RESEARCH ARTICLE
A Novel MiRNA-Based Predictive Model for Biochemical Failure Following PostProstatectomy Salvage Radiation Therapy Erica Hlavin Bell1☯*, Simon Kirste1,2☯, Jessica L. Fleming1☯, Petra Stegmaier1,2, Vanessa Drendel3, Xiaokui Mo4, Stella Ling1, Denise Fabian1, Isabel Manring1, Cordula A. Jilg5, Wolfgang Schultze-Seemann5, Maureen McNulty1, Debra L. Zynger6, Douglas Martin1, Julia White1, Martin Werner3‡, Anca L. Grosu2‡, Arnab Chakravarti1‡ 1 Department of Radiation Oncology, Arthur G. James Hospital/ Ohio State Comprehensive Cancer Center, Columbus, Ohio, United States of America, 2 Department of Radiation Oncology, University Medical Center Freiburg, Freiburg, Germany, 3 Department of Pathology, University Medical Center Freiburg, Freiburg, Germany, 4 Center for Biostatistics, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States of America, 5 Department of Urology, University Medical Center Freiburg, Freiburg, Germany, 6 The Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States of America
OPEN ACCESS Citation: Bell EH, Kirste S, Fleming JL, Stegmaier P, Drendel V, Mo X, et al. (2015) A Novel MiRNA-Based Predictive Model for Biochemical Failure Following Post-Prostatectomy Salvage Radiation Therapy. PLoS ONE 10(3): e0118745. doi:10.1371/journal. pone.0118745 Academic Editor: Zoran Culig, Innsbruck Medical University, AUSTRIA Received: September 26, 2014 Accepted: January 12, 2015 Published: March 11, 2015 Copyright: © 2015 Bell et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All clinical and miRNA data are available from the Gene Expression Omnibus (GSE65061). Funding: This study was supported by funds from The Ohio State University Comprehensive Cancer Center (to AC); NIH/NCI 1U10CA180850-01 and 1R01CA188500-01 (To AC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist.
☯ These authors contributed equally to this work. ‡ These authors also contributed equally to this work. * [email protected]
Abstract Purpose To develop a microRNA (miRNA)-based predictive model for prostate cancer patients of 1) time to biochemical recurrence after radical prostatectomy and 2) biochemical recurrence after salvage radiation therapy following documented biochemical disease progression post-radical prostatectomy.
Methods Forty three patients who had undergone salvage radiation therapy following biochemical failure after radical prostatectomy with greater than 4 years of follow-up data were identified. Formalin-fixed, paraffin-embedded tissue blocks were collected for all patients and total RNA was isolated from 1mm cores enriched for tumor (>70%). Eight hundred miRNAs were analyzed simultaneously using the nCounter human miRNA v2 assay (NanoString Technologies; Seattle, WA). Univariate and multivariate Cox proportion hazards regression models as well as receiver operating characteristics were used to identify statistically significant miRNAs that were predictive of biochemical recurrence.
Results Eighty eight miRNAs were identified to be significantly (p36 months). Nine miRNAs
PLOS ONE | DOI:10.1371/journal.pone.0118745 March 11, 2015
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MiRNA-Based Predictive Model of Salvage RT Therapy Post-RP
were identified to be significantly (p70%). Total RNA was isolated using a combination of RecoverAll Total Nucleic Acid Isolation digestion buffer, AM1975, (Life Technologies; Carlsbad, CA) with Qiagen FFPE miRNeasy kit, Cat. # 217504 (Qiagen; Venlo, Limburg). In brief, cores were digested overnight using RecoverAll Total Nucleic Acid Isolation digestion buffer and proteinase K leading to enhanced digestion of the core. Once cores were digested (next day), we followed the Qiagen FFPE miRNeasy manual starting at the 80°C for 15 minute incubation. RNA concentrations were measured using a NanoDrop 2000 spectrophotometer (Thermo Scientific; Waltham, MA).
miRNA Expression Analysis For miRNA expression data generation, the NanoString human v2 array, which contains 800 miRNA probes, was used. Forty three tumor samples were analyzed. Expression analysis was conducted at The Ohio State University Nucleic Acid Core Facility (Columbus, OH). A total of 100ng RNA input was used per sample and conditions were set according to the manufacturer’s recommended protocol (NanoString Technologies; Seattle, WA). miRNAs were quantified using the nCounter Digital Analyzer as counts. miRNAs were filtered out from downstream analysis if total counts were less than 32 across 90% of the samples. Four hundred and seventy seven miRs were left after the filtering. Data were normalized by the geometric mean of all targets using the nSolver software (NanoString Technologies; Seattle, WA) [33]. The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus [34] and are accessible through GEO Series accession number GSE65061.
Statistical Analysis To compare the difference in tumor miRNA expression between late and early time to first biochemical recurrence post-RP (36 months as a cut-off) or between the time to the recurrence post-salvage RT 2-sample t-tests were used. For each miRNA, patients were dichotomized into high and low groups based on the median miRNA expression and the difference in the probabilities of the time to the recurrence (post-RP or post-salvage RT recurrence) was compared using the log-rank test for each miRNA. Multivariate analyses were performed using Cox proportional hazard regression models. ROC curve analysis was performed to determine the capability and cut-off level of variables that distinguished between the recurrence and nonrecurrence of post-salvage RT. All analyses were performed using SAS 9.3 (SAS, Inc; Cary, NC) or R 3.0.
Results miRNAs predictive of PSA recurrence post-prostatectomy Univariate Analysis. A schematic of the study design is shown in Fig. 1. A clinical database was established for 43 PCa patients who underwent both RP and salvage RT (Table 1). Risk factors included 32.5% of patients who had a Gleason score of 8 or above, 25.6% of patients who had seminal vesicle invasion or extraprostatic extension, and 41.9% of patients who had positive margins. Salvage RT was started at a median PSA value of 0.39 ng/ml. Nineteen patients (44.2%) experienced BF after salvage RT at a median time of 27.1 months (range 0.0–64.1 months). The median follow-up times after RP and after salvage RT were 6.9 and 3.7 years, respectively.
PLOS ONE | DOI:10.1371/journal.pone.0118745 March 11, 2015
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MiRNA-Based Predictive Model of Salvage RT Therapy Post-RP
Fig 1. Study design. All patients underwent radical prostatectomy and salvage radiation therapy following biochemical recurrence. Tissue isolated at the time of radical prostatectomy (n = 43) was used for miRNA profiling. doi:10.1371/journal.pone.0118745.g001
Using these data, miRNA expression was correlated with time to first biochemical recurrence to determine if miRNAs can predict biochemical recurrence post-RP. Time to first recurrence was defined as the time from prostatectomy to the start date of salvage RT. Using tumor expression only, we identified 54 miRNAs that were significantly differentially expressed 1.5fold or greater between patients who had an early ( 36 months) versus late (> 36 months) recurrence (S1 Table). Thirty six months was used as the cut-off as it was near the median of time to recurrence. For another approach to identify miRNAs that correlated with time to first biochemical recurrence, we dichotomized patients according to median miRNA expression and compared the probabilities in the time to the first recurrences between the two groups (high vs low miRNA expression) using log-rank tests. One hundred and twenty three miRNAs were identified that could differentiate the two groups in the time to first biochemical recurrence (p-value < 0.05) (S2 Table). Multivariate Analysis. In order to determine if miRNAs can be independent markers of biochemical recurrence post-RP, two multivariate Cox regression analyses were performed using all 123 miRNAs identified to be predictive of biochemical failure post-prostatectomy (pvalue < 0.05) by univariate log-rank analysis (S2 Table). The first multivariate analysis considered initial PSA value (continuous) and Gleason score; age and resection status were eliminated as these did not seem to affect the recurrence in univariate analysis. Upon analysis of the first recurrence using multivariable Cox regression analysis, 97 miRNAs had p-values < 0.05 (S3 Table). In the second multivariate analysis, different clinical factors were taken into consideration. Three clinical covariates were examined independently, D’Amico score, Stephenson score (categorical), and Stephenson score (continuous), to identify miRNAs associated with time to first recurrence. miRNAs were removed from further analysis if a significant p-value (< 0.05) was not obtained in any of the three analyses which resulted in 88 miRNAs that were statistically associated with time to first recurrence after multivariate analysis (S4 Table).
PLOS ONE | DOI:10.1371/journal.pone.0118745 March 11, 2015
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MiRNA-Based Predictive Model of Salvage RT Therapy Post-RP
Table 1. Clinical characteristics of 43 prostate cancer patients treated with salvage radiation therapy post-prostatectomy. PCa patients (n = 43) Age at RP (years) Median (min-max)
65 (45–73)
Follow-up post-RP (years) Median (min-max)
6.9 (4.0–13.9)
Gleason score 6
8 (18.6%)
7
21 (48.8%)
8
8 (18.6%)
9
5 (11.6%)
10
1 (2.3%)
Pathological Tumor Stage T2
32 (74.4%)
T3
11 (25.6%)
Pathological N Stage N0
39 (89.0%)
N+
4 (11.0%)
Resection Status R0
20 (46.5%)
R1
18 (41.9%)
Rx
5 (11.6%)
PSA at initial diagnosis (ng/mL)< 10
30 (69.8%)
10–20
10 (23.2%)
> 20
3 (7.0%)
Risk groups (D’Amico) Low
0 (0.0%)
Intermediate
8 (18.6%)
High
35 (81.4%)
Risk groups (Stephensen) Low
25 (58.1%)
Intermediate
7 (16.3%)
High
7 (16.3%)
High +
4 (9.3%)
Androgen Deprivation Therapy Pre-op
6 (13.9%)
Pre-RT
3 (7.0%)
Concurrent
4 (9.3%)
Time from RP to RT (months) Median (min-max)
34.5 (10.4–123.1)
Follow-up post-RT (years) Median (min-max)
3.7 (0.7–7.4)
Pre-RT PSA (ng/mL) < 0.2
6 (14.0%)
0.2–1.0
28 (65.1%)
1.0–5.0
7 (16.2%)
> 5.0
2 (4.7%)
Time from RT to BF (months) Median (min-max)
27.1 (0.0–64.1)
Recurrence following RT Biochemical
19 (44.2%)
Proven by imaging
7 (16.3%)
Inside RT field
0 (0.0%) (Continued)
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MiRNA-Based Predictive Model of Salvage RT Therapy Post-RP
Table 1. (Continued) PCa patients (n = 43) Outside RT field
7 (16.3%)
RP, radical prostatectomy; min, minimum; max, maximum; RT, radiation therapy; op, operation; PSA, prostate-specific antigen. doi:10.1371/journal.pone.0118745.t001
Patients were classified into two groups using unbiased hierarchical cluster analysis for those selected 88 miRNAs (Fig. 2A). The Kaplan-Meier method showed that the probabilities in the time to first biochemical failure were significantly different between the two groups of patients with distinguished miRNA signatures (log-rank, p = 0.005) (Fig. 2B).
miRNAs predictive of PSA recurrence post-salvage RT Univariate Analysis. miRNAs that could predict biochemical failure after salvage RT treatment are of utmost interest. At this time, no studies have examined miRNAs associated with clinical outcomes following post-RP salvage radiation. The recurrence after salvage radiation treatment (also referred to as the 2nd recurrence) was defined as a rise in PSA to 0.2 ng/mL at least twice consecutively following the nadir [35]. In our patient cohort, 19 out of 43 patients experienced biochemical failure after salvage radiotherapy (Table 1). Further, using tumor miRNA expression only, we identified 4 miRNAs that were significantly expressed 1.5-fold or greater in patients who experienced a second recurrence when compared with those who did not (p-value < 0.05) (S5 Table). In the univariate log-rank analysis, 24 miRNAs were identified, which could predict patients who recurred a second time after salvage RT from those who did not recur (p-value < 0.05) (S6 Table). Multivariate Analysis. Similarly, in order to determine if miRNAs can independently predict biochemical recurrence post-salvage RT, multivariate Cox regression analyses were performed using the 24 miRNAs that were significantly associated (p-value < 0.05) with recurrence post-salvage RT by univariate log-rank analysis (S6 Table). Due to the small sample size and number of events, only two covariates were examined in the multivariate analysis. The multivariate analysis was performed using lymph node status and Gleason score (these factors were chosen as having the lowest p-values upon univariate analysis as shown in (Table 2)), leading to identification of 9 miRNAs that predicted biochemical recurrence post-salvage RT (Table 3).
Predictive effect of miR-601 + miR-4516 & Development of a miRNAbased Predictive Salvage RT Model A primary objective of this study was to identify miRNAs that could be used independently as predictive biomarkers of salvage RT as well as to utilize in combination with other clinical factors to improve the sensitivity and selectivity of existing models. We then included lymph node status, Gleason score and the 9 miRNAs as the covariates in the Cox regression model and used a stepwise model selection strategy to capture miRNAs which could be good candidates in predicting the biochemical recurrence post-salvage RT. The two miRNAs, miR-601 and miR4516 alone and together with Gleason score and lymph node status were selected by the model. An area under the curve (AUC) of receiver-operator characteristics (ROC) models using the nearest neighbor estimation method was utilized [78]. An AUC for lymph node and Gleason score was found to be 0.66, the addition of miR-601 and miR-4516 increased the AUC to 0.83 (Fig. 3). Interestingly, each miRNA alone (miR-601 AUC = 0.77 and miR-4516 AUC = 0.68) or
PLOS ONE | DOI:10.1371/journal.pone.0118745 March 11, 2015
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MiRNA-Based Predictive Model of Salvage RT Therapy Post-RP
Fig 2. 88-miRNA signature predicts early vs late biochemical recurrence post-RP. Cluster analysis was performed using the statistically significant 88 miRNAs predictive of first biochemical recurrence using Cox regression multivariable analysis (D’Amico score, Stephensen score (categorical), and Stephensen score (continuous)). This signature (A) appears to differentiate between patients with early recurrence (< 36 months) (red box) vs those with late recurrence (> 36 months). Kaplan-Meier plots were generated using the two cluster groups (B). Cluster 1 (blue), left cluster/early recurrence group; Cluster 2 (red), right cluster/late recurrence group. doi:10.1371/journal.pone.0118745.g002
together (miR-601 + miR-4516 AUC = 0.76) (Fig. 3) had a better predictive capability than the combination of positive lymph nodes together with Gleason score (AUC = 0.66) (Fig. 3). Using the risk score generated by this model (miR-4516, miR-601, Gleason score, and lymph node status), patients were classified as high or low risk groups based on median, the probabilities in recurrence post-salvage RT of the two groups were found to be significantly different (logrank, p
A Novel MiRNA-Based Predictive Model for Biochemical Failure Following PostProstatectomy Salvage Radiation Therapy Erica Hlavin Bell1☯*, Simon Kirste1,2☯, Jessica L. Fleming1☯, Petra Stegmaier1,2, Vanessa Drendel3, Xiaokui Mo4, Stella Ling1, Denise Fabian1, Isabel Manring1, Cordula A. Jilg5, Wolfgang Schultze-Seemann5, Maureen McNulty1, Debra L. Zynger6, Douglas Martin1, Julia White1, Martin Werner3‡, Anca L. Grosu2‡, Arnab Chakravarti1‡ 1 Department of Radiation Oncology, Arthur G. James Hospital/ Ohio State Comprehensive Cancer Center, Columbus, Ohio, United States of America, 2 Department of Radiation Oncology, University Medical Center Freiburg, Freiburg, Germany, 3 Department of Pathology, University Medical Center Freiburg, Freiburg, Germany, 4 Center for Biostatistics, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States of America, 5 Department of Urology, University Medical Center Freiburg, Freiburg, Germany, 6 The Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States of America
OPEN ACCESS Citation: Bell EH, Kirste S, Fleming JL, Stegmaier P, Drendel V, Mo X, et al. (2015) A Novel MiRNA-Based Predictive Model for Biochemical Failure Following Post-Prostatectomy Salvage Radiation Therapy. PLoS ONE 10(3): e0118745. doi:10.1371/journal. pone.0118745 Academic Editor: Zoran Culig, Innsbruck Medical University, AUSTRIA Received: September 26, 2014 Accepted: January 12, 2015 Published: March 11, 2015 Copyright: © 2015 Bell et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All clinical and miRNA data are available from the Gene Expression Omnibus (GSE65061). Funding: This study was supported by funds from The Ohio State University Comprehensive Cancer Center (to AC); NIH/NCI 1U10CA180850-01 and 1R01CA188500-01 (To AC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist.
☯ These authors contributed equally to this work. ‡ These authors also contributed equally to this work. * [email protected]
Abstract Purpose To develop a microRNA (miRNA)-based predictive model for prostate cancer patients of 1) time to biochemical recurrence after radical prostatectomy and 2) biochemical recurrence after salvage radiation therapy following documented biochemical disease progression post-radical prostatectomy.
Methods Forty three patients who had undergone salvage radiation therapy following biochemical failure after radical prostatectomy with greater than 4 years of follow-up data were identified. Formalin-fixed, paraffin-embedded tissue blocks were collected for all patients and total RNA was isolated from 1mm cores enriched for tumor (>70%). Eight hundred miRNAs were analyzed simultaneously using the nCounter human miRNA v2 assay (NanoString Technologies; Seattle, WA). Univariate and multivariate Cox proportion hazards regression models as well as receiver operating characteristics were used to identify statistically significant miRNAs that were predictive of biochemical recurrence.
Results Eighty eight miRNAs were identified to be significantly (p36 months). Nine miRNAs
PLOS ONE | DOI:10.1371/journal.pone.0118745 March 11, 2015
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MiRNA-Based Predictive Model of Salvage RT Therapy Post-RP
were identified to be significantly (p70%). Total RNA was isolated using a combination of RecoverAll Total Nucleic Acid Isolation digestion buffer, AM1975, (Life Technologies; Carlsbad, CA) with Qiagen FFPE miRNeasy kit, Cat. # 217504 (Qiagen; Venlo, Limburg). In brief, cores were digested overnight using RecoverAll Total Nucleic Acid Isolation digestion buffer and proteinase K leading to enhanced digestion of the core. Once cores were digested (next day), we followed the Qiagen FFPE miRNeasy manual starting at the 80°C for 15 minute incubation. RNA concentrations were measured using a NanoDrop 2000 spectrophotometer (Thermo Scientific; Waltham, MA).
miRNA Expression Analysis For miRNA expression data generation, the NanoString human v2 array, which contains 800 miRNA probes, was used. Forty three tumor samples were analyzed. Expression analysis was conducted at The Ohio State University Nucleic Acid Core Facility (Columbus, OH). A total of 100ng RNA input was used per sample and conditions were set according to the manufacturer’s recommended protocol (NanoString Technologies; Seattle, WA). miRNAs were quantified using the nCounter Digital Analyzer as counts. miRNAs were filtered out from downstream analysis if total counts were less than 32 across 90% of the samples. Four hundred and seventy seven miRs were left after the filtering. Data were normalized by the geometric mean of all targets using the nSolver software (NanoString Technologies; Seattle, WA) [33]. The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus [34] and are accessible through GEO Series accession number GSE65061.
Statistical Analysis To compare the difference in tumor miRNA expression between late and early time to first biochemical recurrence post-RP (36 months as a cut-off) or between the time to the recurrence post-salvage RT 2-sample t-tests were used. For each miRNA, patients were dichotomized into high and low groups based on the median miRNA expression and the difference in the probabilities of the time to the recurrence (post-RP or post-salvage RT recurrence) was compared using the log-rank test for each miRNA. Multivariate analyses were performed using Cox proportional hazard regression models. ROC curve analysis was performed to determine the capability and cut-off level of variables that distinguished between the recurrence and nonrecurrence of post-salvage RT. All analyses were performed using SAS 9.3 (SAS, Inc; Cary, NC) or R 3.0.
Results miRNAs predictive of PSA recurrence post-prostatectomy Univariate Analysis. A schematic of the study design is shown in Fig. 1. A clinical database was established for 43 PCa patients who underwent both RP and salvage RT (Table 1). Risk factors included 32.5% of patients who had a Gleason score of 8 or above, 25.6% of patients who had seminal vesicle invasion or extraprostatic extension, and 41.9% of patients who had positive margins. Salvage RT was started at a median PSA value of 0.39 ng/ml. Nineteen patients (44.2%) experienced BF after salvage RT at a median time of 27.1 months (range 0.0–64.1 months). The median follow-up times after RP and after salvage RT were 6.9 and 3.7 years, respectively.
PLOS ONE | DOI:10.1371/journal.pone.0118745 March 11, 2015
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MiRNA-Based Predictive Model of Salvage RT Therapy Post-RP
Fig 1. Study design. All patients underwent radical prostatectomy and salvage radiation therapy following biochemical recurrence. Tissue isolated at the time of radical prostatectomy (n = 43) was used for miRNA profiling. doi:10.1371/journal.pone.0118745.g001
Using these data, miRNA expression was correlated with time to first biochemical recurrence to determine if miRNAs can predict biochemical recurrence post-RP. Time to first recurrence was defined as the time from prostatectomy to the start date of salvage RT. Using tumor expression only, we identified 54 miRNAs that were significantly differentially expressed 1.5fold or greater between patients who had an early ( 36 months) versus late (> 36 months) recurrence (S1 Table). Thirty six months was used as the cut-off as it was near the median of time to recurrence. For another approach to identify miRNAs that correlated with time to first biochemical recurrence, we dichotomized patients according to median miRNA expression and compared the probabilities in the time to the first recurrences between the two groups (high vs low miRNA expression) using log-rank tests. One hundred and twenty three miRNAs were identified that could differentiate the two groups in the time to first biochemical recurrence (p-value < 0.05) (S2 Table). Multivariate Analysis. In order to determine if miRNAs can be independent markers of biochemical recurrence post-RP, two multivariate Cox regression analyses were performed using all 123 miRNAs identified to be predictive of biochemical failure post-prostatectomy (pvalue < 0.05) by univariate log-rank analysis (S2 Table). The first multivariate analysis considered initial PSA value (continuous) and Gleason score; age and resection status were eliminated as these did not seem to affect the recurrence in univariate analysis. Upon analysis of the first recurrence using multivariable Cox regression analysis, 97 miRNAs had p-values < 0.05 (S3 Table). In the second multivariate analysis, different clinical factors were taken into consideration. Three clinical covariates were examined independently, D’Amico score, Stephenson score (categorical), and Stephenson score (continuous), to identify miRNAs associated with time to first recurrence. miRNAs were removed from further analysis if a significant p-value (< 0.05) was not obtained in any of the three analyses which resulted in 88 miRNAs that were statistically associated with time to first recurrence after multivariate analysis (S4 Table).
PLOS ONE | DOI:10.1371/journal.pone.0118745 March 11, 2015
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MiRNA-Based Predictive Model of Salvage RT Therapy Post-RP
Table 1. Clinical characteristics of 43 prostate cancer patients treated with salvage radiation therapy post-prostatectomy. PCa patients (n = 43) Age at RP (years) Median (min-max)
65 (45–73)
Follow-up post-RP (years) Median (min-max)
6.9 (4.0–13.9)
Gleason score 6
8 (18.6%)
7
21 (48.8%)
8
8 (18.6%)
9
5 (11.6%)
10
1 (2.3%)
Pathological Tumor Stage T2
32 (74.4%)
T3
11 (25.6%)
Pathological N Stage N0
39 (89.0%)
N+
4 (11.0%)
Resection Status R0
20 (46.5%)
R1
18 (41.9%)
Rx
5 (11.6%)
PSA at initial diagnosis (ng/mL)< 10
30 (69.8%)
10–20
10 (23.2%)
> 20
3 (7.0%)
Risk groups (D’Amico) Low
0 (0.0%)
Intermediate
8 (18.6%)
High
35 (81.4%)
Risk groups (Stephensen) Low
25 (58.1%)
Intermediate
7 (16.3%)
High
7 (16.3%)
High +
4 (9.3%)
Androgen Deprivation Therapy Pre-op
6 (13.9%)
Pre-RT
3 (7.0%)
Concurrent
4 (9.3%)
Time from RP to RT (months) Median (min-max)
34.5 (10.4–123.1)
Follow-up post-RT (years) Median (min-max)
3.7 (0.7–7.4)
Pre-RT PSA (ng/mL) < 0.2
6 (14.0%)
0.2–1.0
28 (65.1%)
1.0–5.0
7 (16.2%)
> 5.0
2 (4.7%)
Time from RT to BF (months) Median (min-max)
27.1 (0.0–64.1)
Recurrence following RT Biochemical
19 (44.2%)
Proven by imaging
7 (16.3%)
Inside RT field
0 (0.0%) (Continued)
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MiRNA-Based Predictive Model of Salvage RT Therapy Post-RP
Table 1. (Continued) PCa patients (n = 43) Outside RT field
7 (16.3%)
RP, radical prostatectomy; min, minimum; max, maximum; RT, radiation therapy; op, operation; PSA, prostate-specific antigen. doi:10.1371/journal.pone.0118745.t001
Patients were classified into two groups using unbiased hierarchical cluster analysis for those selected 88 miRNAs (Fig. 2A). The Kaplan-Meier method showed that the probabilities in the time to first biochemical failure were significantly different between the two groups of patients with distinguished miRNA signatures (log-rank, p = 0.005) (Fig. 2B).
miRNAs predictive of PSA recurrence post-salvage RT Univariate Analysis. miRNAs that could predict biochemical failure after salvage RT treatment are of utmost interest. At this time, no studies have examined miRNAs associated with clinical outcomes following post-RP salvage radiation. The recurrence after salvage radiation treatment (also referred to as the 2nd recurrence) was defined as a rise in PSA to 0.2 ng/mL at least twice consecutively following the nadir [35]. In our patient cohort, 19 out of 43 patients experienced biochemical failure after salvage radiotherapy (Table 1). Further, using tumor miRNA expression only, we identified 4 miRNAs that were significantly expressed 1.5-fold or greater in patients who experienced a second recurrence when compared with those who did not (p-value < 0.05) (S5 Table). In the univariate log-rank analysis, 24 miRNAs were identified, which could predict patients who recurred a second time after salvage RT from those who did not recur (p-value < 0.05) (S6 Table). Multivariate Analysis. Similarly, in order to determine if miRNAs can independently predict biochemical recurrence post-salvage RT, multivariate Cox regression analyses were performed using the 24 miRNAs that were significantly associated (p-value < 0.05) with recurrence post-salvage RT by univariate log-rank analysis (S6 Table). Due to the small sample size and number of events, only two covariates were examined in the multivariate analysis. The multivariate analysis was performed using lymph node status and Gleason score (these factors were chosen as having the lowest p-values upon univariate analysis as shown in (Table 2)), leading to identification of 9 miRNAs that predicted biochemical recurrence post-salvage RT (Table 3).
Predictive effect of miR-601 + miR-4516 & Development of a miRNAbased Predictive Salvage RT Model A primary objective of this study was to identify miRNAs that could be used independently as predictive biomarkers of salvage RT as well as to utilize in combination with other clinical factors to improve the sensitivity and selectivity of existing models. We then included lymph node status, Gleason score and the 9 miRNAs as the covariates in the Cox regression model and used a stepwise model selection strategy to capture miRNAs which could be good candidates in predicting the biochemical recurrence post-salvage RT. The two miRNAs, miR-601 and miR4516 alone and together with Gleason score and lymph node status were selected by the model. An area under the curve (AUC) of receiver-operator characteristics (ROC) models using the nearest neighbor estimation method was utilized [78]. An AUC for lymph node and Gleason score was found to be 0.66, the addition of miR-601 and miR-4516 increased the AUC to 0.83 (Fig. 3). Interestingly, each miRNA alone (miR-601 AUC = 0.77 and miR-4516 AUC = 0.68) or
PLOS ONE | DOI:10.1371/journal.pone.0118745 March 11, 2015
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MiRNA-Based Predictive Model of Salvage RT Therapy Post-RP
Fig 2. 88-miRNA signature predicts early vs late biochemical recurrence post-RP. Cluster analysis was performed using the statistically significant 88 miRNAs predictive of first biochemical recurrence using Cox regression multivariable analysis (D’Amico score, Stephensen score (categorical), and Stephensen score (continuous)). This signature (A) appears to differentiate between patients with early recurrence (< 36 months) (red box) vs those with late recurrence (> 36 months). Kaplan-Meier plots were generated using the two cluster groups (B). Cluster 1 (blue), left cluster/early recurrence group; Cluster 2 (red), right cluster/late recurrence group. doi:10.1371/journal.pone.0118745.g002
together (miR-601 + miR-4516 AUC = 0.76) (Fig. 3) had a better predictive capability than the combination of positive lymph nodes together with Gleason score (AUC = 0.66) (Fig. 3). Using the risk score generated by this model (miR-4516, miR-601, Gleason score, and lymph node status), patients were classified as high or low risk groups based on median, the probabilities in recurrence post-salvage RT of the two groups were found to be significantly different (logrank, p
