Gavin, Anna

Gnanapragasm, Vincent J

Greenberg, David C

Lophatananon, Artitaya

Muir, Kenneth R

Wright, Karen A

Queen's University Belfast Research Portal

An improved clinical risk stratification system to better predictcancer specific mortality at diagnosis in primary non-metastaticprostate cancerGnanapragasm, V. J., Lophatananon, A., Wright, K. A., Muir, K. R., Gavin, A., & Greenberg, D. C. (2016). Animproved clinical risk stratification system to better predict cancer specific mortality at diagnosis in primary non-metastatic prostate cancer. 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Feb. 2017An	improved	clinical	risk	stra2ﬁca2on	system	to	be6er	predict	cancer	speciﬁc	mortality	at	diagnosis	in	primary	non-metasta2c	prostate	cancer.	Vincent	J	Gnanapragasam1*#,	Ar2taya	Lophatananon2*,	Karen	A	Wright3,	Kenneth	R	Muir4,	Anna	Gavin5	and	David	C	Greenberg3*.	1 Academic Urology Group, University of Cambridge, UK, 2Division of Health Sciences, University of Warwick UK, 3 Public Health England, UK, 4 Institute of Population Health, University of Manchester, UK 5 Northern Ireland Cancer Registry, UK Introduc2on		 It	is	es'mated	that	the	number	of	new	cancer	diagnosis	in	the	UK	alone	will	approach	70,000	per	annum	by	2030,	Of	this	popula'on	over	80%	will	be	men	presen'ng	with	non-metasta'c	disease.		 Risk	 stra'ﬁca'on	 is	 the	 cornerstone	 of	 management	 for	 these	men.	The	most	widely	used	stra'ﬁca'on	system	is	the	3	strata	D’Amico	classiﬁca'on	ﬁrst	described	 in	 the	 late	1990’s	 (5).	 It	 is	now	clear	 that	within	these	standard	groupings	there	exists	signiﬁcant	heterogeneity	in	outcomes	(9-10).	This	development	is	par'cularly	welcome	as	work	from	our	own	centre	and	others	have	shown	signiﬁcant	grade	inﬂa'on	in	 contemporary	 cohorts	 but	 not	 necessarily	 linked	 to	 a	 poorer	outcome	(12-13).			 A	novel	 approach	 to	 risk	 sub-stra'ﬁca'on	was	 recently	 reported	by	 the	 EMPACT	 group	 in	 high-risk	 surgically	 treated	 prostate	 cancer	(16).	This	work	demonstrated	that	beXer	and	poorer	performing	sub-groups	 could	 be	 iden'ﬁed	 by	 considering	 the	 number	 of	 prevalent	high-risk	factors	an	individual	had.		 In	this	current	study	we	explored	if	this	no'on	could	be	applied	in	other	 risk	 categories	 and	 in	 the	 context	 of	 predic'ng	 prognosis	 in	 a	primary	 diagnosis	 popula'on.	 We	 also	 considered	 the	 impending	changes	 in	 the	 pathological	 repor'ng	 system	 	 in	 the	 updated	WHO	guidelines	2016.		 Our	goal	was	to	test	whether	a	new	clinical	risk	stra'ﬁca'on	model	could	be	developed	which	would	provide	a	beXer	predic've	model	for	prostate	cancer	speciﬁc	mortality	(PCSM)	at	the	point	of	ﬁrst	diagnosis.					Pa2ents	and	methods		 Cohorts	 :	Primary	 prostate	 cancers	 (ICD10	 site:	 C61)	 diagnosed	 in	residents	 of	 the	 East	 of	 England	 Cancer	Network	 area	 between	 2000	and	2010.	Cases	with	any	metasta'c	 involvement	were	excluded.	The	median	 follow	up	was	6.9	years	 for	 the	primary	cohort.	Only	subjects	with	all	components	of	diagnos'c	stage,	primary	and	secondary	grade	and	presen'ng	PSA	 (ng/ml)	 as	well	 as	data	on	 follow	up	and	 survival	were	 included.The	 ﬁnal	 primary	 cohort	 used	 for	 tes'ng	 and	 training	sets	 comprised	 10,139	 subjects	with	 789	 prostate	 cancer	 deaths	 and	2610	 overall	 deaths.	 To	 validate	 the	 results	 we	 sourced	 an	 available	independent	 dataset	 from	 the	 Northern	 Ireland	 Cancer	 Registry	 This	valida'on	 cohort	 comprised	 1706	 subjects	 with	 43	 prostate	 cancer	deaths,	144	all	cause	deaths.				 Sta+s+cal	 analysis	 :	 Risk	 groups	 were	 ini'ally	 assigned	 as	 low,	intermediate	 and	 high-risk	 based	 on	 the	 UK	 (NICE)	 guidelines	 The	individual	variables	used	 to	deﬁne	 the	groups	 (PSA	 level,	 the	Gleason	pathological	 grade	 sum	 and	 clinical	 stage)	 were	 then	 used	 to	 sub-stra'fy	 within	 each	 risk	 category	 and	 their	 associa'on	 with	 prostate	cancer	 speciﬁc	 mortality	 (PCSM).	 In	 addi'on	 we	 used	 the	 new	 ISUP	prognos'c	 scores	as	a	discriminator.	Based	on	 this	we	derived	a	new	risk	stra'ﬁca'on	system	that	iden'ﬁed	5	poten'al	outcome	groups	for	PCSM	(Table	1).	We	then	used	a	cross	valida'on	method	to	test	and	re-test	the	model	by	genera'ng	a	random	number	seed	and	then	splicng	the	 cohort	 into	 60%	 (n=6026)	 as	 training	 set	 and	 40%	 (n=4113)	 as	tes'ng	 set.	To	compare	 survival	diﬀerences	between	each	 risk	group,	we	applied	a	 cox	hazards	model	 and	 the	 Log	 rank	 test	with	pair-wise	comparisons.	 For	 visual	 comparison	 we	 used	 Kaplan-Meier	 plots.	 To	assess	 predic'on	 performance,	 the	 Harrell’s	 concordance	 index	 (CI)	was	 computed.	 Compe'ng	 hazards	 risk	 regression	 was	 applied	 to	include	 the	 poten'al	 inﬂuence	 of	 non-cancer	 deaths	 and	 cumula've	incidence	 curves	 generated	 and	 compared	 between	 the	 risk	 groups	(SPSS	sta's'cs	version	22,	STATA/MP	12.1,	R	Commander	plug-in	EZR	(Easy	R)	version	1.23	(1)	and	R	version	3.0.1.				 New risk group  Criteria  1  Gleason 6 (Prognostic score 1)  AND  PSA <10ng/ml  AND  Stage T1-T2   2  Gleason 3+4=7 (Prognostic score 2)  OR  PSA 10-20ng/ml  AND  Stage T1-T2     3  Gleason 3+4=7 (Prognostic score 2)  AND  PSA 10-20ng/ml  AND  Stage T1-T2  OR  Gl 4+3=7 (Prognostic score 3)   4   Gleason 8 (Prognostic score 4)  OR  PSA >20ng/ml  OR  Stage T3     5   Any combination of Gleason 8 (Prognostic score 4), PSA >20ng/ml, Stage T3  OR  Gleason 9-10 (Prognostic score 5)  OR  Stage T4   	Table	1	–	Proposed	New	Prostate	Cancer	Risk	Group	criteria.	The	prognos'c	scores	refer	to	the	new	ISUP	group	grading	system.	Fig	1	A:	New	risk	criteria	applied	to	training	set.	B:	NICE	risk	groups	applied	to	training	set.	Kaplan	Meir	curves	and	95%	conﬁdence	intervals	(shaded	areas)	are	shown	for	each	risk	group	(n=	6026).	Fig	2	A:	New	risk	criteria	applied	to	tes'ng	set.	B:	NICE	risk	groups	applied	to	tes'ng	set.	Kaplan	Meir	curves	and	95%	conﬁdence	intervals	(shaded	areas)	are	shown	for	each	risk	group	(n=	4113).	Fig	3:	Cumula've	incidence	curve	for	the	whole	cohort	to	assess	compe'ng	mortality	risk	in	the	new	risk	model.	(n=10,139,	789	prostate	cancer	death,	1821	other	causes)	.	  Concordance index (confidence interval) Cohort (n) Prostate cancer deaths (%) NICE New Risk Group     Testing set (4113)  327 (7.9%) 0.67 (0.64-0.69) 0.75 (0.72-0.77) Full primary cohort (10139)  789 (7.7%) 0.69 (0.68-0.71) 0.76 (0.74-0.77) Validation cohort (1706) 43 (2.5%) 0.67 (0.64-0.70) 0.83 (0.80-0.87)     	 Table	3	–	Concordance	indices	for	prostate	cancer	speciﬁc	mortality	of	the	NICE	risk	criteria	and	the	new	risk	group	criteria	in	tes'ng	and	full	sets	as	well	as	the	valida'on	cohort.	    New risk group Sub-hazard ratio  95% CI p value     1 1 NA NA 2 1.77 1.16-2.70 0.007 3 3.54 2.38-5.26 <0.0001 4 4.97 3.47-7.12 <0.0001 5 14.34 10.05-20.46 <0.0001     	 Table	2	–	Compe'ng	risk	regression	analysis	for	the	whole	cohort	(n-10,139)	including	789	prostate	cancer	deaths	and	1821	other	cause	mortality.	Comparison	of	the	groups	is	made	with	group	1	as	the	reference.	Summary	of	Results			 In	the	en're	primary	cohort	there	were	789	prostate	cancer	deaths	within	a	median	follow	up	of	6.9	years.		 In	 the	 training	 set	 the	 new	 risk	 system	 iden'ﬁed	 dis'nct	subgroups	with	diﬀerent	risks	of	PCSM	in	pair-wise	comparison	(p<0.0001).	 Speciﬁcally,	 the	new	classiﬁca'on	 iden'ﬁed	a	very	low-risk	 group	 (Group	 1),	 a	 subgroup	 of	 intermediate-risk	cancers	with	a	low	PCSM	risk	(Group	2,	HR	1.62[0.97-2.75])	and	a	 further	 subgroup	with	 an	 increased	 PCSM	 risk	 (Group	 3,	 HR	3.35[2.04-5.59])	(p<0.0001)	(Figure	1).		 High-risk	 cancers	 were	 also	 sub-classiﬁed	 by	 the	 new	 risk	strata	into	a	beXer	and	worse	outcome	group:	Group	4	(HR	5.03	[3.25-7.80])	 and	 Group	 5	 (HR	 17.28	 [11.2-26.67])	 (p<0.0001)	(Figure	1).		 These	results	were	recapitulated	in	the	tes'ng	set	(Figure	2).		 In	 compe'ng	 risk	 regression,	 cumula've	 incidence	 curves	and	 sub-hazard	 ra'os	 con'nued	 to	 demonstrate	 a	 good	separa'on	in	survival	outcomes	(Figure	3	and	Table	3)		 Compared	 to	 NICE	 the	 new	 risk	 stra'ﬁca'on	 system	demonstrated	 an	 improved	 prognos'c	 concordance	 index	 of	0.75-0.76	 versus	 0.67-0.69	 (	 (p<0.0001).	 In	 an	 external	 cohort	the	new	system	achieved	a	concordance	index	of	0.83	(Table	3).				Conclusion		A	 novel	 and	 simple	 5	 strata	 risk	 classiﬁca'on	 system	 out-performs	the	standard	3	strata	risk	criteria	in	predic'ng	the	risk	of	 PCSM	 at	 diagnosis	 in	 men	 with	 primary	 non-metasta'c	prostate	cancer.		

An improved clinical risk stratification system to better predict cancer specific mortality at diagnosis in primary non-metastatic prostate cancer

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