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. 2022 Apr;25(4):755-761.
doi: 10.1038/s41391-022-00497-7. Epub 2022 Feb 12.

Prostate cancer risk stratification improvement across multiple ancestries with new polygenic hazard score

Minh-Phuong Huynh-Le  1   2   3 Roshan Karunamuni  2   3 Chun Chieh Fan  3 Lui Asona  2 Wesley K Thompson  4   5 Maria Elena Martinez  6 Rosalind A Eeles  7   8 Zsofia Kote-Jarai  7 Kenneth R Muir  9 Artitaya Lophatananon  9 Johanna Schleutker  10   11 Nora Pashayan  12   13 Jyotsna Batra  14   15 Henrik Grönberg  16 David E Neal  17   18   19 Børge G Nordestgaard  20   21 Catherine M Tangen  22 Robert J MacInnis  23   24 Alicja Wolk  25 Demetrius Albanes  26 Christopher A Haiman  27 Ruth C Travis  28 William J Blot  29   30 Janet L Stanford  31   32 Lorelei A Mucci  33 Catharine M L West  34 Sune F Nielsen  20   21 Adam S Kibel  35 Olivier Cussenot  36   37 Sonja I Berndt  26 Stella Koutros  26 Karina Dalsgaard Sørensen  38   39 Cezary Cybulski  40 Eli Marie Grindedal  41 Florence Menegaux  42 Jong Y Park  43 Sue A Ingles  44 Christiane Maier  45 Robert J Hamilton  46   47 Barry S Rosenstein  48 Yong-Jie Lu  49 Stephen Watya  50 Ana Vega  51   52   53 Manolis Kogevinas  54   55   56 Fredrik Wiklund  16 Kathryn L Penney  57 Chad D Huff  58 Manuel R Teixeira  59   60   61 Luc Multigner  62 Robin J Leach  63 Hermann Brenner  64   65   66 Esther M John  67 Radka Kaneva  68 Christopher J Logothetis  69 Susan L Neuhausen  70 Kim De Ruyck  71 Piet Ost  72 Azad Razack  73 Lisa F Newcomb  31   74 Jay H Fowke  75 Marija Gamulin  76 Aswin Abraham  77 Frank Claessens  78 Jose Esteban Castelao  79 Paul A Townsend  80   81 Dana C Crawford  82 Gyorgy Petrovics  83   84 Ron H N van Schaik  85 Marie-Élise Parent  86   87 Jennifer J Hu  88 Wei Zheng  29 UKGPCS collaboratorsAPCB (Australian Prostate Cancer BioResource)NC-LA PCaP InvestigatorsIMPACT Study Steering Committee and CollaboratorsCanary PASS InvestigatorsProfile Study Steering CommitteePRACTICAL ConsortiumIan G Mills  89 Ole A Andreassen  90 Anders M Dale  3   90   91 Tyler M Seibert  92   93   94   95   96
Affiliations

Prostate cancer risk stratification improvement across multiple ancestries with new polygenic hazard score

Minh-Phuong Huynh-Le et al. Prostate Cancer Prostatic Dis. 2022 Apr.

Abstract

Background: Prostate cancer risk stratification using single-nucleotide polymorphisms (SNPs) demonstrates considerable promise in men of European, Asian, and African genetic ancestries, but there is still need for increased accuracy. We evaluated whether including additional SNPs in a prostate cancer polygenic hazard score (PHS) would improve associations with clinically significant prostate cancer in multi-ancestry datasets.

Methods: In total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry-the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured.

Results: The final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43-15.16] in ProtecT, 7.07 [6.58-7.60] in African ancestry, 10.31 [9.58-11.11] in Asian ancestry, and 11.18 [10.34-12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11-0.14). For the top 20% and top 5% of PHS290, the PPV of PSA testing was 0.19 (0.15-0.22) and 0.26 (0.19-0.33), respectively.

Conclusions: We demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.

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Conflict of interest statement

All authors declare no support from any organization for the submitted work except as follows: AMD and TMS report a past research grant from the US Department of Defense. OAA reports research grants from K.G Jebsen Stiftelsen, Research Council of Norway, and South East Norway Health Authority. Authors declare no financial relationships with any organizations that might have an interest in the submitted work in the previous 3 years except as follows, with all of these relationships outside the present study: TMS reports honoraria from Multimodal Imaging Services Corporation, Varian Medical Systems, and WebMD; he has an equity interest in CorTechs Labs and also serves on its Scientific Advisory Board. These companies might potentially benefit from the research results. The terms of this arrangement have been reviewed and approved by the University of California San Diego in accordance with its conflict-of-interest policies. OAA reports speaker honoraria from Lundbeck. Authors declare no other relationships or activities that could appear to have influenced the submitted work except as follows: OAA has a patent application # U. S. 20150356243 pending; AMD also applied for this patent application and assigned it to UC San Diego. AMD has additional disclosures outside the present work: founder, equity holder, and advisory board member for CorTechs Labs, Inc.; founder and equity holder in HealthLytix, Inc., advisory board member of Human Longevity, Inc.; recipient of nonfinancial research support from General Electric Healthcare. OAA is a consultant for HealthLytix, Inc. Additional acknowledgments for the PRACTICAL consortium and contributing studies are described in the Supplementary Information.

Figures

Fig. 1
Fig. 1. PPV performance in the ProtecT dataset for clinically significant prostate cancer, estimated using 3 approaches: standard (not using PHS), top 20% of PHS values (PPV80), and top 5% of PHS values (PPV95).
Error bars are 95% bootstrap confidence intervals.
Fig. 2
Fig. 2. Genetic-risk-adjusted cumulative incidence curves for PHS290.
Curves are shown for the upper 5th (>95th) and upper 20th (>80th) percentile of PHS290 for clinically significant and non-clinically-significant prostate cancer. The reference curves represent the overall population average from the UK.

References

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