EURURO Vol. 72 No. 3

Introduction

Active surveillance is a management strategy for low-grade,

localized prostate cancer that allows men to delay or be

spared the potential morbidities of treatment. Cancers that

appear to be low-risk at diagnosis are monitored, typically

with serial prostate-specific antigen (PSA) measurements,

clinical examinations, and repeat prostate biopsies. Inter-

vention is recommended on evidence of a more aggressive

tumor, usually based on changes in biopsy characteristics.

However, fear of occult high-grade cancer, in part because

of the known undersampling of systematic prostate biopsies,

has tempered widespread adoption of active surveillance.

Even with emerging magnetic resonance imaging (MRI)–

based biopsy protocols, there remains uncertainty surround-

ing the presence of more aggressive disease against a

background of apparently low-risk cancer. In addition, the

optimal surveillance schedule and triggers for intervention

have not been established, resulting in substantial variations

in the practice of active surveillance. Prostate biopsy can be

painful, anxiety-provoking, expensive, and potentially mor-

bid, so avoiding unnecessary surveillance biopsies is

attractive. Methods to reduce the number of biopsies in

active surveillance regimens, while maximizing the identi-

fication of high-grade cancers that may benefit from

treatment, would have substantial clinical utility.

A promising approach to determine active surveillance

candidacy and surveillance regimens (eg, more intensive vs

less intensive biopsy schedules) involves the addition of

biomarker panels to prediction models based on known

clinical and demographic variables

[1]

. Among men

suspected of having prostate cancer, a panel of four

kallikreins (total PSA [tPSA], free PSA [fPSA], intact PSA

[iPSA], and human kallikrein 2 [hK2]) combined with age

using a mathematical algorithm improves the prediction of

high-grade cancers compared to the PCPT risk calculator or

models using tPSA alone

[2,3]

. Here, we explore the utility of

prediction models incorporating the predefined four

kallikrein panel algorithm (4Kpanel) to predict the presence

of occult high-grade disease in men already diagnosed with

Gleason 6 cancer and on active surveillance. We use

plasma specimens and data from the prospective, multi-

institutional Canary Prostate Active Surveillance Study

(PASS).

Patients and methods

2.1.

Study cohort

This study included men from Canary PASS, a multicenter, prospective

study enrolling men on active surveillance

[4]

. Participants in PASS

consented to specimen collection as part of the PASS protocol

(clinicaltrials.gov NCT00756665), which was approved by institutional

review boards at participating sites. The PASS protocol includes

monitoring at clinic visits every 6 mo, with the first 10-core prostate

needle biopsy at 6–12 mo, the second at 24 mo after cancer diagnosis,

and subsequent biopsies every 2 yr. Specimens, including EDTA plasma,

were collected at study entry and every 6-mo clinic visit, and were stored

at 70

C until use.

In February 2015, 1170 participants were enrolled in PASS at nine

sites throughout North America. Of these, 956 participants had an on-

study biopsy, of whom 877 had Gleason 3 + 3 disease at study entry,

771 had not used 5

-reductase inhibitors, and EDTA plasma collected

before biopsy was available for 753 men. Participants with missing

prostate volume or ratio of positive to total biopsy cores were excluded

from the modeling (

= 35); the remaining 718 men, who had

1111 biopsies, were included in this study.

2.2.

Laboratory methods

Blood was collected in K

EDTA vacutainers, inverted, centrifuged at

1600

, and frozen at 70

C within 4 h of collection. Frozen plasma

was stored until shipment on dry ice to OPKO Labs (Nashville, TN, USA)

for analysis. The analysis laboratory was blinded to all specimen and

clinical information. Specimens were thawed immediately before

analysis. tPSA, fPSA, iPSA, and hK2 were measured

[2]

2.3.

Study design and analyses

The objective of the analyses was to determine whether a model using

clinical predictors and kallikrein data collected after diagnosis of Gleason

6 cancer, but before surveillance biopsy, can predict high-grade cancer in

the surveillance biopsy. Sequential surveillance biopsies were consid-

ered as two groups: (1) the initial biopsy after cancer diagnosis

(sometimes called confirmatory biopsy) and (2) all subsequent surveil-

lance biopsies. Biopsy data were split 2:1 into training and test sets

matched by outcome.

The primary outcome was reclassification from Gleason score 6 to

Gleason score 7. A value for the 4Kpanel was calculated with tPSA, fPSA,

iPSA, hk2, and age using locked down coefficients developed before the

study was conducted

[3]

. This combination of the four kallikreins is the

same as in the commercial 4Kscore. However, the commercial 4Kscore is

a model containing the 4Kpanel and clinical data available before cancer

diagnosis, and is calibrated for a patient before diagnosis. Because we

evaluated the kallikreins in a cohort already diagnosed with cancer, we

developed a new model that included the 4Kpanel and clinical

information available after a diagnosis of cancer, and calibrated to an

active surveillance population. Additional clinical predictors considered

in modeling included age, body mass index (BMI), race (African

American or other), digital rectal examination (DRE) results, number

of previous biopsies after diagnosis, number of negative biopsies after

diagnosis, core ratio (ratio of biopsy cores containing cancer to total

cores) from previous biopsy, maximum core ratio among all previous

biopsies, months since diagnosis, and prostate volume (prostate size

measured closest to the time of sampling and imputed within 2 yr).

Conclusions:

The 4Kpanel provided incremental value over routine clinical information in

predicting high-grade cancer in the first biopsy after diagnosis. The 4Kpanel did not add

predictive value to the base model at subsequent surveillance biopsies.

Patient summary:

Active surveillance is a management strategy for many low-grade

prostate cancers. Repeat biopsies monitor for previously undetected high-grade cancer.

We show that a model with clinical variables, including a panel of four kallikreins, indicates

the presence of high-grade cancer before a biopsy is performed.

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