Progress in Understanding the Pathology of Prostate Cancer...benign on RP) 1. Gleason DF. Urologic...
Transcript of Progress in Understanding the Pathology of Prostate Cancer...benign on RP) 1. Gleason DF. Urologic...
Progress in Understanding the Pathology of Prostate Cancer
M. Scott Lucia, MD Professor and Vice Chair of Anatomic Pathology Chief of Genitourinary and Renal Pathology Director, Prostate Diagnostic Laboratory Dept. of Pathology University of Colorado SOM
Disclosure: Genomic Health - consultant
Metastatic Potential = p X T
p = phenotype (biologic aggressiveness) - Assessed by Gleason grade, biomarkers
T = time - Reflected by volume, stage - Currently difficult to assess
Death from prostate cancer
Metastatic disease develops
Cancer spreads to lymph nodes
Cancer spreads beyond prostate
Cancer detectable—PSA>4 ng/mL Prostate cancer develops
Zone of detection when cure is possible
TIME Death
Role of Pathology
• Establish a diagnosis – Cancer – BPH, inflammation
• Determine “aggressiveness” – Grade – Perineural invasion – Biomarkers
• Predict extent – Percent cores positive – Linear extent
• Confirm diagnosis
• Determine “aggressiveness” – Grade – Perineural invasion
– Vascular invasion
– Biomarkers
• Determine extent – Stage
– Volume
– Margin status
Prostate Biopsy Prostatectomy
Assessing the Aggressiveness of Prostate Cancer on Biopsy
• Histologic grade
• Perineural invasion
• Extraprostatic disease
• Biomarkers/molecular determinants
Assessing the Aggressiveness of Prostate Cancer on Biopsy
• Histologic grade
• Perineural invasion
• Extraprostatic disease
• Biomarkers/molecular determinants
Prostatic Adenocarcinoma Gleason Grading1
• Morphologic resemblance to normal prostate
• Degree of invasiveness
• Score = most + 2nd most
• 2005 ISUP2: Grading biopsies: ─ Most + highest remaining grade
present ─ Grades 1&2 should not be used
(most upgraded or found to be benign on RP)
1. Gleason DF. Urologic Pathology: The Prostate, 1977. 2. ISUP: Amer J Surg Pathol, 2005.
Prog
ress
ion-
Free
Pro
babi
lity
Months to Progression0 40 80 120 160 200 240
0.00
0.25
0.50
0.75
1.00
GS 7
GS 5-6with tertiary 4/5
GS 5-6162128 99 64 64 77 82 60 58 47 4432
16
7 1
16
2984122
2926 12
33
3552
6972
771
3
Months to Progression0 40 80 120 160 200 240
0.00
0.25
0.50
0.75
1.00
GS 8
GS 7with tertiary 5
9 2
1
8412229
7277
GS 7
11
1
12
813
44
6
7
2
1
1
14
6952
3526
Prog
ress
ion-
Free
Pro
babi
lity
Prog
ress
ion-
Free
Pro
babi
lity
Months to Progression0 40 80 120 160 200 240
0.00
0.25
0.50
0.75
1.00
GS 7
GS 5-6with tertiary 4/5
GS 5-6162128 99 64 64 77 82 60 58 47 4432
16
7 1
16
2984122
2926 12
33
3552
6972
771
3
Months to Progression0 40 80 120 160 200 240
0.00
0.25
0.50
0.75
1.00
GS 8
GS 7with tertiary 5
9 2
1
8412229
7277
GS 7
11
1
12
813
44
6
7
2
1
1
14
6952
3526
Prog
ress
ion-
Free
Pro
babi
lity
HG = high-grade *Tertiary pattern is defined as a third Gleason pattern in a tumor that occupies less than 5% of the tumor.
Pan CC, et al. Am J Surg Pathol. 2000;24:563-9.
Significance of Tertiary (<5%) HG Gleason Pattern*
-100
0
100
0
Gleason Grade 4/5 (%)
Cumulative No-Evidenceof-Disease Rate (%)
Fail Rate (%)
1–10
11–20
21–30
31–40
41–50
51–60
61–70
71–80
81–90
91–100
-100
0
100
0
Gleason Grade 4/5 (%)
Cumulative No-Evidenceof-Disease Rate (%)
Fail Rate (%)
1–10
11–20
21–30
31–40
41–50
51–60
61–70
71–80
81–90
91–100
Stamey TA, et al. JAMA. 1999;281:1395-400. © 1999, American Medical Association.
Failure Rates as a Function of Percent Gleason Pattern 4/5 Cancer
Predicting 15-year prostate cancer specific mortality after radical prostatectomy1
PCSM (black areas) and mortality from competing causes (gray areas) by pathological Gleason score and patient age at diagnosis.
1. Eggener SE, et al. J Urol 2011;185:869-75. http://dx.doi.org/10.1016/j.juro.2010.10.057
N=23,910 across 5 institutions
Impact of grade stratification on biochemical recurrence
Pierorazio PM et al. BJU Int 2013;111:753-60. ©2013 BJU International doi:10.1111/j.1464-410X.2012.11611.x
Multivariate regression
HR (95% CI) P
Preoperative variables
Family history 0.77 (0.54-1.08) 0.132
PSA 1.06 (1.04-1.07) <0.001
cT2b 2.70(1.79-4.06) <0.001
cT2c-cT3 3.36(1.55-7.31) 0.002
Biopsy Gleason score
3 + 4 2.19 (1.35-3.56) 0.002
4 + 3 5.38 (3.33-8.68) <0.001
8 6.92 (3.99-11.98) <0.001
9-10 10.27 (5.29-19.92) <0.001
>3 cores 0.96 (0.65-1.42) 0.834
>50% positive 1.99 (1.31-3.00) 0.001
Prostate Cancer in the Contemporary Era: Does it make sense to continue to use a
2-10 scaled grading system?
• Gleason score 6 has favorable outcomes
• Gleason score 6 (low grade) is halfway between Gleason score 2 and 10 – Contributes to reluctance to choose active
surveillance
• Gleason scores 2-5 rarely used and not prognostically different from GS6
• Amount of pattern 4/5 most important for prognosis
Classification of Prostate Cancer Using 5-teired Prognostic Grade Groupings
• 2014 ISUP (Nov. 2014, Chicago) – 85 GU pathologists from 17 countries with input from
urologists
– Voted to adopt 5-teired system (90% consensus)
– Recommended that percent high grade patterns be specified for groups II and III
– Manuscript pending – stay tuned!
The overall Gleason score is based on the core with the highest Gleason score. Gleason scores can be grouped and range from Prognostic Grade Group I (most favorable) to Prognostic Grade Group V (least favorable).
Gleason score ≤ 6: Prognostic Grade Group IGleason score 3 + 4 = 7: Prognostic Grade Group IIGleason score 4 + 3 = 7: Prognostic Grade Group IIIGleason score 8: Prognostic Grade Group IVGleason score 9-10: Prognostic Grade Group V
Gleason Grading on Needle Biopsy: Limitations
• Cancer sampling is a function of tumor volume: prostate volume
– Similarly, sampling of high-grade tumor is a function of high-grade component: prostate volume
• Biopsy may not sample highest grade
3-Dimensional Reconstruction of Prostatectomy: Tumor Multifocality and Heterogeneity
Gleason Grading on Needle Biopsy: Limitations
• Cancer sampling is a function of tumor volume: prostate volume
– Similarly, sampling of high-grade tumor is a function of high-grade component: prostate volume
• Biopsy may not sample highest grade
Have consequences for choice and potential effectiveness of expectant management
Can we improve our prognostic ability through the addition of molecular biomarkers?
Prognostic Biomarkers for Prostate Cancer
• Identifying molecular markers associated with potentially aggressive cancer to aid in therapeutic decision making – Risk of progression
– Monitoring for expectant management or targeted focal therapy
• Independent of Gleason grade and biopsy sampling
• Readily available
Prognostic value of a cell cycle progression signature* for prostate cancer death in a
conservatively managed needle biopsy cohort1
1. Cuzick J et al. Br J Cancer 2012; 106(6):1095-1099. © 2012 Cancer Research UK.
*Prolaris®, Myriad Genetics, Inc.
N=349
Endpoint PCA specific death
Events 90 (26%)
Med years follow-up 11.8 (10.8, 12.7)
Median age 71 (66, 73)
Gleason <7 7 >7
106 (30%) 152 (44%) 91 (26%)
Median PSA 21.4 (11.9, 42)
Variable Hazard Ratio (95% CI)
p-value
Prolaris Score
1.65 (1.31, 2.09) <0.0001
Gleason <7 7 >7
0.61 (0.32, 1.16)
1 (ref) 1.90 (1.18, 3.07)
<0.0001
--- ---
PSA 1.37 (1.05, 1.79) 0.017
Multivariate analysis
Prognostic utility of the cell cycle progression score generated from needle biopsy in men
treated with prostatectomy1
1. Bishoff et al. J. Urology 2014; 192 (2).
A. Biochemical recurrence: Multivariate analysis: HR=4.83 (CI 95%); p<10-5
B. Metastasis-free survival: Multivariate analysis: HR=1.53 (CI 95%); p<10-4
N=582
Copyright © 2014 AUA Education and Research, Inc.
Genomic Prostate Score (GPS)*
Stromal Response BGN
COL1A1 SFRP4
Proliferation TPX2
Androgen Signaling FAM13C
KLK2 AZGP1
SRD5A2
Cellular Organization
FLNC GSN TPM2
GSTM2
Genes Associated with Better Outcome
Genes Associated with Worse Outcome
ARF1 ATP5E CLTC
GPS1 PGK1
Reference Genes
• PCR-based expression assay
• 17 gene panel • 5 reference genes • 12 genes covering
multiple pathways predictive of:
1. Metastasis & Death
when measured in RP specimens
2. Dominant grade pattern
4 & EPE/SV/LN+ when measured in biopsy specimens
* Oncotype DX®, Genomic Health, Inc
UCSF Validation Study of GPS
0 70 10 20 30 40 50 60
GPS
Very Low Low
Intermediate
10
90 80 70 60 50 40 30 20
100
0
Like
lihoo
d of
Fav
orab
le
Path
olog
y (%
)
0 70 10 20 30 40 50 60
Very Low Low
Intermediate
NCCN Intermediate = 56%
NCCN Very Low = 84%
NCCN Low = 75%
Klein EA et al, Eur Urol 2014. http://eorder.sheridan.com/30/app/orders/3732/article.php
Multivariate Analysis NCCN p-value = 0.002 GPS p-value = 0.001
Improved Risk Discrimination with Addition of GPS to NCCN in 395 Men with Very Low-Intermediate Risk Prostate Cancer on Biopsy
Fav
orab
le P
ath=
GS≤3
+4
+ p
T2
Genomic prostate score predicts adverse pathology1 at radical prostatectomy with adjustment for the clinical/
pathology covariates (n=382)
Model Variable OR 95% CI p value
1 GPS/20 units 3.23 2.14–4.97 <0.001
Biopsy Gleason score 3 + 4 vs ≤3 + 3 1.89 1.12–3.18 0.016
2* GPS/20 units 3.25 2.12–5.10 <0.001
NCCN risk group: low vs very low 3.17 1.33–8.81 0.008
Intermediate vs very low 4.52 1.81–13.03 <0.001
3** GPS/20 units 2.74 1.77–4.36 <0.001
Age at diagnosis, yr 1.06 1.02–1.09 <0.001
NCCN risk group: low vs very low 3.44 1.43–9.65 0.005
Intermediate vs very low 5.20 2.05–15.18 <0.001
CI = confidence interval; GPS = Genomic Prostate Score; OR = odds ratio; *n = 372 (NCCN risk category could not be assigned for 10 patients).
1. Adverse pathology=GS≥4+3, any pattern 5; or ≥pT3
Cullen J, et al. Eur Urol 2014. http://dx.doi.org/10.1016/j.eururo.2014.11.030
Univariable odds ratios for GPS in predicting adverse pathology at radical prostatectomy within different
clinical subgroups
Cullen J, et al. A Biopsy-based 17-gene Genomic Prostate Score Predicts Recurrence After Radical Prostatectomy and Adverse Surgical Pathology in a Racially Diverse Population of Men with Clinically Low- and Intermediate-risk Prostate Cancer . Eur Urol 2014. http://dx.doi.org/10.1016/j.eururo.2014.11.030
Risk of Progression Choice of Management
Clinical Factors: PSA
Stage
Molecular Profiling
Pathologic Factors: Grade Extent
(# pos cores, etc)
Mutational Analysis
Primary Data
Secondary Data
Other?
Current
Future?