Patent ID: 12221655

DETAILED DESCRIPTION OF EMBODIMENTS

Overview of Pre-Surgical Risk Stratification

FIG.1shows schematically and exemplarily a flowchart of an embodiment of a method of pre-surgical risk stratification of a prostate cancer subject.

The method begins at step S100.

At step S102, one or more biological sample(s) is/are obtained from each of a first set of patients (subjects) diagnosed with prostate cancer. Preferably, monitoring prostate cancer has been performed for these prostate cancer patients over a period of time, such as at least one year, or at least two years, or about five years, after obtaining the biological sample.

At step S104, gene expression profiles for PDE4D7 and for DHX9 are obtained for a same or different biological sample(s) obtained from each of the first set of patients, e.g., by performing RT-qPCR (real-time quantitative PCR) on RNA extracted from each biological sample. The exemplary gene expression profiles include an expression level (e.g., value) for PDE4D7 and for DHX9 which can be normalized using value(s) for each of a set of reference genes, such as HPRT1, TUBA1B, PUM1, and/or TBP. In one realization, the gene expression profile value of PDE4D7 and of DHX9 is normalized to with respect to one or more reference genes selected from the group consisting of HPRT1, TUBA1B, PUM1, and TBP, e.g., at least one, or at least two, or at least three, or, preferably, all of these reference genes.

At step S106, a regression function for assigning a pre-surgical prognostic risk score is determined, based on the gene expression profiles for PDE4D7 and for DHX9 obtained for at least some of the biological samples obtained for the first set of patients and respective results obtained from the monitoring. In one possible realization, the normalized gene expression profile(s) for PDE4D7 and/or DHX9 are first transformed into a predefined range of values, such as the above-mentioned range of 1 to 5, using (a) suitable transform function(s). As mentioned above, such a transformation can be determined by considering the frequency distribution of the normalized gene expression profile values for e.g. PDE4D7 (or for DHX9) for biological samples of a population of prostate cancer subjects (here, the first set of patients) and by determining the transformation that transforms the frequency distribution into the desired range. In one particular realization, the normalized transformed gene expression profile value(s) is/are determined as specified in Eq. (1) above.

At step S108, one or more biological sample(s) is/are obtained from a patient (subject or individual). The patient can be a new patient or one of the first set.

At step S110, gene expression profiles are obtained for PDE4D7 and DHX9, e.g., by performing PCR on the biological sample. In one realization, the gene expression profile values of PDE4D7 and of DHX9 are normalized to with respect to one or more reference genes selected from the group consisting of HPRT1, TUBA1B, PUM1, and TBP, e.g., at least one, or at least two, or at least three, or, preferably, all of these reference genes. This is substantially the same as in step S104. Moreover, the normalized gene expression profile(s) for PDE4D7 and/or DHX9 can be pretransformed as described with respect to step S106.

Other reference genes which may be additionally or alternatively used in steps S104and S110include:Homo sapiensactin, beta, mRNA (ACTB);Homo sapiens60S acidic ribosomal phosphoprotein P0 mRNA (RPLP0); Polymerase (RNA) II (DNA Directed) Polypeptide A, 220 kDa (POLR2A); Beta-2-Microglobulin (B2M); and Aminolevulinate-Delta-Synthase (ALAS-1).

At step S112, a pre-surgical prognostic risk score is determined for the patient with the regression function based on the gene expression profiles for PDE4D7 and DHX9. This will be described in more detail later in the description. To make the pre-surgical prognostic score intuitive for the user, it may also be determined such that its values fall into a predefined value range, such as the range from 1 to 5. This can either be achieved already with the regression function itself or by means of a suitable subsequent transformation that transforms the output values of the regression function into the desired value range. Again, this is similar to other categories used in the clinical routine, e.g., in histo-pathology grading (Gleason) or multi-parametric MRI radiology scoring (PIRADS).

At S114, a therapy recommendation may be provided, e.g., to the patient or his or her guardian, to a doctor, or to another healthcare worker, based on the pre-surgical prognostic risk score. To this end, the pre-surgical prognostic risk score may be categorized into one of a predefined set of risk groups, based on the value of the pre-surgical prognostic risk score. Providing a therapy recommendation may include one or more of: a) proposing a therapy for the patient based on the assigned risk group, with at least two of the risk groups being associated with different therapies, b) computing a disease progression risk prediction of the patient before or after prostate surgery; and c) computing a therapy response prediction for the patient before or after prostate surgery. Example therapies include at least a partial prostatectomy, an active therapy selected from radiation treatment, chemotherapy, and a combination thereof, and observation alone, i.e., without performing prostatectomy or active therapy (i.e., active surveillance).

The method ends at S116.

Each of the risk groups may be associated with a respective proposed therapy, which differs in its aggressiveness. Each proposed therapy may be based on the results of the patients from the first set that were assigned to that risk group and is one which is predicted to provide the least aggressive therapy which does not exceed a threshold clinical risk for development of prostate cancer. In some cases, this enables a new patient to be assigned to a risk group associated with a less aggressive proposed therapy than would be the case for other risk profiling methods, such as that using the Gleason score, the NCCN risk categories, or the pre-surgical CAPRA score.

In one embodiment, the gene expression profiles at steps S104and S110are determined by detecting mRNA expression using one or more primers and/or probes and/or one or more sets thereof.

A detailed description of PDE4D7, DHX9 and the one or more reference genes including their Transcript ID (NCBI RefSeq) and the corresponding amino acid sequences for the primer pair and probe are shown in TABLE 1. This table also shows, for each gene, a sense primer, and antisense primer, and a probe sequence that specifically binds to the amplicon.

TABLE 1Exemplary primer and probe nucleic acid sequencesExemplaryGeneExemplaryProteinNameNCBI RefSeqAccessionSense PrimerAntisense primerProbe SequencePDE4D7NM_001165899.1NP_001159371.1GAACATTCAACGACTGCCATTGTCCACATCCTGCCGCTGATTGCT(SEQ ID NO: 19)(SEQ ID NO: 20)CAACCAAAAAATCACTTCTGCA(SEQ ID NO: 21)(SEQ ID NO: 22)(SEQ ID NO: 23)CGCTGATTGCTATCAGTCGTTGACTGTGGACTTCCCTTGGATCCCACTTCTGCAAAATTTGTGACCAGCCCATAAG(SEQ ID NO: 24)(SEQ ID NO: 25)GGAA(SEQ ID NO: 26)DHX9NM_001357.4NP_001348.2to beto beto be(SEQ ID NO: 74)(SEQ ID NO: 75)individuallyindividuallyindividuallydesigneddesigneddesignedHPRT1NM_000194.2NP_000185.1GAGGATTTGGAAAGGACAGAGGGCTACAATGACGTCTTGCTCGAGA(SEQ ID NO: 34)(SEQ ID NO: 35)GTGTTTATTTGATGTGTGATGAAGG(SEQ ID NO: 36)(SEQ ID NO: 37)(SEQ ID NO: 38)TUBA1BNM_006082.2NP_006073.2TGACTCCTTCAACACTGCCAGTGCGAACTTCCCGGGCTGTGTTTGT(SEQ ID NO: 39)(SEQ ID NO: 40)CTTCTTCATAGACTTGGA(SEQ ID NO: 41)(SEQ ID NO: 42)(SEQ ID NO: 43)PUM1NM_001020658.1NP_001018494.1GCCAGCTTGTCTTCACAAAGCCAGCTTCTGTATCCACCATGAGTTG(SEQ ID NO: 44);(SEQ ID NO: 46);ATGAAATTCAAGGTAGGCAGCNM_014676.2NP_055491.1(SEQ ID NO: 48)(SEQ ID NO: 49)(SEQ ID NO: 50)(SEQ ID NO: 45)(SEQ ID NO: 47)TBPNM_003194.4NP_003185.1GCCAAGAAGAAAGTGATAGGGATTCCGGGAGTCAGAACAACAGCCT(SEQ ID NO: 51)(SEQ ID NO: 52)AACATCATTCATGCCACCTTA(SEQ ID NO: 53)(SEQ ID NO: 54)(SEQ ID NO: 55)ACTBNM_001101.3NP_001092.1CCAACCGCGAGAAGACCAGAGGCGTACAGGGCCATGTACGTTGCTA(SEQ ID NO: 56)(SEQ ID NO: 57)TGAATAGTCCAGGCT(SEQ ID NO: 58)(SEQ ID NO: 59)(SEQ ID NO: 60)RPLP0NM_001002.3NP_444505.1/TAAACCCTGCGTGGCACATTTCGGATAATCAAAGTAGTTGGACTTC(SEQ ID NO: 61)NP_000993.1AATTCCAATAGTTGCAGGTCGCC(SEQ ID NO:(SEQ ID NO: 64)(SEQ ID NO: 65)(SEQ ID NO: 66)62/63)ALAS-1NM_000688.5/NP_000679.1/AGCCACATCATCCCTCGTAGATGTTATGTCTTTTAGCAGCATCTGCNM_199166.2NP_954635.1GTGCTCATAACCCGC(SEQ ID NO:(SEQ ID NO:(SEQ ID NO: 71)(SEQ ID NO: 72)(SEQ ID NO: 73)67/68)69/70)

Instead of using RT-qPCR, the gene expression profiles for PDE4D7 and/or DHX9 at steps S104and S110may be determined in other embodiments by other means, for example, by performing RNA next-generation sequencing (NGS RNAseq) according to standard methods (Illumina, Inc.). In this case, the described transformations to a predefined value range may also be performed on the gene expression profile(s) provided by NGS RNAseq.

To explore the prognostic power of PDE4D7 and DHX9 in pre-surgical patient risk assessment, the correlation to disease recurrence was investigated.

A combination model based on the gene expression profiles of PDE4D7 and DHX9 was developed in a surgery cohort and the model was validated for different longitudinal clinical outcomes. The results show that the combination of the gene expression profiles for both PDE4D7 and DHX9 in an improved pre-surgical prognostic risk score may allow for better patient stratification in order to optimize primary treatment decisions.

EXAMPLES

Patient Cohort and Samples

A radical prostatectomy (RP) patient cohort, with the demographics shown in TABLE 2, was employed. For the RP patient cohort, a small biopsy punch (approximately 1 millimeter by 2 millimeters) of tissue was collected of a representative tumor area from the resected prostate from 575 patients who had been consecutively operated on between 2000 and 2004 at a single high-volume clinical center in Germany.

TABLE 2Demographics of the radical prostatectomy (RP) patient cohortSurgery: 2000-2004ParameterRP cohort (#575)Demographic & ClinicalAge (at RP)41.3-79.2(62.7)RangePreoperative PSA0.18-120.0(7.1)(median)Percentage tumor in biopsy0.2-80.0(10.3)Prostate Volume9-244(42)PSA density0.01-24.0(0.18)CAPRA-S Risk CategoryLow Risk (CAPRA-S 0-2)275(47.8%)No. of patientsIntermediate Risk (CAPRA-S 3-5)220(38.3%)(percentage)High Risk (CAPRA-S >5)80(13.9%)Post-Surgery PathologyPathology Gleason 3 + 3 (GG1)190(33%)No. of patientsPathology Gleason 3 + 4 (GG2)288(50.1%)(percentage)Pathology Gleason 4 + 3 (GG3)73(12.7%)Pathology Gleason >=4 + 4 (≥GG4)24(4.2%)pT2331(57.6%)pT3244(42.4%)pT40(0%)Positive Surgical Margins211(36.7%)Extra-Capsular Extension (=T3a)151(26.3%)Positive Seminal Vesicle Invasion95(16.5%)Positive Lymph Node Invasion20(3.5%)Follow-upMean104.3MonthsIQR median120Outcome<5 y BCR184/512(35.9%)No. of events/<10 y BCR228/428(53.3%)total no. of patients<5 y CR49/503(9.7%)(percentage)<10 y CR64/356(18.0%)Salvage Treatment<5 y SRT141/506(27.9%)No. of events/<10 y SRT178/405(44.0%)total no. of patients<5 y SADT79/498(15.9%)(percentage)<10 y SADT118/370(31.9%)Mortality<5 y PCSS14/483(2.9%)No. of events/<10 y PCSS26/321(8.1%)total no. of patients<5 y OS27/496(5.4%)(percentage)<10 y OS54/349(15.5%)

For patient age, preoperative PSA, percentage of tumor in biopsy, prostate volume, and PSA density, the minimum and maximum values in the cohort are shown, while the median values are depicted in parentheses. For the CAPRA-S risk categories, the number of patients and percentage per risk group are shown. Post-surgical pathology is represented by the pathology Gleason scores and Gleason grade groups, the pathology stages, the surgical margin status after prostatectomy, the tumor invasion status of the seminal vesicles and pelvic lymph nodes (by number and percentage of patients). In this respect, it is noted that the extracapsular extension was not provided as a primary parameter but was derived from pathology stage pT3a. The follow-up demonstrates the mean and median follow-up periods in months after surgery for all patients. The outcome category illustrates the cumulative 5- and 10-year biochemical recurrence (BCR) and clinical recurrence to metastases (CR) post-surgical primary treatment. The treatment category lists the cumulative 5- and 10-year start to salvage radiation therapy (SRT) or salvage androgen deprivation therapy (SADT) after surgery. Mortality is shown as prostate cancer specific survival (PCSS) as well as overall survival (OS). For all outcomes, the number of men experiencing the outcome per total number of men with the respective 5- or 10-year follow are shown, wherein the percentage of events is given in parentheses.

Laboratory Methods

All used laboratory methods including oligonucleotide primers and probes for RT-qPCR (quantitative real-time PCR), RNA extraction, and quality control and procedures to include/discard samples from the statistical analysis were as described previously in Böttcher R. et al. or, in the case of DHX9, were based on RNA next-generation sequencing (NGS RNAseq) according to standard methods (Illumina, Inc.). The primers and probes used for the RT-qPCR to measure the genes of interest as well as the reference genes are also given in TABLE 1. In the case of NGS RNAseq, the resulting FastQ files were aligned to the human genome and processed according to standard methods. For each gene a gene expression value in the form of a TPM (transcript per kilobase million as obtained.

RESULTS

Logistic Regression Modeling of PDE4D7 and DHX9 Expression

The expression values for PDE4D7 and DHX9 were used in logistic regression modeling to create a combination model. As shown in TABLE 1 below, the dependent variable was taken as the 5-year biochemical recurrence (BCR) after prostate cancer surgery in a sub-cohort of 481 patients (169 events; 35.14%) of the RP patient cohort with complete 5-year outcome histories. The logit(p) regression function was transformed to p=1/(1+{circumflex over ( )}(-logit(p)) in order to calculate the probability p for an individual patient to experience a biochemical relapse within 5 years after surgery. TABLES 2 to 6 show the results of the logistic regression modeling to combine the expression values of PDE4D7 and DHX9. TABLE 1 describes the inputs for the logistic regression modeling in terms of cohort size (#481) and the number of positive cases with 5-year biochemical recurrence (BCR) and negative cases without 5-year BCR. TABLE 2 provides information about the model fit and TABLE 3 outlines the coefficients (or weights) of the regression model with the respective statistics. TABLE 4 gives an overview on the odds ratios for PDE4D7 and DHX9 while TABLES 5 and 6 outline the data of a classification table and a ROC curve analysis for the “PDE4D7 & DHX9” regression model.

TABLE 1Input of the logistic regression modeling.Dependent Y5-year biochemical recurrence (BCR)MethodEnterSample Size481Positive casesa169 (35.14%)Negative casesb312 (64.86%)a5-year BCR = 1b5-year BCR = 0

TABLE 2Overall model fit.Null model - 2 Log Likelihood623.645Full model - 2 Log Likelihood557.901Chi-squared65.744DF2Significance levelP < 0.0001Cox & Snell R20.1278Nagelkerte R20.1758

TABLE 3Coefficients and standard errors.VariableCoefficientStd. errorWaldPPDE4D7−0.987990.1715833.1574<0.0001DHX9−0.758910.1709519.7082<0.0001Constant7.960321.3827733.1409<0.0001

TABLE 4Odd ratios and 95% confidence intervals.VariableOdds ratio95% CIPDE4D70.37230.2660 to 0.5212DHX90.46820.3349 to 0.6545

TABLE 5Classification table (cut-off value p = 0.5).Predicted groupActual group01Percent correctY = 02773588.78%Y = 11165331.36%Percentage of cases correctly classified68.61%

TABLE 6ROC curve analysis.Area under the ROC curve (AUC)0.713Standard error0.024195% confidence interval0.670 to 0.753
Validation of the Combination Model for Different Longitudinal Clinical Outcomes

The combination model was tested to predict various clinically relevant endpoints after surgery, like biochemical recurrence (BCR), clinical recurrence (progression to local and/or distant metastases) (CR), and prostate cancer specific death. The prognostic power of the combination model was compared to the prognostic power of just PDE4D7 or DHX9 for the same various clinically relevant endpoints.

FIG.2shows results of a ROC curve analysis of 5-year biochemical recurrence (BCR) after prostate cancer surgery for a sub-cohort (#481) of patients of the RP patient cohort with complete 5-year follow-up (see also TABLE 7). As detailed in TABLE 8, the 5-year AUCs (area under the curve) were calculated as 0.659 for PDE4D7 alone, as 0.624 for DHX9 alone, and as 0.713 for the combination model (indicated as “PDE4D7 & DHX9” inFIG.2and TABLE 8). The standard error was 0.0254 (PDE4D7), 0.0280 (DHX9) and 0.0242(PDE4D7 & DHX9), respectively, the 95% confidence interval was 0.615 to 0.702 (PDE4D7), 0.579 to 0.667 (DHX9) and 0.670 to 0.753 (PDE4D7 & DHX9), respectively. TABLE 9 shows a pairwise comparison of the ROC curves. As can be seen, the AUCs of PDE4D7 alone and of the combination model PDE4D7 & DHX9 were tested to be significantly different (p=0.0022). The same held true even in a slightly more significant manner for the AUCs of DHX9 alone and of the combination model PDE4D7 & DHX9 (p=0.0008).

TABLE 7Sub-cohort.Variable 1PDE4D7Variable 2DHX9Variable 3PDE4D7 & DHX9Classification variable5 year BCRSample Size481Positive casesa169 (35.14%)Negative casesb312 (64.86%)a5-year BCR = 1b5-year BCR = 0

TABLE 8ROC curve analysis.VariableAUCStd. errorc95% CIdPDE4D70.6590.02540.615 to 0.702DHX90.6240.02800.579 to 0.667PDE4D7 & DHX90.7130.02420.670 to 0.753cDeLong E.R. et al., “Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparameteric approach”, Biometrics, Vol. 44, No. 3, pages 837 to 845, 1988.bBinomial exact

TABLE 9Pairwise comparison of ROC curves.PDE4D7~DHX9Difference between areas0.0352Standard errorc0.039295% confidence interval−0.0416 to 0.112z statistic0.899Significance levelp = 0.3687PDE4D7~PDE4D7 & DHX9Difference between areas0.0538Standard errorc0.017695% confidence interval0.0193 to 0.0883z statistic3.058Significance levelp = 0.0022DHX9~PDE4D7 & DHX9Difference between areas0.0890Standard errorc0.026595% confidence interval0.0372 to 0.141z statistic3.365Significance levelp = 0.0008cDeLong E.R. et al.

FIG.3shows results of a ROC curve analysis of 10-year clinical recurrence (CR) after prostate cancer surgery for a sub-cohort (#335) of patients of the RP patient cohort with complete 10-year follow-up (see also TABLE 10). As detailed in TABLE 11, the 10-year AUCs (area under the curve) were calculated as 0.666 for PDE4D7 alone, as 0.691 for DHX9 alone, and as 0.748 for the combination model (indicated as “PDE4D7 & DHX9” inFIG.3and TABLE 11). The standard error was 0.0364 (PDE4D7), 0.0384 (DHX9) and 0.0314 (PDE4D7 & DHX9), respectively, the 95% confidence interval was 0.613 to 0.717 (PDE4D7), 0.639 to 0.740 (DHX9) and 0.698 to 0.794 (PDE4D7 & DHX9), respectively. TABLE 12 shows a pairwise comparison of the ROC curves. As can be seen, the AUCs of PDE4D7 alone and of the combination model PDE4D7 & DHX9 were tested to be significantly different (p=0.0039).

TABLE 10Sub-cohort.Variable 1PDE4D7Variable 2DHX9Variable 3PDE4D7 & DHX9Classification variable10 year clinical recurrence (CR)Sample Size335Positive casesa61 (18.21%)Negative casesb274 (81.79%)a10-year CR = 1b10-year CR = 0

TABLE 11ROC curve analysis.VariableAUCStd. errorc95% CIdPDE4D70.6660.03640.613 to 0.717DHX90.6910.03840.639 to 0.740PDE4D7 & DHX90.7480.03140.698 to 0.794cDeLong E.R. et al.dBinomial exact

TABLE 12Pairwise comparison of ROC curves.PDE4D7~DHX9Difference between areas0.0249Standard errorc0.057395% confidence interval−0.0873 to 0.137z statistic0.435Significance levelp = 0.6635PDE4D7~PDE4D7 & DHX9Difference between areas0.0816Standard errorc0.028395% confidence interval0.0262 to 0.137z statistic2.888Significance levelp = 0.0039DHX9~PDE4D7 & DHX9Difference between areas0.0567Standard errorc0.036195% confidence interval−0.0141 to 0.128z statistic1.569Significance levelp = 0.1166cDeLong E.R. et al.

FIG.4shows results of a ROC curve analysis of 10-year prostate cancer specific death after prostate cancer surgery for a sub-cohort (#302) of patients of the RP patient cohort with complete 10-year follow-up (see also TABLE 13). As detailed in TABLE 14, the 10-year AUCs (area under the curve) were calculated as 0.729 for PDE4D7 alone, as 0.622 for DHX9 alone, and as 0.754 for the combination model (indicated as “PDE4D7 & DHX9” inFIG.4and TABLE 14). The standard error was 0.0588 (PDE4D7), 0.0593 (DHX9) and 0.0481 (PDE4D7 & DHX9), respectively, the 95% confidence interval was 0.676 to 0.779 (PDE4D7), 0.564 to 0.676 (DHX9) and 0.702 to 0.802 (PDE4D7 & DHX9), respectively. TABLE 15 shows a pairwise comparison of the ROC curves. As can be seen, the AUCs of DHX9 alone and of the combination model PDE4D7 & DHX9 were tested to be significantly different (p=0.0403).

TABLE 13Sub-cohort.Variable 1PDE4D7Variable 2DHX9Variable 3PDE4D7 & DHX9Classification variable10-year prostate cancer specific deathSample Size302Positive casesa25 (8.28%)Negative casesb277 (91.72%)a10-year PCSS = 1b10-year PCSS = 0

TABLE 14ROC curve analysis.VariableAUCStd. errorc95% CIdPDE4D70.7290.05880.676 to 0.779DHX90.6220.05930.564 to 0.676PDE4D7 & DHX90.7540.04810.702 to 0.802cDeLong E.R. et al.dBinomial exact

TABLE 15Pairwise comparison of ROC curves.PDE4D7~DHX9Difference between areas0.108Standard errorc0.090995% confidence interval−0.0702 to 0.286z statistic1.188Significance levelp = 0.2349PDE4D7~PDE4D7 & DHX9Difference between areas0.0249Standard errorc0.035695% confidence interval−0.0448 to 0.0946z statistic0.700Significance levelp = 0.4836DHX9~PDE4D7 & DHX9Difference between areas0.133Standard errorc0.064895% confidence interval0.00585 to 0.260z statistic2.050Significance levelp = 0.0403cDeLong E.R. et al.

The provided results demonstrate that the use of the combination logistic regression model of PDE4D7 with DHX9 gene expression values to predict 5-year post-surgical biochemical recurrence improves the area under the curve (AUC) in ROC analysis between 2.5% (for prostate cancer specific death as endpoint) and 8% (for clinical recurrence as endpoint) compared to using PDE4D7 alone as a prognostic marker.

INdependent Testing of the Combination Model to Predict Metastases after Prostate Cancer Surgery in an Independent Data Set

FIG.5shows an independent testing of the combination model (indicated as “PDE4D7 & DHX9” in TABLE 16) to predict metastases after prostate cancer surgery in an independent data set (data from Taylor B.S. et al., “Integrative genomic profiling of human prostate cancer”, Cancer Cell, Vol. 18, No. 1, pages 11 to 22, 2010). As detailed in TABLE 16, the metastasis class AUC (area unter the curve) was calculated as 0.736 for PDE4D7 & DHX9. The standard error was 0.0777, the 95% confidence interval was 0.651 to 0.809.

TABLE 16Independent data set.VariablePDE4D7 & DHX9Classification variableMetastasis classSample Size129Positive casesa8 (6.20%)Negative casesb121 (93.8%)Disease prevalence (%)unknownaMetastasis class = 1bMetastasis class = 0

TABLE 17ROC curve analysis.AUC0.736Standard errorc0.077795% confidence intervald0.651 to 0.809z statistic3.029Significance levelp = 0.024cDeLong E.R. et al.dBinomial exact

TABLE 18Youden index.Youden index J0.4194Associated criterion>−1.16Sensitivity75.00Specificity66.94
Logistic Regression Modeling of the PDE4D7 & DHX9 Combination Model with the Post-Surgical Clinical Risk Score Capra-s to Predict Post-Surgical Metastases

The PDE4D7 & DHX9 combination model and the post-surgical clinical risk score CAPRA-S were used in an additional logistic regression modeling to create a further combination model (indicated as “PDE4D7 & DHX9 & CAPRA-S” in the following). As shown in TABLE 19 below, the dependent variable was taken as the metastasis class after prostate cancer surgery in the independent data from Tayler B.S. et al. (see above). The logit(p) regression function was transformed to p=1/(1+e{circumflex over ( )}(-logit(p)) in order to calculate the probability p for an individual patient to experience metastases after surgery. TABLES 20 to 26 show the results of the logistic regression modeling to combine PDE4D7 & DHX9 and the CAPRA-S score. TABLE 19 describes the inputs for the logistic regression modeling in terms of sample size (#129) and the number of positive cases with metastases and negative cases without metastases. TABLE 20 provides information about the model fit and TABLE 21 outlines the coefficients (or weights) of the regression model with the respective statistics. TABLE 22 gives an overview on the odds ratios for PDE4D7 & DHX9 and the CAPRA-S score while TABLES 23 and 24 show the results of the calibration testing of the regression model (according to the Hosmer & Lemeshow test). Finally, TABLES 25 and 26 outline the data of a classification table and a ROC curve analysis for the “PDE4D7 & DHX9 & CAPRA-S” regression model.

TABLE 19Input of the logistic regression modeling.Dependent YMetastasis classMethodEnterSample Size129Positive casesa8 (6.20%)Negative casesb121 (93.8%)aMetastasis class = 1bMetastasis class = 0

TABLE 20Overall model fit.Null model - 2 Log Likelihood59.979Full model - 2 Log Likelihood49.989Chi-squared9.990DF2Significance levelp = 0.0068Cox & Snell R20.0745Nagelkerte R20.2004

TABLE 21Coefficients and standard errors.VariableCoefficientStd. errorWaldPPDE4D7 & DHX90.772520.423923.32080.0684CAPRA-S score0.253750.124884.12900.0422Constant−2.808800.8716510.38370.0013

TABLE 22Odd ratios and 95% confidence intervals.VariableOdds ratio95% CIPDE4D7 & DHX92.16520.9433 to 4.9699CAPRA-S score1.28881.0090 to 1.6463

TABLE 23Hosmer & Lemeshow test.Chi-squared2.9654DF8Significance levelp = 0.9365

TABLE 24Contingency table for Hosmer & Lemeshow test.Y = 0Y = 1GroupObservedExpectedObservedExpectedTotal11312.92800.0721321312.86300.1371331312.77600.2241341312.69600.3041351212.61510.3851361312.49900.5011371212.29210.7081381212.04010.9601391211.43311.567131088.85743.14312

TABLE 25Classification table (cut-off value p = 0.5).Predicted groupActual group01Percent correctY = 0120199.17%Y = 1800.00%Percentage of cases correctly classified93.02%

TABLE 26ROC curve analysis.AUC0.713Standard errorc0.024195% confidence intervald0.670 to 0.753

FIG.6shows a ROC analysis of the PDE4D7 & DHX9 and PDE4D7 & DHX9 & CAPRA-S combination models to predict metastases after prostate cancer surgery (data from Taylor B. S. et al., “Integrative genomic profiling of human prostate cancer”, Cancer Cell, Vol. 18, No. 1, pages 11 to 22, 2010). As detailed in TABLE 28, the metastasis class AUCs (area under the curve) were calculated as 0.736 for PDE4D7 & DHX9 alone and as 0.840 for PDE4D7 & DHX9 & CAPRA-S. The standard error was 0.0777 (PDE4D7 & DHX9) and 0.0636 (PDE4D7 & DHX9 & CAPRA-S), respectively, the 95% confidence interval was 0.651 to 0.809 (PDE4D7 & DHX9) and 0.765 to 0.899 (PDE4D7 & DHX9 & CAPRA-S), respectively. TABLE 29 shows a pairwise comparison of the ROC curves. As can be seen, the AUCs of PDE4D7 & DHX9 alone and of the combination model PDE4D7 & DHX9 & CAPRA-S were tested to be significantly different (p=0.0323) by a difference in AUC of 0,104 (or 10,4%).

TABLE 27Independent data set.Variable 1PDE4D7 & DHX9Variable 2PDE4D7 & DHX9 & CAPRA-SClassification variableMetastasis classSample Size129Positive casesa8 (6.20%)Negative casesb121 (93.8%)Disease prevalence (%)unknownaMetastasis class = 1bMetastasis class = 0

TABLE 28ROC curve analysis.VariableAUCStd. errorc95% CIdPDE4D7 & DHX90.7360.07770.651 to 0.809PDE4D7 & DHX9 & CAPRA-S0.8400.06360.765 to 0.899cDeLong E.R. et al.dBinomial exact

TABLE 29Pairwise comparison of ROC curves.PDE4D7 & DHX9~PDE4D7 & DHX9 & CAPRA-SDifference between areas0.104Standard errorc0.048795% confidence interval0.00881 to 0.200z statistic2.141Significance levelp = 0.0323cDeLong E.R. et al.
Discussion

Treatment decisions in primary, localized prostate cancer are largely subject to a combination of the risk of future disease progression and life expectancy. The provided data illustrate that the use of a combination of PDE4D7 and DHX9 in a pre-surgical risk score adds value compared to using PDE4D7 as a prognostic marker alone. Thus, DHX9 may be adding prognostic value to PDE4D7 in clinical prediction models for disease specific outcomes like post-surgical progression to biochemical relapse or clinical recurrence to metastases as well as to the prediction of cancer specific survival to support treatment decision making.

Other variations to the disclosed realizations can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims.

In the claims, the word “comprising” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude a plurality.

One or more steps of the method illustrated inFIG.1may be implemented in a computer program product that may be executed on a computer. The computer program product may comprise a non-transitory computer-readable recording medium on which a control program is recorded (stored), such as a disk, hard drive, or the like. Common forms of non-transitory computer-readable media include, for example, floppy disks, flexible disks, hard disks, magnetic tape, or any other magnetic storage medium, CD-ROM, DVD, or any other optical medium, a RAM, a PROM, an EPROM, a FLASH-EPROM, or other memory chip or cartridge, or any other non-transitory medium from which a computer can read and use.

Alternatively, the one or more steps of the method may be implemented in transitory media, such as a transmittable carrier wave in which the control program is embodied as a data signal using transmission media, such as acoustic or light waves, such as those generated during radio wave and infrared data communications, and the like.

The exemplary method may be implemented on one or more general purpose computers, special purpose computer(s), a programmed microprocessor or microcontroller and peripheral integrated circuit elements, an ASIC or other integrated circuit, a digital signal processor, a hardwired electronic or logic circuit such as a discrete element circuit, a programmable logic device such as a PLD, PLA, FPGA, Graphical card CPU (GPU), or PAL, or the like. In general, any device, capable of implementing a finite state machine that is in turn capable of implementing the flowchart shown inFIG.1, can be used to implement one or more steps of the method of risk stratification for therapy selection in a patient with prostate cancer is illustrated. As will be appreciated, while the steps of the method may all be computer implemented, in some embodiments one or more of the steps may be at least partially performed manually.

The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified herein.

While the invention has described so far based on the gene expression profile for PDE4D7, which can include an expression level (e.g., value) for PDE4D7 which can be normalized using value(s) for each of a set of reference genes, the gene expression profile may further include expression information from other PDE4D variants. For example, the other PDE4D variant(s) may include one or more of PDE4D1, PDE4D2, PDE4D3, PDE4D4, PDE4D5, PDE4D6, PDE4D8 and PDE4D9. The diagnostic kit may then additionally comprise at least one primer and/or probe for determining the gene expression profile for each of the other PDE4D variant(s) in the biological sample obtained from the prostate cancer subject. Preferably, however, only the gene expression profile for PDE4D7, in particular, an expression level (e.g., value) for PDE4D7 which can be normalized using value(s) for each of a set of reference genes, is employed.

The term “phosphodiesterase 4D1” or “PDE4D1” relates to the splice variant 1 of the human phosphodiesterase PDE4D, i.e., the human phosphodiesterase PDE4D1 gene, for example, to the sequence as defined in NCBI Reference Sequence: NM_001197222.1, specifically, to the nucleotide sequence as set forth in SEQ ID NO:1, which corresponds to the sequence of the above indicated NCBI Reference Sequence of the PDE4D1 transcript, and also relates to the corresponding amino acid sequence for example as set forth in SEQ ID NO:2, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001184151.1 encoding the PDE4D1 polypeptide. The term “phosphodiesterase 4D1” or “PDE4D1” also relates to the amplicon that can be generated by the primer pair PDE1D1D2_forward (SEQ ID NO:3) and the PDE1D1D2_reverse (SEQ ID NO:4) and can be detected by probe SEQ ID NO:5.

The term “phosphodiesterase 4D2” or “PDE4D2” refers to the splice variant 2 of the human phosphodiesterase PDE4D, i.e., the human phosphodiesterase PDE4D2 gene, for example, to the sequence as defined in NCBI Reference Sequence: NM_001197221.1, specifically, to the nucleotide sequence as set forth in SEQ ID NO:6, which corresponds to the sequence of the above indicated NCBI Reference Sequence of the PDE4D2 transcript, and also relates to the corresponding amino acid sequence for example as set forth in SEQ ID NO:7, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001184150.1 encoding the PDE4D2 polypeptide.

The term “phosphodiesterase 4D3” or “PDE4D3” refers to the splice variant 3 of the human phosphodiesterase PDE4D, i.e., the human phosphodiesterase PDE4D3 gene, for example, to the sequence as defined in NCBI Reference Sequence: NM_006203.4, specifically, to the nucleotide sequence as set forth in SEQ ID NO:8, which corresponds to the sequence of the above indicated NCBI Reference Sequence of the PDE4D3 transcript, and also relates to the corresponding amino acid sequence for example as set forth in SEQ ID NO:9, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_006194.2 encoding the PDE4D3 polypeptide.

The term “phosphodiesterase 4D4” or “PDE4D4” refers to the splice variant 4 of the human phosphodiesterase PDE4D, i.e., the human phosphodiesterase PDE4D4 gene, for example, to the sequence as defined in NCBI Reference Sequence: NM_001104631.1, specifically, to the nucleotide sequence as set forth in SEQ ID NO:10, which corresponds to the sequence of the above indicated NCBI Reference Sequence of the PDE4D4 transcript, and also relates to the corresponding amino acid sequence for example as set forth in SEQ ID NO:11, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001098101.1 encoding the PDE4D4 polypeptide.

The term “phosphodiesterase 4D5” or “PDE4D5” refers to the splice variant 5 of the human phosphodiesterase PDE4D, i.e., the human phosphodiesterase PDE4D5 gene, for example, to the sequence as defined in NCBI Reference Sequence: NM_001197218.1, specifically, to the nucleotide sequence as set forth in SEQ ID NO:12, which corresponds to the sequence of the above indicated NCBI Reference Sequence of the PDE4D5 transcript, and also relates to the corresponding amino acid sequence for example as set forth in SEQ ID NO:13, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001184147.1 encoding the PDE4D5 polypeptide. The term “phosphodiesterase 4D5” or “PDE4D5” also relates to the amplicon that can be generated by the primer pair PDE4D5_forward (SEQ ID NO:14) and the PDE4D5_reverse (SEQ ID NO:15) and can be detected by probe SEQ ID NO:16.

The term “phosphodiesterase 4D6” or “PDE4D6” refers to the splice variant 6 of the human phosphodiesterase PDE4D, i.e., the human phosphodiesterase PDE4D6 gene, for example, to the sequence as defined in NCBI Reference Sequence: NM_001197223.1, specifically, to the nucleotide sequence as set forth in SEQ ID NO:17, which corresponds to the sequence of the above indicated NCBI Reference Sequence of the PDE4D6 transcript, and also relates to the corresponding amino acid sequence for example as set forth in SEQ ID NO:18, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001184152.1 encoding the PDE4D6 polypeptide.

The term “phosphodiesterase 4D8” or “PDE4D8” relates to the splice variant 8 of the human phosphodiesterase PDE4D, i.e., the human phosphodiesterase PDE4D8 gene, for example, to the sequence as defined in NCBI Reference Sequence: NM_001197219.1, specifically, to the nucleotide sequence as set forth in SEQ ID NO:27, which corresponds to the sequence of the above indicated NCBI Reference Sequence of the PDE4D8 transcript, and also relates to the corresponding amino acid sequence for example as set forth in SEQ ID NO:28, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001184148.1 encoding the PDE4D8 polypeptide.

The term “phosphodiesterase 4D9” or “PDE4D9” relates to the splice variant 9 of the human phosphodiesterase PDE4D, i.e., the human phosphodiesterase PDE4D9 gene, for example, to the sequence as defined in NCBI Reference Sequence: NM_001197220.1, specifically, to the nucleotide sequence as set forth in SEQ ID NO:29, which corresponds to the sequence of the above indicated NCBI Reference Sequence of the PDE4D9 transcript, and also relates to the corresponding amino acid sequence for example as set forth in SEQ ID NO:30 which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001184149.1 encoding the PDE4D9 polypeptide. The term “phosphodiesterase 4D9” or “PDE4D9” also relates to the amplicon that can be generated by the primer pair PDE4D9_forward (SEQ ID NO:31) and the PDE4D9_reverse (SEQ ID NO:32) and can be detected by probe SEQ ID NO:33.

The terms “PDE4D1,” “PDE4D2,” “PDE4D3,” “PDE4D4,” “PDE4D5,” “PDE4D6,” “PDE4D8,” and “PDE4D9” also comprises nucleotide sequences showing a high degree of homology to PDE4D1, PDE4D2, PDE4D3, PDE4D4, PDE4D5, PDE4D6, PDE4D8 and PDE4D9 respectively, e.g., nucleic acid sequences being at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NOs: 1, 6, 8, 10, 12, 17, 27 or 29 respectively or amino acid sequences being at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:2, 7, 9, 11, 13, 18, 28 or 30 respectively or nucleic acid sequences encoding amino acid sequences being at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:2, 7, 9, 11, 13, 18, 28 or 30 or amino acid sequences being encoded by nucleic acid sequences being at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:1, 6, 8, 10, 12, 17, 27 or 29.

Any reference signs in the claims should not be construed as limiting the scope.

The invention relates to a method of pre-surgical risk stratification of a prostate cancer subject, comprising determining a gene expression profile for phosphodiesterase 4D variant 7 (PDE4D7) in a biological sample obtained from the subject, determining a gene expression profile for DExH-box helicase 9 (DHX9) in the same or another biological sample obtained from the subject, and determining a pre-surgical prognostic risk score for the subject based on the gene expression profile for PDE4D7 and the gene expression profile for DHX9. This may allow for an improved stratification of the subject in a pre-surgical setting that may result in better primary treatment decisions. For instance, the pre-surgical prognostic risk score may allow to make better recommendations on whether to select active surveillance vs. active intervention, e.g., radical prostatectomy, for certain sub-populations of prostate cancer patients.