Polygenic Risk of Breast Cancer in Latinx Populations

Elad Ziv, MD

Department of Medicine

Helen Diller Family Comprehensive Cancer Center

Institute for Human Genetics

University of California, San Francisco

Disclosures

No conflicts to disclose

Genetic effects on Breast Cancer

Common variants, modest effects

Very rare variant very large odds ratios

Relatively rare variants large effects

Increase risk by 1.05 to 1.3-fold; lifetime risk goes from 12% to 12-13% (some women’s risk will go down), at least 180 of these in large studies of European populations

Increase risk by 2-5 fold or lifetime risk goes from 12% to 20-30

Lifetime risk goes from 12% to 50-80%BRCA1,

BRCA2

PALB2

ATM, CHEK2

>180 SNPs

GWASFamily studies / Sequencing

Building a Polygenic Risk Score

SNP Minor allele frequency

OR per allele Gene

rs35054928 0.4 1.27 FGFR2

rs4784227 0.24 1.23 TOX3

rs62355902 0.16 1.18 MAP3K1

rs3757322 0.32 1.14 ESR1

rs9397437 0.07 1.32 ESR1

7.69 x Risk

Polygenic Risk

Currently some companies provide Polygenic Risk Scores for women who test negative for known susceptibility genes.

Scores are provided only to women of European ancestry.

Genetic Ancestry in Latinos

Bryc et al PNAS 2010

Building a Polygenic Risk Model in Latinas

Methods

SNP selection: SNPs 5x10-8 from prior studies for overall breast cancer (not ER+

or ER-neg BC)

PRS model construction:Use a Bayesian approach and calculate Likelihood ratio for BC for each genotype

Assume SNPs are independent

Approach automatically corrects for allele frequency differences

Lu et al AJHG 2008

Michalidou, et al Nature 2017 Fejerman, et al Nature Comm 2014

StudiesRegion Study Name Sample Sizes Age Family History Estrogen Receptor Status

Positive Negative Unknown

SF Bay Area SFBCS/NC-BCFR Controls 589 53 (11) 55 (9)* NA

Cases 942 50 (11) 190 (20)* 593 (63)§ 230 (19) 119 (13)

Kaiser RPGEH Controls 3563 55 (13) 211 (6)‡ NA

Cases 222 57 (10) 38 (17)‡ 161 (73) 29 (13) 32 (14)

LA County MEC Controls 1469 67 (8) 141 (10)* NA

Cases 532 66 (8) 73 (14)* 303 (57) 108 (20) 121 (23)

COH/CCGCRN Controls 305 52 (11) 26 (9)† NA

Cases 1039 43 (9) 348 (33)† 585 (56) 233 (22) 221 (21)

Mexico CAMA Controls 702 52 (9) 27 (4) NA

Cases 709 52 (10) 50 (7) 116 (16) 52 (7) 541 (76)

COLUMBUS Controls 453 35 (12) 34 (8)‡ NA

Cases 481 57 (13) 23 (5)‡ 140 (29) 41 (9) 300 (62)

Colombia COLUMBUS Controls 768 64 (10) ND NA

Cases 954 52 (10) 49 (5)† 354 (37) 177 (19) 423 (44)

Peru PEGEN-BC Controls 85 ND ND NA

Cases 818 50 (11) 54 (7) 548 (67) 246 (30) 24 (3)

All Controls 7934 57 (13) 494 (6) NA

Cases 5697 52 (12) 825 (14) 2800 (49) 1116 (20) 1781 (31)

Genetic Ancestry

SFBACS/NCBCFR (SF Bay Area) Kaiser RPGEH (SF Bay Area) CCGCRN (LA) MEC (LA County)

CAMA (Mexico) COLOMBUS (Mexico) COLOMBUS (Colombia) PEGEN (Peru)

Risk Models Discrimination: separation of cases from controls

Calibration: Centering of cases and controls at correct risk of disease

Good Calibration, Good Discrimination

Poor Calibration, Poor Discrimination Good Calibration, Poor Discrimination

Poor Calibration, Good Discrimination

PRS Discrimination

Shieh, Fejerman et al, JNCI 2019

180-SNP PRS* 71-SNP PRS†

Controls Cases OR (95% CI)‡ P-trend§ Controls

Cases OR (95% CI)‡ P-trend§

Continuous (per standard deviation)

7629 4658 1.58 (1.52 to 1.64) 7934 5697 1.51 (1.46 to 1.57)

Percentiles of PRS <0.001 <0.001

<10 763 192 0.44 (0.37 to 0.53) 794 276 0.54 (0.46 to 0.63)

10-20 763 233 0.54 (0.46 to 0.64) 793 347 0.68 (0.59 to 0.79)

20-30 763 321 0.74 (0.64 to 0.87) 794 377 0.74 (0.64 to 0.85)

30-40 762 352 0.82 (0.70 to 0.95) 793 429 0.84 (0.73 to 0.97)

40-60 1526 863 1 (referent) 1587 1023 1 (referent)

60-70 764 505 1.1 (1.02 to 1.34) 793 649 1.27 (1.11 to 1.45)

70-80 763 572 1.33 (1.16 to 1.52) 793 701 1.37 (1.21 to 1.56)

80-90 762 744 1.73 (1.51 to 1.97) 793 827 1.62 (1.43 to 1.83)

>90 763 876 2.03 (1.79 to 2.31) 794 1068 2.09 (1.85 to 2.35)Latinas: AUC ROC = 0.63 (0.62 to 0.64) for 180 SNP model0.61 (0.61-0.62 ) for 71 SNP model

European Ancestry AUC ROC = 0.63 (0.629-0.651) for ~313 SNP model

0.615 ( 0.608-0.616) for 75 SNP model

Mavaddat ,et al, AJHG 2019

Mavaddat ,et al, JNCI 2015

PRS by Ancestry

180 SNP PRS 71 SNP PRS

Controls Cases AUROC (95% CI)‡ P odds ratio (95% CI) Controls Cases AUROC (95% CI) P odds ratio (95% CI)‖

All 7622 4658 0.63 (0.62 to 0.64) 1.58 (1.52 to 1.64) 7927 5697 0.61 (0.61 to 0.62) 1.51 (1.46 to 1.56)

By Quartiles of Indigenous AncestryQ1

<29% 2349 721 0.63 (0.61 to 0.66) 0.56 1.67 (1.52 to 1.83) 2455 951 0.64 (0.62 to 0.66) 0.02 1.68 (1.55 to 1.83)Q2,

29 - 42% 2049 1021 0.61 (0.59 to 0.63) 1.51 (1.39 to 1.64) 2117 1289 0.60 (0.58 to 0.62) 1.44 (1.34 to 1.55)

Q3, 42– 54% 1820 1250 0.63 (0.61 to 0.65) 1.57 (1.45 to 1.69) 1869 1537 0.62 (0.60 to 0.63) 1.52 (1.41 to 1.63)

Q4, >55% 1404 1666 0.63 (0.61 to 0.65) 1.56 (1.45 to 1.68) 1486 1920 0.61 (0.59 to 0.63) 1.46 (1.36 to 1.56)

Model Calibration

Shieh, Fejerman et al, JNCI 2019

Can we improve: Signals at

Other Known Loci

0

2

4

6

8

10

-lo

g1

0(p−

valu

e)

0

20

40

60

80

100

Recom

bin

atio

n ra

te (c

M/M

b)

rs4849887

0.2

0.4

0.6

0.8

r2

TMEM185B

RALB

INHBB LINC01101

121 121.1 121.2 121.3 121.4 121.5

Position on chr2 (Mb)

Plotted SNPs

0

2

4

6

8

10

-lo

g1

0(p−

valu

e)

0

20

40

60

80

100

Reco

mbin

atio

n ra

te (cM

/Mb)rs2981582

0.2

0.4

0.6

0.8

r2

FGFR2 ATE1

123.1 123.2 123.3 123.4 123.5 123.6

Position on chr10 (Mb)

Plotted SNPs

0

2

4

6

8

10

-lo

g1

0(p−

va

lue)

0

20

40

60

80

100

Recom

bin

atio

n ra

te (c

M/M

b)

rs4808801

0.2

0.4

0.6

0.8

r2

PIK3R2

IFI30

MPV17L2

RAB3A

PDE4C

LOC729966

KIAA1683

JUND

MIR3188

LSM4

PGPEP1

GDF15

MIR3189

LRRC25

SSBP4

ISYNA1

ELL

FKBP8

KXD1

UBA52

C19orf60

CRLF1

TMEM59L

KLHL26

CRTC1

18.3 18.4 18.5 18.6 18.7 18.8

Position on chr19 (Mb)

Plotted SNPs

Future goal is to capitalize on loci such as these, but need adequate sample sizes to avoid overfitting

Variants in Intermediate Penetrance Genes in

Latinx Populations

Variants of Uncertain Significance (VUS)

Caswell-Jin Genetics in Medicine 2017

VUS in BRCA1 & BRCA2

VUS remain a significant challenge in interpretation of genetic tests

Reclassifying VUSs depends on data and lags in non-European populations

Eggington, ClinGenet 2015

Variants of Unknown Significance in gene panels

Pathogenic and likely Pathogenic Mutations in Known Breast Cancer Susceptibility Genes

Gene No.

frameshift

No.

nonsense

No.

missense

No.

splicing

Total No.

variants

ATM 3 1 2 6

BRIP1 2 2

CDH1 1 1

CHEK2 1 1 17 1 20

NF1 1 1

PALB2 14 3 1 18

PTEN 1 1

TP53 2 2

21 5 19 6 51 (4.8%)

Weitzel et al , Cancer, 2019

1054 Latinas were sequenced. Included women with breast cancer & family history (age<60 1st degree relative) or age<50 or bilateral breast cancer AND BRCA1/2 negative

Pathogenic Mutations in Known Breast Cancer Susceptibility Genes

Weitzel et al , Cancer, 2019

Gene Variant No. Alleles in

cases

/ Total

Chromosomes

in Cases (%)

No. Alleles in

Exac /

Total Chrom

ExAC144(%)

OR (95% CI) P Value

CHEK2 c.707T>C: pL236P 11/ 1104 (1.32) 35/11206 (0.31) 3.2 (1.5-6.5) 0.002

PALB2 c.2167_2168del:

p.M723fs

9/ 1104 (0.85) 5/11216 (0.045) 12.2 (3.1-50.8) 0.0001

PALB2 c.2411_2412del: p.

S804fs

3/1104 (0.28) 1/11202 (0.009) 30.4 (2.4-1582.5) 0.0027

PRS Going Forward

Gravel et al PNAS 2011

Common variants, modest effects

Relatively rare variants large effects

BRCA1, BRCA2

PALB2

ATM, CHEK2

>180 SNPs

Summary• PRS for breast cancer works well in Latinas (approximately

same as in Caucasians)

• Modest (non-significant) attenuation by ancestry

• Intermediate penetrance genes: PALB2 and CHEK2 have recurrent (founder) mutations

• Going forward, prediction from rare variants will need larger sample sizes in minority populations, complemented by functional studies

Acknowledgements

UCSF

Laura Fejerman

Yiwey Shieh

Katie Marker

Donglei Hu

Scott Huntsman

UC Davis

Luis Carvajal-Carmona

Paul Lott

Ana Estada-Florez

Guadalupe Polanco-Echeverry

City of Hope

Susan Neuhausen

Jeffrey Weitzel

USC

Chris Haiman

Stanford

Esther John

INSP Mexico

Gabriela Torres-Mejia

Kaiser

Larry Kushi

University of Tolima, Colombia

Magdalena Echeverry

Mabel E. Bohorquez

IMSS, Mexico

Javier Torres Juan

Carlos Martínez-Chéquer

INEN, Peru

Tatianna Vidaurre

Sandro CasavilcaZambrano