRNPs and recognize their targets by antisense complementarity. If the microRNAs match 20 100% their target, i.e. the complementarity is complete, the target mRNA is cleaved, and the miR acts like a siRNA. If the match is incomplete, i.e. the complementarity is partial, then the translation of the target mRNA is blocked.
• miRs refer to the nomenclature of miRBase (version 21 ), see tabel 1 1 .
• RNA-molecule refers to the transcription and/or accumulation of RNA- molecules within a cell or in a cell-free biofluid.
• Q-PCR refers to quantitative polymerase chain reaction.
• Q-PCR is highly sensitive method for quantifying the amounts of specific DNA (and RNA) species in a test sample.
• qRT-PCR As 30 quantification of RNA by the PCR technique requires that the RNA is reverse transcribed it is often referred to as "qRT-PCR” or "RT-Q-PCR” to indicate that quantitative PCR is used to quantify specific RNAs.
• qRT-PCR or RT-Q-PCR
• the terms "expression level of a miR”, “miR expression level” and “level of a miR” are used synonymously as a measure of the "amount of a specific miR” that is detected in the sample.
• the “amount of a specific miR” may be expressed in either absolute, relative or normalised measures and refers to values obtained by both quantitative, as well as qualitative methods.
• One particularly preferred measure of the "amount of a specific miR” is the Crossing point (Cp) value obtained by real-time RT-Q-PCR (qRT-PCR) as described below and in the examples, but “amount” may as well be quantified by digital PCR, or various Next Generation Sequencing methods. In certain situations, e.g.
• determinations may be based on the normalised expression levels of the miRs.
• Expression levels are normalised by correcting the absolute expression level of a miR by comparing its expression to the expression of a gene that is constitutively or nearly constitutively expressed. Suitable genes often used for normalisation include housekeeping genes such as the actin gene.
• a collection miRs for normalizing. Typically a collection of 3 or 5 miRNAs can are used to calculate a mean normalization value.
• the preferred 3 miR-normalizer is: hsa-miR-200b- 3p, hsa-miR-27b-3p, and hsa-miR-30b-5p ( see example 8).
• the preferred 5 miR-normalizer is: hsa- miR-20a-5p, hsa-miR-30b-5p, hsa-let-7a-5p, hsa-miR-27b-3p, and hsa-miR-23b-3p (see example 5).
• ROC-curve is short for receiver operating characteristic curve. (ROC) curves are widely used to compare diagnostic tests.
• the expressions "healthy individual”, “healthy donor” and “healthy control” are used synonymously to refer to apparently healthy individuals with no overt indication of prostate cancer in contrast to differentiate them from prostate cancer patients.
• the "cut-off value” is a threshold-value above (or below) which a value, calculated to represent the level of a number of miRs indicate that a test cell sample is from a colon cancer.
• the technical problem underlying the invention is the provision of an improved in vitro method for assessing the risk that a subject suffers from prostate cancer.
• a method which relayed on non-invasive procedures and consequently useful for screening purposes was sought in the hope that it could supplement the widespread PSA test.
• AUC Area Under Curve
• one aspect of the present invention is an in vitro method for assessing the risk that a subject suffers from prostate cancer, comprising measuring the expression level of at least two miRs selected from group of miRs consisting of: hsa-let-7a-5p, hsa-let-7b-5p, hsa-let-7c-5p, hsa-let-7e- 5p, hsa-let-7f-5p, hsa-let-7g-5p, hsa-miR-100-5p, hsa-miR-106a-5p, hsa-miR-1 0a-5p, hsa-miR-10b- 5p, hsa-miR-1238-3p, hsa-miR-125b-5p, hsa-miR-1260a, hsa-miR-130a-3p, hsa-miR-132-3p, hsa-
• one embodiment of the present invention is an in vitro method for assessing the risk that a subject suffers from prostate cancer, comprising measuring the expression level of at least two miRs selected from group of miRs consisting of hsa-miR-141 -3p, hsa-miR-142-3p, hsa-miR- 146a-5p, hsa-miR-151 a-3p, hsa-miR-16-5p, hsa-miR-191 -5p, hsa-miR-200c-3p, hsa-miR-203a, hsa-miR-204-5p, hsa-miR-205-5p, hsa-miR-222-3p, hsa-miR-223-3p, hsa-miR-24-3p, hsa-miR-30a-
• one further embodiment of the invention is an in vitro method for discrimination between PC and non-PC comprising measuring the expression level of at least two miRs selected from group of miRs consisting of hsa- miR-141 -3p, hsa-miR-146a-5p, hsa-miR-16-5p, hsa-miR-200c-3p, hsa-miR-205-5p, hsa-miR-222- 3p, hsa-miR-24-3p, hsa-miR-30a-5p, hsa-miR-30c-5p and hsa-miR-31 -5p.
• an embodiment of the invention is an in vitro method for wherein the least two miRs are selected from group of miRs consisting of the six best ranking miRs, i.e. hsa-miR-146a-5p, hsa-miR- 16-5p, hsa-miR-200c-3p, hsa-miR-205-5p, hsa-miR-30c-5p and hsa-miR-31 -5p is contemplated.
• miRs may be drawn from table 3, e.g. the group of 4 miRs consisting of hsa-miR- 146a-5p, hsa-miR-31 -5p, hsa-miR-24-3p and hsa-miR-30c-5p, which happens to be the 4 miRS with the highest AUC in cohort 1 , the group of 3 miRs consisting of hsa-miR-30c-5p, hsa-miR-222-3p, and hsa-miR-24-3p, and the group of 3 miRs consisting of hsa-miR-30a-5p, hsa-miR-222-3p, and hsa-miR-24-3p.
• an in vitro method for assessing the risk that a subject suffers from prostate cancer comprising measuring the expression level of at least 2, 3, 4, 5, 6, 7 or more, 10 or more or 20 miRs selected from group of miRs consisting of consisting of: hsa-let-7a-5p, hsa-let-7b-5p, hsa-let-7c-5p, hsa-let-7e-5p, hsa-let-7f-5p, hsa-let-7g-5p, hsa-miR-100-5p, hsa-miR-106a-5p, hsa- miR-10a-5p, hsa-miR-10b-5p, hsa-miR-1238-3p, hsa-miR-125b-5p, hsa-miR-1260a, hsa-miR-130a- 3p, hsa-miR-132-3p, hsa
• said at least 2, 3, 4, 5 or more, 10 or more or 20 miRs are selected from group of miRs consisting of: hsa-miR-10b-5p, hsa-miR-135b-5p, hsa-miR-140-3p, hsa- miR-141 -3p, hsa-miR-142-3p, hsa-miR-146a-5p, hsa-miR-151 a-3p, hsa-miR-16-5p, hsa-miR-191 -5p, hsa-miR-19a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a, hsa-miR-204-5p, hsa-miR-205- 5p, hsa-miR-20a-5p, hsa-
• two of the best classifiers comprise only three selected miRs namely 1 ) hsa-miR-30c-5p, hsa-miR-222-3p, and hsa-miR-24-3p, and 2) hsa-miR-30a-5p, hsa-miR-222-3p, and hsa- miR-24-3p (see example 1 1 , 12, and 13) making these two signatures preferred embodiments of the invention.
• the terms "expression level of a miR”, “miR expression level” and “level of a miR” are used synonymously as a measure of the “amount of a specific miR” that is detected in the sample.
• the “amount of a specific miR” may be expressed in either absolute, relative or normalised measures and refers to values obtained by both quantitative, as well as qualitative methods.
• determinations may be based on the normalised expression levels of the miRs.
• Expression levels are normalised by correcting the absolute expression level of a miR by comparing its expression to the expression of a gene that is constitutively or nearly constitutively expressed. Suitable genes often used for normalisation include housekeeping genes such as the actin gene. Accordingly, in one embodiment of the invention the expression levels are normalized expression levels.
• the top 3 or top 5 most stable miRNAs from Normfinder is used to calculate a mean normalization value for each sample.
• the normalization is performed by calculating a mean normalization value of 3 miRs being: hsa-miR-200b-3p, hsa-miR-27b-3p, and hsa-miR-30b-5p or 5 miRs being : hsa-miR-20a-5p, hsa-miR-30b-5p, hsa-let-7a-5p, hsa-miR-27b-3p, hsa-miR-23b-3p .
• the 3 miR-normaliser is preferred.
• tPSA total prostate specific antigen
• Example 4 is performed on urine samples obtained after prostatic massage.
• the 2-miR classifier (miR-30c, miR-31 -5p) possess a diagnostic power that by far outrange the diagnostic power of the standard total prostate specific antigen (tPSA) test.
• the 2-miR classifier performed surprisingly well with an area (AUC) under the ROC curve of 0,79 (CI: 0,69 to 0,90, p ⁇ ⁇ 0,0001 ) and a sensitivity of 72,9% and specificity of 84,8%, using a the cut-off value established in cohort 1 .
• the urine sample is from a subject who have been subjected to prostatic massage immediately before the urine was sampled.
• a diagnostic assay in practice it is advantageous to use the assay values to calculate a diagnostic diagnostic score (S) allowing one to define cut-off values and to differentiate between cancer and non-cancer samples based on the diagnostic diagnostic score.
• one embodiment of the present invention is a method, wherein the assessment of the risk that a subject suffers from prostate cancer involves detecting the level of said at least two miRs in said sample and calculate a diagnostic diagnostic score (S) based on a dataset comprising the expression level data of said at least two miRs.
• the level of miRs may conveniently be quantified by Quantitative real-time Reverse Transcriptase mediated Polymerase Chain Reaction method, RT-QPCR (13).
• One particularly preferred measure of the "amount of a specific miR” is the Crossing point (Cp) value obtained by real-time qRT-PCR.
• Another preferred measure of the "amount of a specific miR” is the “threshold cycle value (Ct)” value likewise obtained by real-time qRT-PCR as described in the examples.
• the Cp and the Ct measures of the "amount of a specific miR” provide roughly similar measures, see (13). Whether to choose Cp or Ct is largely a matter of choice of the machine the assay tied to and performed on. If the amplification is performed in a LightCycler® 480 Real-Time PCR System using the Roche LC software the amount of a specific miR is expressed by the Cp.
• the Cp-value is related to the level of e.g. a specific miR, by the relation:
• Cp(miRx) designates the Cp-readout from real-time QPCR instrument specifically detecting one specific miR called miRx.
• Example 2 describes such an assay in details.
• a useable estimator - diagnostic diagnostic score (S) - for the 4-miR classifier is a linear regression model, such as:
• an estimator - diagnostic diagnostic score (S) - for the 2-miR classifier is X x Cp(miR31) + Y x Cp(miR30c) where the coefficients X and Y are determined by the regression-analysis. Both linear and other types of regression are contemplated.
• the diagnostic diagnostic score (S) is calculated as follows:
• C is the threshold cycle value (Ct) or the crossing point value (Cp), and wherein X and Y are coefficients determined by linear or another type of regression.
• machine learning is referred to a process which take advantage of computer algorithms that improve automatically through experience, in the art this process of improving the algorithms is often referred to as "training”.
• Machine learning can be used to discover general rules in large data sets, machine learning can e.g. be used to extract clinical informative data from a dataset comprising miR expression in cancer and non-cancer samples of the prostate.
• a general treatise of the concept of machine learning can be found in (21 ) which hereby is incorporated herein by reference. Accordingly in one embodiment of the invention the algorithm for calculating the diagnostic diagnostic score (S) was reached applying machine learning.
• tPSA total prostate specific antigen
• the diagnostic score S is calculated as a ratio of the expression level of miRs, e.g. the ratio of the expression level of hsa- miR-24-3p and hsa-miR-222-3p vs. the expression level of hsa-miR-30a-5p, the ratio of the expression level of hsa-miR-24-3p and hsa-miR-222-3p vs. the expression level of hsa-miR-30c-5p, or the ratio of the expression level of hsa-miR-30c-5p vs. the expression level of hsa-miR-31 -5p.
• the diagnostic diagnostic score (S) is calculated as:
• C is the threshold cycle value (Ct) or the crossing point value (Cp) obtained by quantitative real-time PCR (qRT-PCR) for hsa-miR-24-3p, hsa-miR-222-3p and hsa-miR-30a-5p, respectively. or as:
• C is the threshold cycle value (Ct) or the crossing point value (Cp) obtained by quantitative real-time PCR (qRT-PCR) for hsa-miR-24-3p, hsa-miR-222-3p and hsa-miR-30c-5p, respectively.
• a particularly interesting embodiment of the invention relate to the group of patients characterized by that a previous standard prostate specific antigen (PSA) measurement has indicated that their serum PSA level is below 10 ng/mL.
• PSA prostate specific antigen
• hsa-miR-30c-5p, hsa-miR-222-3p, and hsa-miR-24-3p classifier with the diagnostic score (S) C(hsa-miR-24-3p) + C(hsa-miR-222-3p) - (2 ⁇ C(hsa-miR-30c-5p)), wherein "C " is the threshold cycle value (Ct) or the crossing point value (Cp) obtained by quantitative real-time PCR (qRT-PCR) specific for the miR, is the preferred embodiment of the invention.
• the invention relate to a method of treating a patient in need of prostate cancer treatment, the method comprising: performing a diagnostic test according to any of the preceding claims to determine if the patient have an increased probability of suffering from prostate cancer, and selecting an appropriate therapy for the patient based on this Information.
• a further aspect of the invention is a kit of parts for in vitro assessment of the risk that a subject suffers from prostate cancer, comprising aliquots of the reagents needed for measuring the expression level the level of at least two miRs selected from group of miRs consisting of: hsa-let-7a-5p, hsa-let-7b-5p, hsa-let-7c-5p, hsa-let-7e-5p, hsa-let-7f-5p, hsa-let-7g-5p, hsa-miR-100-5p, hsa-miR-106a-5p, hsa- miR-10a-5p, hsa-miR-10b-5p, hsa-miR-1238-3p, hsa-miR-125b-5p, hsa-miR-1260a, hsa-miR-130a- 3p, hsa-miR-132-3p,
• kits wherein the at least two miRs are selected from the group of miRs consisting of: hsa-miR-141 -3p, hsa-miR-142-3p, hsa-miR-146a-5p, hsa-miR-151 a-3p, hsa-miR-16-5p, hsa-miR-191 -5p, hsa-miR-200c-3p, hsa-miR-203a, hsa-miR-204-5p, hsa-miR-205-5p, hsa-miR-222-3p, hsa-miR-223-3p, hsa-miR-24-3p, hsa-miR-30a-5p, hsa-miR-30c-5p, hsa-miR-31 -3p, hsa-miR-31 -3p, hsa-m
• the at least two miRs are selected from the group of miRs consisting of: hsa-miR-141 -3p, hsa- miR-16-5p, hsa-miR-222-3p, hsa-miR-24-3p, hsa-miR-31 -3p, hsa-miR-31 -5p, and hsa-miR-331 -3p, or wherein the at least two miRs are selected from the group of miRs consisting of: hsa-miR-146a-5p, hsa-miR-16-5p, hsa-miR-200c-3p, hsa-miR-205-5p, hsa-miR-30c-5p and hsa-miR-31 -5p.
• kits wherein the at least two selected miRs are: hsa-miR-30c-5p, hsa-miR-222-3p, and hsa-miR-24-3p is preferred.
• microRNAs have been shown to be stabilized and protected from RNase degradation by inclusion in various protein complexes and membranous particles such as exosomes or microvesicles.
• the miRs are extracted from an exosome preparation of the urine sample.
• a preferred kit according to the invention comprises reagents needed to obtain the exosome preparation of the urine sample.
• Figure 1 The ROC curves for the 2 miR classifier composed of the ratio of miR-31 -5p vs. miR-30c.
• Fig 1 A is the ROC curve for cohorl (discovery cohort)
• fig 1 B is the ROC curve for cohort 2 (validation cohort, Example 3)
• fig 1 C is the ROC curve for cohort 3 (validation cohort, Example 4).
• Figure 3 2 miR classifier for PC diagnosis applied on bladder cancer vs. controls (miR31 -5p - miR30c-5p).
• FIG. 4 The UniRT method.
• Figure 5 ROC curve of 3 miR panel classifier in discovery and validation cohorts (cohort 4 and 5).
• Figure 6 ROC curve of 7miR panel classifier (hsa-miR-141 -3p, hsa-miR-16-5p, hsa-miR-222-3p, hsa-miR-24-3p, hsa-miR-31 -3p, hsa-miR-31 -5p, and hsa-miR-331 -3p) in the discovery and validation cohorts (cohort 4 and 5).
• 7miR panel classifier hsa-miR-141 -3p, hsa-miR-16-5p, hsa-miR-222-3p, hsa-miR-24-3p, hsa-miR-31 -3p, hsa-miR-31 -5p, and hsa-miR-331 -3p
• Figure 7a ROC curve of 3miR_1 ratio based classifier (hsa-miR-24-3p, hsa-miR-222-3p, hsa-miR- 30a-5p) in the discovery cohort, cohort 4.
• Figure 7b ROC curve of 3miR_2 ratio based classifier (hsa-miR-24-3p, hsa-miR-222-3p, hsa-miR- 30c-5p) in the discovery cohort, cohort 4.
• Figure 8a ROC curve of 3miR_1 (hsa-miR-24-3p, hsa-miR-222-3p, hsa-miR-30a-5p) ratio based biomarker signature in validation cohort (cohort 5).
• Figure 8b ROC curve of 3miR_2 (hsa-miR-24-3p, hsa-miR-222-3p, hsa-miR-30c-5p) ratio based classifier in validation cohort (cohort 5).
• Figure 9a ROC curve of 3miR_1 ratio based biomarker signature in sub-set of validation cohort 5 ( ⁇ 10ng/mL).
• Figure 9b ROC curve of 3miR_2 ratio based biomarker signature in sub-set of validation cohort (cohort 5).
• Figure 11 ROC curve of 3miR_2 (hsa-miR-24-3p, hsa-miR-222-3p, hsa-miR-30c-5p) ratio based classifier validation in cohort 3.
• NM non-malignant samples
• BPH Benign Prostate Hyperplasia
• RP curatively intended radical prostatectomy
• CRPC castration resistant PC
• RNA from exosomes were extracted from 3 ml urine supernatant using the miRCURYTM RNA Isolation Kit (Catalog no. 300102) - Exosome Isolation in combination with miRCURYTM RNA Isolation Kits - Cell & Plant (Catalog no. 3001 10), both from Exiqon, in accordance to manufacturer's instructions. The purified RNA was stored at -80 °C until use.
• microRNA Ready-to-Use PCR Human panel l+ll, V3,R, in 384-well PCR plates, catalog no. 20361 1 /203612
• assaying 752 different microRNAs 752 different microRNAs. Note that what was previously referred to as "hsa-miR-210" relating to the nomenclature in miRBase (version 19), should be referred to as “hsa-miR- 210-3p" to take the nomenclature of miRBAse (version 21 ) into account.
• the training cohort consisted of 8 non-malignant samples (NM; from BPH patients (controls), 122 samples from patients who have undergone curatively intended RPs of histologically verified clinically localized PC 5 Castration Resistant Prostate Cancer (CRPC). See table 2 for overview of sample composition of cohorts.
• Table 2 Patient composition of cohorts 1 and 2
• RNA from exosomes were extracted from 3 ml urine supernatant using the miRCURYTM RNA Isolation Kit (Catalog no. 300102) - Exosome Isolation in combination with miRCURYTM RNA Isolation Kits - Cell & Plant (Catalog no. 3001 10), both from Exiqon, in accordance to manufacturer's instructions. The purified RNA was stored at -80 °C until use.
• microRNA levels were analyzed using a microRNA Ready-to-Use PCR, Pick-&-Mix microRNA PCR Panel (custom made; item no 20381 ) consisting of the 183 selected microRNA assays (Example 1 ) and 4 spike-in controls, 384-well.
• ExiLENT SYBR® Green master mix (Catalog no. 203421 ).
• Negative controls excluding RNA template from the reverse transcription reaction were performed and profiled in parallel.
• the amplification was performed in a LightCycler® 480 Real-Time PCR System (Roche).
• the amplification curves were analyzed using the Roche LC software, both for determination of quantification cycle (Cp) value and for melting curve analysis. MicroRNAs for which all Cp values exceeded 37 in all samples were excluded from further analysis. In order to exclude any low quality samples, any sample with a detected number of miRNAs below 100 was removed.
• the amplification efficiency was calculated using algorithms similar to the LinReg software. All assays were inspected for distinct melting curves and the Tm was checked to be within known specifications for the assay. Furthermore assays must be detected with 3 Cps less than the negative control, and with Cp ⁇ 37 to be included in the data analysis. Data that did not pass these criteria were omitted from any further analysis. Cp was calculated as the 2 nd derivative. In order to exclude any low quality samples, any sample with a detected number of miRNAs below 100 was removed. Using Norm Finder the best normalizer was found to be the average of assays detected in all samples.
• microRNAs were found to be significantly regulated (P ⁇ 0,01 ) based on Wilcoxon rank test, the top 20 being: hsa-miR-135b-5p, hsa-miR-140-3p, hsa-miR-141 -3p, hsa-miR-146a-5p, hsa-miR- 16-5p, hsa-miR-19a-3p, hsa-miR-200c-3p, hsa-miR-205-5p, hsa-miR-20a-5p, hsa-miR-222-3p, hsa- miR-23a-3p, hsa-miR-24-3p, hsa-miR-27b-3p, hsa-miR-30a-3p, hsa-miR-30a-5p, hsa-miR-30b-5p, hsa-miR-30
• the diagnostic potential of a miRNA can be assessed by receiver operating characteristic (ROC), using the area under the curve (AUC) as a measure for performance.
• ROC receiver operating characteristic
• AUC area under the curve
• the validation cohort consisted of 47 non-malignant samples (controls: BPH patients), 98 samples from patients with undergoing curatively intended RP for s of histologically verified clinically localized PC (RP) and 3 Castration Resistant Prostate Cancer (CRPC). See table 2 for overview of sample composition of cohorts Method
• microRNAs were found to be significantly regulated (P ⁇ 0,01 ) based on Wilcoxon rank test adjusted for multiple testing (Benjamin Hochberg (BH) method), the top 20 being: hsa-miR-141 -3p, hsa- miR-142-3p, hsa-miR-146a-5p, hsa-miR-151 a-3p, hsa-miR-16-5p, hsa-miR-191 -5p, hsa-miR-200c-3p, hsa-miR-203a, hsa-miR-204-5p, hsa-miR-205-5p, hsa-miR-222-3p, hsa-miR-223-3p, hsa-miR-24-3p, hsa-miR-30a-5p, hsa-miR-30c-5p, hsa-miR-
• miRNA with strongest diagnostic potential for discrimination between cancer and controls is miR-31 -5p and miR-146,
• Table 3 Significantly regulated miRNA in both cohort 1 and 2, MiRs are ranked according to the p-value based on Wilcoxon rank test in the validation cohort after adjustment for multiple testing by the Benjamin Hochberg (BH) method.
• BH Benjamin Hochberg
• AUC designate the area under the receiver operator characteristic curve.
• the diagnostic potential of the 2-miR (miR-31 -5p/miR30c) classifier with the cut-off established in cohort 1 was validated in cohort 2 using ROC analysis, with a high diagnostic accuracy; defined by the area under the ROC curve, of 0,86 (95% CI: 0,79 to 1 ,00, p ⁇ 0,0001 ) and a sensitivity of 72,5% and specificity of 84,1 % was found.
• RNA samples were provided by Department of Oncology, The Medical School, Sheffield, UK.
• the validation cohort 3 consisted of 34 non-malignant control samples, 36 samples from patients with localized prostate cancer, 20 patients with advanced PC.
• RNA was extracted directly from the cell free urine fraction (supernatant) using the mirVanaTM miRNA Isolation Kit (Catalog number: AM1560) from Ambion, Life Technology, in accordance to manufacture instructions.
• the purified RNA was stored at - 80°C until use.
• the 2 miR classifier (miR31 -5p, 30c) classified cases from controls with a sensitivity and specificity of 72,9% and 84,4%, respectively, using a the cut-off value established in cohort 1 and with an area under the ROC curve of 0,79 (CI : 0,69 to 0,90, p ⁇ ⁇ 0,0001 ), see tab 4 and fig 1 c.
• miR classifier AUC 95% CI b AUC 95% CI b AUC 95% CI b miR-31-5p/miR-30c 0,92 0,80 to 1 ,00 0,86 0,79 to 0,100 0,79 0,69 to 0,90
• Table 4 Receiver operator curves were plotted for the miR-31 -5p/miR-30c classifier in three cohorts see fig 1 a, b and c, and the Area under the receiver operator characteristic curve is calculated. The 95% confidence intervals (CI) are indicated in table.
• cohort 3 is fundamentally different from cohort 1 and 2; difference of specimen collection, processing and normalization strategy, the diagnostic potential of miR31 was confirmed.
• a panel of miRNAs a classifier that does not rise or fall on the expression level of a single miRNA.
• To increase the statistical power of the identification of miRNA with diagnostic potential we merge cohort 1 and cohort 2 to one large discovery cohort. The merged data set has sufficient power for statistical approach for building of classifiers.
• the merged cohort (cohort 1 , example 1 and cohort 2, example 2) consisted of 48 non-malignant samples (NM; from BPH patients (controls), 205 samples from patients with curatively intended RPs of histologically verified clinically localized PC and 8 Castration Resistant Prostate Cancer (CRPC).
• Each miRNA classifier were constructed by Monte Carlo sampling, drawing from 2 to 20 miRNAs at random from the top 1 00 of the most significant miRNAs between two groups (based on a Benjamini-Hochberg adjusted Wilcoxon test p-value) (17). This was done 1 million times, and each classifier was tested for performance, using ROC AUC or log-rank test p-value as score for diagnostic and prognostic classifiers, respectively. The classifier was then reduced by leave-one-out
• a cutoff value was selected for a given miRNA based on the best threshold (Youden's J statistic (Youden, 1950)) (17) from a ROC curve as determined by the pROC package in R. Then, for each patient the
• AACq is a measure of the relative difference in gene expression between controls (BPH) and cases (RP) determined by the 2-AACt method, see (20).
• the 2 miR classifier (miR-31 -5p/miR-30c) showed a diagnostic accuracy based on ROC curve analysis of 0,9 (AUC).
• a single miRNA, or a ratio-based biomarker can perform well , it may increase the robustness of the classifier to use a panel of miRNAs.
• the 20 miRNA classifier consisted of: hsa-miR-132- 3p, hsa-miR-135b-5p, hsa-miR-187-3p, hsa-miR-194-5p, hsa-miR-222-3p, hsa-miR-24-3p, hsa- miR-363-3p, hsa-miR-495-3p, hsa-miR-548c-5p, hsa-miR-96-5p, hsa-miR-135a-5p, hsa-miR-140- 3p, hsa-miR-141 -3p, hsa-miR-221 -3p, hsa-miR-490-3p, hsa-miR-148a-3p, hsa-miR-16-5p, hsa- miR-20a-3p, hsa-mi
• the diagnostic potential of a miRNA can be assessed by receiver operating characteristic (ROC), using the area under the curve (AUC) as a measure for performance.
• ROC receiver operating characteristic
• AUC area under the curve
• miRNA-31 -5p displays the highest potential, having an AUC of 89,9% in the discovery cohort. Potentially, several miRNAs may supplement each other in a diagnostic test, thereby performing better than either single miRNA would.
• Table 7 List of miRNAs that performs well together with miRNA-31 -5p. "Expressed” is a measure for in how many percentages of samples the given miRNA is found to be expressed. MiRs are ranked according to the product of AUC and Expression.
• microRNAs with significant aberrant expression in bladder cancers vs. controls only three microRNAs overlapped with those identified in PC, being: hsa-miR-16-5p, hsa- miR-320a and hsa-miR-10b-5p.
• the ROC plot for the strong PC diagnostic 2-miR classifier is shown in figure 3 for Bladder cancer vs. Controls, showing very low discriminating value.
• the UniRT protocol is a two-step protocol.
• the miRs present in a sample are firstly poly-A-tailed using a poly(A) polymerase, which adds adenine residues to the 3'-end of RNA molecules.
• an extension primer which has a poly-T-core nucleotide sequence, a 3'-end VN- degenerate motif and a 5'-end tail, is annealed to the poly-A-tailed miRs through hybridization with the VN-poly-T-sequence of the extension primer.
• the extension primer is extended in a reverse transcription reaction using the miR as template.
• the resulting primary extension product is composed of the extension primer and the newly synthesized cDNA, which complementary to the miRs in the sample.
• a miR-specific PCR is carried out.
• a miR-specific forward primer is annealed to 3'-end of the newly synthesized cDNA and the upper-strand synthesis is carried out by extending the forward primer in a DNA-polymerization reaction using the primary extension product as template.
• a miR-specific reverse primer composed of a miR-specific 3'-end sequence, a poly-T-stretch and a 5'- end tail is then hybridized to the upper-strand and the lower-strand is synthesized by extension of the reverse primer.
• the LNA's help to ensure a specific and efficient annealing of the primers to their respective targets.
• MicroRNAs for which all Cq values exceeded 37 in all samples were excluded from further analysis. In order to exclude any low quality samples, any sample where the total number of expressed miRNAs falls below 50% of the potential maximum was removed. To select only robustly expressed assays, microRNA assays that are expressed in less than 80% of the samples were removed and assay with an average Cq value above 35 was removed as well.
• Normalization of cohort 4 was performed by calculating a mean normalization value of the top three most stably expressed assays identified by the NormFinder algorithm (40): hsa-miR-200b-3p, hsa-miR- 27b-3p, hsa-miR-30b-5p.
• X x), i.e the probability that the patient has cancer given the estimated Cq value, can be expressed by the following model:
• microRNA assays were selected by LASSO (Least Absolute Shrinkage and Selection Operator) regression and coefficients are estimated by the maximum likelihood method. Selection of microRNA panels
• Lasso regression was used as the statistical method to identify microRNA panels with optimal diagnostic potential. In order to select from microRNA with a sufficient signal to noise ratio, only miRNAs displaying at least 1 , 75-fold change in the discovery set between normal and PCa subjects were included. As logistic regression requires a complete dataset, multiple imputation technique was used to fill in missing values. The imputation was done using the R package "mice".
• lasso regression was performed by using the R package glmnet to find which predictors to use.
• Lasso regression introduces a penalty ( ⁇ ) which shrinks the coefficients to zero when ⁇ is increased.
• ⁇ a predictor
• the more a predictor (a microRNA assay) contributes to the model the greater the ⁇ -value has to be before its ⁇ becomes zero (See http://www-bcf.usc.edu/ ⁇ gareth/ISL/ page 21 9 for a full explanation of lasso regression).
• the coefficients were shrunk as a result of increasing ⁇ and the deviance in a cross validation using cv.glmnet, indicated that using 3 or 7 predictors (microRNAs) appeared to be optimal.
• the diagnostic potential of microRNA panels were assessed by ROC analyses using the p-value from the logistic regression model.
• microRNAs The top 1 0 ranked microRNAs is shown in table 9, The LASSO regression indicated that using 3 or 7 predictors (microRNAs) appeared to be optimal.
• Table 9a Rank of microRNA and list of identified panels (based on Lasso regression). The classifiers were shrunk from 10 microRNAs (miR10) to one microRNA (miR1).
• the 'x' indicates that the microRNA is included in the given miR biomarker panel.
• panel miR3 is hsa-miR-31 -5p, hsa-miR-141 -3p, and hsa-miR-24-3p
• Table 9b Diagnostic accuracy of the biomarker panels (miR1 - miR10) given by AUC values; the area under the Receiver Operator Curve (ROC) in the discovery/training set, the full validation set and the intended use sub-population.
• ROC Receiver Operator Curve
• the single microRNA assays with highest diagnostic accuracy was hsa-miR-31 -5p with an AUC of 0,92,
• PSA prostate specific antigen
• the subsequent two microRNAs both added significant accuracy to the classifier: hsa- miR-31 -5p, hsa-miR-141 -3p, hsa-miR-24-3p, reaching an AUC of 0,95 (cut-off: 0,94).
• the microRNA panel with highest diagnostic accuracy was a 7 miR classifier: hsa-miR-31 -5p, hsa- miR-141 -3p, hsa-miR-24-3p, hsa-miR-31 -3p, hsa-miR-16-5p, hsa-miR-222-3p, hsa-miR-331 -3p, with an area under the ROC curve (AUC) of 0,96 (cut-off: 0,94). Adding more assays did not increase the discriminative power further.
• NM non-malignant samples
• BPH BPH patients
• 205 samples from patients with curatively intended RPs of histologically verified clinically localized PCa (table 8).
• the single microRNA classifier (hsa-miR-31 -5p) showed a diagnostic accuracy of 0,81 in the validation cohort, whereas the each of the two subsequent microRNAs (hsa-miR-141 -3p and hsa-miR-24-3) added a considerable increase to the diagnostic accuracy reaching an AUC of 0,86 (see also Figure 5).
• the diagnostic miR classifiers no. 1 to 10 were successfully validated in an independent validation cohort using cut-off values established in cohort 4, Several of the microRNA panels were able to predict a positive outcome of a biopsy test with a diagnostic accuracy above 0,86, The most optimal microRNA biomarker panel was the 7-miR classifier. For a diagnostic test, low numbers of assays are preferable due to price and complexity of the diagnostic test.
• the 3 miR (hsa-miR-31 -5p, hsa- miR-141 -3p, and hsa-miR-24-3p) and 4 miR (hsa-miR-31 -5p, hsa-miR-141 -3p, hsa-miR-24-3p, and hsa-miR-31 -3p) classifiers are promising biomarker candidates.
• the biomarker panels are intended to be used to support the clinical decision; whether or not to biopsy, in patients with a previous PSA test result in the 'grey zone area', below 10 ng/mL.
• the aim of the study was to validate if the identified biomarker panels could be applied to the sub population of patients with PSA levels below 10ng/ml_, and still discriminate between benign and cancerous conditions with high diagnostic accuracy.
• the cohort was a subset of the validation cohort. It consisted of 1 9 non-malignant samples (NM; from BPH patients (controls), and 83 samples from patients with curatively intended RPs of histologically verified clinically localized PCa with PSA levels below or equal to 10 ng/mL (table 8).
• the diagnostic potential of the classifiers defined in cohort 4 were validated in the subpopulation of (PSA ⁇ 10) cohort 5 using ROC analysis. Results are shown in table 9,
• the single microRNA classifier (hsa-miR-31 -5p) showed a diagnostic accuracy of 0,82 in the validation cohort, whereas the three microRNA (3-miR) classifier showed a considerable increase in diagnostic accuracy to an AUC of 0,88 in line with the discovery study in example 8,
• the 7-miR microRNA panel that showed the highest diagnostic accuracy in discriminating between normal and cancerous conditions in the discovery cohort was also found to hold the highest discriminative power in the validation cohort with an AUC of 0,89, Adding more microRNAs to the panel did not improve the biomarker panel, as was observed in the discovery cohort.
• a ratio based microRNA biomarker signature would have the considerable advantage of increasing the detection difference by circumventing the need for normalization and thereby reduce the number of assays needed to obtain diagnostic accuracy.
• the aim of this study was to discover ratio based classifiers with diagnostic potential for discriminating between benign and cancerous conditions.
• the scoring algorithm for a 3-miR ratio-classifier can be expressed
• Xi - X3 are a set of experimentally determined values (measurements) relating to the expression level of 3 different miRs.
• Cq value for the particular microRNA assays in the classifier is used as a measure of expression level.
• the score can also be written as:
• the selected ratio-based classifiers circumvent the need for normalization:
• the diagnostic potential of the two 3-miR ratio based classifiers hsa-miR-24-3p, hsa-miR-222-3p, hsa- miR-30a-5p and hsa-miR-24-3p, hsa-miR-222-3p, hsa-miR-30c-5p with the cut-off established in cohort 1 was validated in cohort 2 using ROC analysis.
• the diagnostic accuracy defined by the area under the ROC curve for the two classifiers were 0,87 (sensitivity 0,89, specificity 0,76, cut-off >2) and 0,89 (Sensitivity 0,78, specificity 0,95, cut-off >6), respectively (Figure 8).
• the diagnostic potential of the two identified ratio based microRNA classifiers were successfully validated in an independent validation cohort using a similar cut-off value as established in the discovery cohort.
• the accuracy of the diagnostic 3 miR classifiers was high with AUC of more than 0,87.
• a diagnostic score S may be calculated for both 3 miR classifiers and used to decide on the further treatment of the patient.
• the aim of the study is to validate if the identified ratio based classifiers can be applied to intended use sub population; patients with PSA levels similar to or below 10ng/ml_, and still discriminate between benign and cancerous subjects with high diagnostic accuracy.
• the 3 miR classifiers (hsa-miR-24-3p, hsa-miR-222-3p, hsa-miR-30a-5p and hsa-miR-30a-5p and hsa- miR-24-3p, hsa-miR-222-3p, hsa-miR-30c-5p) were validated in the sub-population (PSA ⁇ 10 ng/mL) with a diagnostic accuracy (AUC) of 0,88 (sensitivity 0,79, specificity 0,88) and 0,91 (sensitivity 0,88, specificity 0,95), respectively (Figure 9).
• the diagnostic biomarker potential of the two 3 miR ratio based classifiers was successfully validated in the intended use sub-population (PSA ⁇ 1 0 ng/mL) of cohort 2 with high diagnostic accuracy of 0,88 and 0,91 , respectively.
• the diagnostic cut-off values to be applied on clinical samples would be between 0,5 and 2 (dynamic range: [-3, +6] for the first 3 miR classifier (hsa-miR-24-3p, hsa-miR-222- 3p, hsa-miR-30a-5p and hsa-miR-30a-5p) and between 4 and 6,5 (dynamic range: [1 ,10] for the second 3 miR classifier (hsa-miR-24-3p, hsa-miR-222-3p, hsa-miR-30c-5p).
• a diagnostic score S may be calculated for both 3 miR classifiers and used to decide on the further treatment of the patient.
• One of the 3-miR classifiers (hsa-miR-24-3p, hsa-miR-222-3p, hsa-miR-30c-5p) classified benign from cancerous conditions with an accuracy (area under the ROC curve) of 0,79 (CI: 0,68 to 0,87, p ⁇ ⁇ 0,0001 , sensitivity and specificity of 64% and 84 %, respectively) see figure 1 1 .
• the diagnostic potential of the other ratio based 3miR classifier (hsa-miR-24-3p, hsa-miR-222-3p, hsa-miR-30a-5p) could not be confirmed (AUC of 0,66).
• hsa-let-7a-5p 1 UGAGGUAGUAGGUUGUAUAGUU hsa-let-7b-5p 2 UGAGGUAGUAGGUUGUGUGGUU hsa-let-7c-5p 3 UGAGGUAGUAGGUUGUAUGGUU hsa-let-7e-5p 4 UGAGGUAGGAGGUUGUAUAGUU hsa-let-7f-5p 5 UGAGGUAGUAGAUUGUAUAGUU hsa-let-7g-5p 6 UGAGGUAGUAGUUUGUACAGUU hsa-miR-100-5p 7 AACCCGUAGAUCCGAACUUGUG hsa-miR-106a-5p 8 AAAAGUGCUUACAGUGCAGGUAG hsa-miR-107 9 AGCAGCAUUGUACAGGGCUAUCA hsa-miR-10a-5p 10 UACCCUGUAGAUCCGAAUUUGUG hsa-miR-10b-5p 1 1 UACCCUGU

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