# PREDICT validity for prognosis of breast cancer patients with pathogenic BRCA1/2 variants

Taru A. Muranen, Anna Morra, Sofia Khan, Daniel R. Barnes, Manjeet K. Bolla, Joe G. Dennis, Renske Keeman, Goska Leslie, Michael T. Parsons, Qin Wang, et al.

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## ► To cite this version:

Taru A. Muranen, Anna Morra, Sofia Khan, Daniel R. Barnes, Manjeet K. Bolla, et al., PREDICT validity for prognosis of breast cancer patients with pathogenic BRCA1/2 variants. npj Breast Cancer, 2023, 9 (1), 10.1038/s41523-023-00546-x. doi: 10.41908/hal-04190871

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# PREDICT validity for prognosis of breast cancer patients with pathogenic *BRCA1/2* variants

Taru A. Muranen ID 1,2,3, Anna Morra 3, Sofia Khan ID 1,4,5,6, Daniel R. Barnes 7, Manjeet K. Bolla 7, Joe Dennis ID 7, Renske Keeman ID 3, Goska Leslie ID 7, Michael T. Parsons ID 8, Qin Wang 7, Thomas U. Ahearn ID 9, Kristiina Aittomäki 10, Irene L. Andrulis ID 11,12, Banu K. Arun 13, Sabine Behrens ID 14, Katarzyna Bialkowska 15, Stig E. Bojesen 16,17,18, Nicola J. Camp 19, Jenny Chang-Claude 14,20, Kamila Czene 21, Peter Devilee ID 22,23, HEBON investigators*, Susan M. Domchek 24, Alison M. Dunning 25, Christoph Engel 26,27, D. Gareth Evans ID 28,29,30,31, Manuela Gago-Dominguez 32, Montserrat García-Closas ID 9, Anne-Marie Gerdes 33, Gord Glendon 11, Pascal Guénel 34, Eric Hahnen 35,36, Ute Hamann 37, Helen Hanson 38, Maartje J. Hooning 39, Reiner Hoppe 40,41, Louise Izatt ID 42, Anna Jakubowska ID 15,43, Paul A. James ID 44, Vessela N. Kristensen 45,46, Fiona Lalloo 30, Geoffrey J. Lindeman ID 44,47,48,49, Arto Mannermaa 50,51,52, Sara Margolin 53,54, Susan L. Neuhausen ID 55, William G. Newman ID 29,56, Paolo Peterlongo ID 57, Kelly-Anne Phillips 48,58,59,60, Miquel Angel Pujana 61, Johanna Rantala 62, Karina Rønlund 63, Emmanouil Saloustros ID 64, Rita K. Schmutzler 35,36, Andreas Schneeweis 65,66, Christian F. Singer 67, Maija Suvanto ID 1, Yen Yen Tan ID 67, Manuel R. Teixeira ID 68,69, Mads Thomassen 70, Marc Tischkowitz 71, Vishakha Tripathi 42, Barbara Wappenschmidt 35,36, Emily Zhao 7, Douglas F. Easton ID 7,25, Antonis C. Antoniou 7, Georgia Chenevix-Trench 72, Paul D. P. Pharoah ID 7,25, Marjanka K. Schmidt ID 3,73,74, Carl Blomqvist 75 and Heli Nevanlinna ID 1

We assessed the PREDICT v 2.2 for prognosis of breast cancer patients with pathogenic germline *BRCA1* and *BRCA2* variants, using follow-up data from 5453 *BRCA1/2* carriers from the Consortium of Investigators of Modifiers of *BRCA1/2* (CIMBA) and the Breast Cancer Association Consortium (BCAC). PREDICT for estrogen receptor (ER)-negative breast cancer had modest discrimination for *BRCA1* carrier patients overall (Gönen & Heller unbiased concordance 0.65 in CIMBA, 0.64 in BCAC), but it distinguished clearly the high-mortality group from lower risk categories. In an analysis of low to high risk categories by PREDICT score percentiles, the observed mortality was consistently lower than the expected mortality, but the confidence intervals always included the calibration slope. Altogether, our results encourage the use of the PREDICT ER-negative model in management of breast cancer patients with germline *BRCA1* variants. For the PREDICT ER-positive model, the discrimination was slightly lower in *BRCA2* variant carriers (concordance 0.60 in CIMBA, 0.65 in BCAC). Especially, inclusion of the tumor grade distorted the prognostic estimates. The breast cancer mortality of *BRCA2* carriers was underestimated at the low end of the PREDICT score distribution, whereas at the high end, the mortality was overestimated. These data suggest that *BRCA2* status should also be taken into consideration with tumor characteristics, when estimating the prognosis of ER-positive breast cancer patients.

npj Breast Cancer (2023)9:37; https://doi.org/10.1038/s41523-023-00546-x

The online PREDICT tool for estimating breast cancer patient prognosis has been widely adopted by clinicians during the past decade¹,². The algorithm for expected mortality for up to 10 years after breast cancer diagnosis has been validated in patient cohorts from Western Europe, North America, and South-East Asia³⁻⁸. PREDICT handles estrogen receptor (ER)-positive and ER-negative breast cancers as distinct disease entities². In either case, PREDICT estimates the prognosis according to a baseline hazard function and a proportional prognostic score, based on diagnosis age and tumor characteristics, such as size and grade, ki67 and HER2 expression, and the number of affected lymph nodes. Furthermore, the progesterone receptor expression (PgR) will be incorporated in the score in the near future⁹. In addition to expected mortality, PREDICT estimates the absolute benefit from multiple treatment lines, including adjuvant endocrine therapy, 2nd or 3rd generation chemotherapy, trastuzumab, or bisphosphonates.

Pathogenic variants in *BRCA1* and *BRCA2* confer a high lifetime risk of breast cancer and increased risk of ovarian cancer¹⁰. The *BRCA1* carrier breast tumors are characteristically triple-negative, high-grade carcinomas, whereas *BRCA2* carrier

tumors are most often positive for estrogen receptor expression (ER-positive). The *BRCA1/2* carriers are diagnosed at a younger age when compared to non-carriers¹¹, and the typical *BRCA1/2*-associated tumor characteristics are enriched in the younger age groups¹²,¹³. The overall survival rate of breast cancer patients with pathogenic *BRCA1/2* variants is lower than the survival of non-carriers¹⁴,¹⁵. However, the difference may be largely explained by differences in tumor pathology and incidence of secondary ovarian cancer¹⁶⁻¹⁸. Intriguingly, some studies have suggested that the effects associated with the conventional pathological prognostic factors could be opposite in *BRCA1/2* carriers and non-carriers. For example, decreased survival of *BRCA1/2* carriers with ER-positive breast cancer has been reported in several studies¹⁶,¹⁹⁻²². Furthermore, the relevance of tumor grade as a prognostic factor for *BRCA1/2* has been questioned repeatedly²²,²³.

We have tested PREDICT model in retrospective follow-up data from *BRCA1/2* carrier patients from the Consortium of Investigators of Modifiers of *BRCA1/2* (CIMBA) and the Breast Cancer Association Consortium (BCAC).
## RESULTS

Follow-up data was available for 2892 *BRCA1* and 1813 *BRCA2* variant carriers from CIMBA, and for 316 *BRCA1* and 432 *BRCA2* variant carriers from the BCAC. The pathology data was partially missing for many patients, but Multiple Imputation with Chained Equations (MICE, Supplementary Table 1) allowed inclusion of all patients with follow-up data. Multiple imputation requires that all statistical analyses are performed in parallel in the imputed datasets and that the analysis outputs are pooled for a final result. In the following, we use the term 'pooled' in this connotation. We performed all analyses separately for the ER-negative and ER-positive patient groups (Table 1), corresponding to the specific PREDICT models for ER-negative and ER-positive breast cancer and the characteristic tumor phenotypes of the *BRCA1* and *BRCA2* variant carriers, respectively. PREDICT scores and the expected breast cancer-associated mortality were calculated according to

algorithm v 2.2., including variables for diagnosis age, tumor grade and size, lymph node and HER2 status, adjuvant therapy, and further adjusted for progesterone receptor expression<sup>2,9,24,25</sup>. The analyses included estimating the model discrimination, re-fitting the prognostic factors in a Cox regression model with the full score as an offset, and measuring the model calibration.

## ER-negative PREDICT

The ER-negative PREDICT score was able to discriminate the better and worse surviving *BRCA1* carriers with ER-negative breast cancer. In a study-stratified analysis of the CIMBA *BRCA1* carriers, the Gönen & Heller unbiased concordance for the PREDICT score with 15-year follow-up was 0.647, whereas in the analysis of the BCAC *BRCA1* carriers the concordance was 0.637 and in the analysis of the CIMBA *BRCA2* carriers 0.568. However, the model discrimination was slightly better when follow-up was restricted to

<table><caption>**Table 1.** Tumor characteristics of the *BRCA1/2* carrier breast cancer patients from CIMBA and BCAC in ER-specific subgroups.</caption><thead><tr><th rowspan="3"></th><th colspan="4">CIMBA</th><th colspan="4">BCAC</th></tr><tr><th colspan="2">BRCA1 (ER-status known: 61.3%)</th><th colspan="2">BRCA2 (ER-status known: 68.4%)</th><th colspan="2">BRCA1 (ER-status known: 87.0%)</th><th colspan="2">BRCA2 (ER-status known: 83.8%)</th></tr><tr><th>ER- (75.7%)</th><th>ER+ (24.3%)</th><th>ER- (22.2%)</th><th>ER+ (77.8%)</th><th>ER- (71.6%)</th><th>ER+ (28.4%)</th><th>ER- (25.4%)</th><th>ER+ (74.6%)</th></tr></thead><tbody><tr><td>PgR-negative</td><td>1140<br>(95.7%)</td><td>98<br>(27.3%)</td><td>193<br>(85%)</td><td>139<br>(17.9%)</td><td>172<br>(94.5%)</td><td>24<br>(36.4%)</td><td>70<br>(84.3%)</td><td>61<br>(27.5%)</td></tr><tr><td>PgR-positive</td><td>51<br>(4.3%)</td><td>261<br>(72.7%)</td><td>34<br>(15%)</td><td>637<br>(82.1%)</td><td>10<br>(5.5%)</td><td>42<br>(63.6%)</td><td>13<br>(15.7%)</td><td>161<br>(72.5%)</td></tr><tr><td>PgR status<br>unknown</td><td>152<br>(11.3%)</td><td>71<br>(16.5%)</td><td>48<br>(17.5%)</td><td>189<br>(19.6%)</td><td>15<br>(7.6%)</td><td>12<br>(15.4%)</td><td>9<br>(9.8%)</td><td>48<br>(17.8%)</td></tr><tr><td>Her2-negative</td><td>793<br>(94.2%)</td><td>240<br>(86.6%)</td><td>146<br>(90.7%)</td><td>545<br>(88.8%)</td><td>127<br>(92%)</td><td>45<br>(91.8%)</td><td>53<br>(88.3%)</td><td>150<br>(83.8%)</td></tr><tr><td>Her2-positive</td><td>49<br>(5.8%)</td><td>37<br>(13.4%)</td><td>15<br>(9.3%)</td><td>69<br>(11.2%)</td><td>11<br>(8%)</td><td>4<br>(8.2%)</td><td>7<br>(11.7%)</td><td>29<br>(16.2%)</td></tr><tr><td>Her2 status<br>unknown</td><td>501<br>(37.3%)</td><td>153<br>(35.6%)</td><td>114<br>(41.5%)</td><td>351<br>(36.4%)</td><td>59<br>(29.9%)</td><td>29<br>(37.2%)</td><td>32<br>(34.8%)</td><td>91<br>(33.7%)</td></tr><tr><td>Grade 1</td><td>9<br>(0.9%)</td><td>19<br>(5.6%)</td><td>6<br>(2.7%)</td><td>43<br>(5.6%)</td><td>1<br>(0.6%)</td><td>8<br>(12.5%)</td><td>1<br>(1.3%)</td><td>16<br>(6.6%)</td></tr><tr><td>Grade 2</td><td>123<br>(11.9%)</td><td>115<br>(33.7%)</td><td>43<br>(19%)</td><td>382<br>(49.6%)</td><td>26<br>(15.4%)</td><td>28<br>(43.8%)</td><td>19<br>(23.8%)</td><td>132<br>(54.5%)</td></tr><tr><td>Grade 3</td><td>902<br>(87.2%)</td><td>207<br>(60.7%)</td><td>177<br>(78.3%)</td><td>345<br>(44.8%)</td><td>142<br>(84%)</td><td>28<br>(43.8%)</td><td>60<br>(75%)</td><td>94<br>(38.8%)</td></tr><tr><td>Grade unknown</td><td>309<br>(23%)</td><td>89<br>(20.7%)</td><td>49<br>(17.8%)</td><td>195<br>(20.2%)</td><td>28<br>(14.2%)</td><td>14<br>(17.9%)</td><td>12<br>(13%)</td><td>28<br>(10.4%)</td></tr><tr><td>Size &lt;=20 mm</td><td>604<br>(62.3%)</td><td>201<br>(63.2%)</td><td>124<br>(57.4%)</td><td>391<br>(55.7%)</td><td>95<br>(55.2%)</td><td>45<br>(65.2%)</td><td>39<br>(50%)</td><td>140<br>(59.3%)</td></tr><tr><td>Size &gt;20 mm &amp; &lt;=50 mm</td><td>330<br>(34.1%)</td><td>110<br>(34.6%)</td><td>86<br>(39.8%)</td><td>271<br>(38.6%)</td><td>66<br>(38.4%)</td><td>22<br>(31.9%)</td><td>33<br>(42.3%)</td><td>83<br>(35.2%)</td></tr><tr><td>Size &gt;50 mm</td><td>35<br>(3.6%)</td><td>7<br>(2.2%)</td><td>6<br>(2.8%)</td><td>40<br>(5.7%)</td><td>11<br>(6.4%)</td><td>2<br>(2.9%)</td><td>6<br>(7.7%)</td><td>13<br>(5.5%)</td></tr><tr><td>Size<br>unknown</td><td>374<br>(27.8%)</td><td>112<br>(26%)</td><td>59<br>(21.5%)</td><td>263<br>(27.3%)</td><td>25<br>(12.7%)</td><td>9<br>(11.5%)</td><td>14<br>(15.2%)</td><td>34<br>(12.6%)</td></tr><tr><td>No affected lymph nodes</td><td>769<br>(70%)</td><td>226<br>(62.3%)</td><td>152<br>(64.4%)</td><td>397<br>(48.9%)</td><td>107<br>(61.1%)</td><td>44<br>(61.1%)</td><td>54<br>(67.5%)</td><td>116<br>(49.4%)</td></tr><tr><td>Any affected lymph nodes</td><td>329<br>(30%)</td><td>137<br>(37.7%)</td><td>84<br>(35.6%)</td><td>415<br>(51.1%)</td><td>68<br>(38.9%)</td><td>28<br>(38.9%)</td><td>26<br>(32.5%)</td><td>119<br>(50.6%)</td></tr><tr><td>Lymph node status unknown</td><td>245<br>(18.2%)</td><td>67<br>(15.6%)</td><td>39<br>(14.2%)</td><td>153<br>(15.9%)</td><td>22<br>(11.2%)</td><td>6<br>(7.7%)</td><td>12<br>(13%)</td><td>35<br>(13%)</td></tr><tr><td>No metastasis at diagnosis</td><td>93<br>(95.9%)</td><td>30<br>(100%)</td><td>29<br>(100%)</td><td>76<br>(96.2%)</td><td>75<br>(93.8%)</td><td>31<br>(93.9%)</td><td>45<br>(95.7%)</td><td>132<br>(97.1%)</td></tr><tr><td>Metastasis at<br>diagnosis</td><td>4<br>(4.1%)</td><td>0<br>(0%)</td><td>0<br>(0%)</td><td>3<br>(3.8%)</td><td>5<br>(6.2%)</td><td>2<br>(6.1%)</td><td>2<br>(4.3%)</td><td>4<br>(2.9%)</td></tr><tr><td>Metastasis at<br>dg unknown</td><td>1246<br>(92.8%)</td><td>400<br>(93%)</td><td>246<br>(89.5%)</td><td>886<br>(91.8%)</td><td>117<br>(59.4%)</td><td>45<br>(57.7%)</td><td>45<br>(48.9%)</td><td>134<br>(49.6%)</td></tr></tbody></table>
<table><caption>**Table 2.** Concordance of the PREDICT model for 5-, 10-, and 15-year follow-up for patients with ER-negative breast cancer.</caption><thead><tr><th>Patient group</th><th>5-year concordance</th><th>10-year concordance</th><th>15-year concordance</th></tr></thead><tbody><tr><td>CIMBA BRCA1</td><td>0.656 (0.648–0.663)<sup>a</sup></td><td>0.651 (0.643–0.657)</td><td>0.647 (0.638–0.652)</td></tr><tr><td>CIMBA BRCA2</td><td>0.558 (0.523–0.576)</td><td>0.554 (0.536–0.568)</td><td>0.568 (0.549–0.590)</td></tr><tr><td>BCAC BRCA1</td><td>0.656 (0.646–0.668)</td><td>0.651 (0.641–0.662)</td><td>0.637 (0.626–0.650)</td></tr></tbody></table>

the first five or ten years after the diagnosis (Table 2). The Gönen & Heller unbiased concordance derives the concordance probability directly from the Cox regression model. It is not dependent on uninterrupted follow-up and is therefore more reliable than AUC statistic for estimating discrimination in censored survival data. A concordance value of 0.50 suggests that a model is as good as a random guess and value 1.0 implies perfect prediction. The Kaplan–Meier graphs of patient survival at discrete risk levels provide visual evidence on the discriminatory potential of PREDICT for ER-negative breast cancer in BRCA1 carriers (Fig. 1).

We found no significant residual hazard associated with any of the tumor characteristics, on top of the ER-negative PREDICT score (Supplementary Table 2). Furthermore, a graphical examination of a spline of age-related hazard in the CIMBA BRCA1 carriers suggested that the age-factor in the ER-negative PREDICT model fits well with the observed survival data (Supplementary Fig. 1).

The ER-negative PREDICT-algorithm overestimated breast cancer mortality in all BRCA1/2 patient groups with ER-negative breast cancer from CIMBA and BCAC (Table 3). The pooled expected mortality was outside the 95% confidence interval of the pooled observed mortality when examining either all BRCA1 or all BRCA2 patients together (Table 3, first and two last rows). Consistently, when calibration was tested in CIMBA patient subgroups dichotomized by tumor size, grade, HER2 expression, node status, or in three distinct age categories, the expected mortality was higher than the observed mortality (Table 3). A calibration plot of low-to-high PREDICT percentiles in the BRCA1 carriers with ER-negative breast cancer suggested a mild but consistent overestimation of 10-year mortality, with good separation of the middle-high (50–80%ile) and high (80–100%ile) mortality categories from middle-low (20–50%ile) and low categories (0–20%ile) (Fig. 2a). However, the difference between expected and observed mortality was slightly alleviated with a longer, 15-year, follow-up time (Supplementary Fig. 2).

In summary, the PREDICT score predicted survival with modest precision in the ER-negative BRCA1 carrier patients, although it tended to overestimate mortality throughout all risk levels. The prognostic impact of the individual risk factors in the PREDICT model did not deviate significantly from those of the PREDICT algorithm and the high-risk patients were identified well. Thus, the PREDICT model estimated the mortality risk in ER-negative BRCA1 carriers with moderate accuracy, whereas for ER-negative BRCA2-carriers the analysis was indecisive, due to small cohort size.

## ER-positive PREDICT score

The ability of the PREDICT ER-positive model to discriminate BRCA1/2 carriers was quite low in the CIMBA data, with Gönen & Heller concordance 0.601 for BRCA2 carriers and 0.551 for BRCA1 carriers, for follow-up time of 15 years after diagnosis, and equally poor for shorter follow-up of 10 years (Table 4). This was evident also in a modest separation of the Kaplan–Meier curves of BRCA1/2 carriers with ER-positive breast cancer in different PREDICT percentile-based risk categories (Fig. 3). However, in the smaller dataset of BRCA2 carriers from BCAC, the discrimination was higher, ranging from 0.665 for 5-year follow-up to 0.648 for 15-year follow-up (Table 4).

When the pathologic factors, included in the PREDICT score, were refitted in a Cox regression model with the PREDICT score as an offset, to explain the survival of the CIMBA BRCA2 carriers, the tumor grade had significant residual hazard in opposite direction to the coefficients embedded in the PREDICT (Table 5). A marginal residual hazard to opposite direction was seen also for PgR status, tumor size, and the number of affected lymph nodes, suggesting an overall poor fit of the PREDICT ER-positive score for the BRCA2 carriers from CIMBA. When grade was removed from the PREDICT score, and included as an independent categorical covariate in a Cox regression model, offsetting with the reduced score, no significant difference was associated with either grade 3 or grade 1 when compared to grade 2 (Supplementary Table 3). Consequently, excluding grade from the PREDICT score improved the score concordance in CIMBA BRCA2 carriers from 0.601 to 0.610, but also in BCAC BRCA2 carriers from 0.648 to 0.658, suggesting that the tumor grade have little value in the prognosis of BRCA2 carriers. A similar trend was seen also when restricting the follow-up time to ten years after diagnosis (Table 4).

The PREDICT ER-positive score includes a non-linear component for diagnosis age, with steeply increasing hazard for ages younger than 40 years, and moderately increasing hazard for ages above 50 years. The relative hazard associated with diagnosis age had a milder curve in the CIMBA BRCA2 carriers, when modeled with a spline. However, the PREDICT estimate was within the 95% confidence interval of the spline across ages 20 to 70 years (Supplementary Fig. 3).

The overall 10-year observed mortality of the BRCA1/2 carriers with ER-positive breast cancer did not differ significantly from the PREDICT point estimate of expected mortality, either in data from CIMBA or BCAC (Table 6). However, a calibration plot of low to high risk categories of PREDICT percentiles (0–20%ile, 20–50%ile, 50–80%ile, 80–100%ile), suggested that PREDICT underestimated BRCA2 carrier 10-year mortality in the lower risk categories, whereas in the high risk category, the observed mortality was significantly lower than the expected mortality (Fig. 2b). A longer follow-up time of 15-years did not affect the pattern (Supplementary Fig. 4). When examined in subgroups dichotomized by tumor pathology, the patients with grade 3 or node-positive tumors had lower mortality than expected, but patients with either grade 2 or node-negative tumors had higher mortality than expected (Table 6).

In summary, the accuracy of the PREDICT score for estimating the survival in ER-positive patients was lower than the accuracy in the ER-negative population. Although the PREDICT model estimated the average survival in the whole ER-positive patient population with moderate accuracy, the model did not reliably discriminate the low- and high-risk groups. Especially, the prognostic impact of the tumor grade deviated highly significantly from the PREDICT model, possibly reflecting underlying differences in the impact of tumor grade on prognosis in BRCA1/2 carriers when compared to the patient populations on which the PREDICT model is based. In fact, the survival of BRCA2 carriers with grade 3 tumors was similar to survival of BRCA2 carriers with grade 2 tumors. Thus, the accuracy of the PREDICT model for estimating mortality risk in ER-positive BRCA1- or BRCA2-carriers was sub-optimal.

<footnote>ªInterquartile range of concordance estimates from imputed datasets in parenthesis.</footnote>
<figure><img src="image_4.png" /><figcaption>**Fig. 1** Survival of patients at distinct ER-negative PREDICT score percentiles. Kaplan-Meier survival estimator curves of *BRCA1* carriers with ER-negative breast cancer from those a CIMBA, b BCAC studies, which provided known cause of death for at least 80% of deceased patients. Similar visualization of the *BRCA2* carriers with ER-negative breast cancer from c CIMBA, d BCAC.</figcaption></figure>

DISCUSSION

The primary motivation of PREDICT has been to provide a tool for clinicians to numerically estimate the benefit from adjuvant therapy. The relative benefit from adjuvant therapy is similar at all risk levels, but the absolute benefit is higher for patients at high risk of recurrence or cancer-associated death, making the risk of adverse side effects more acceptable in this group². The algorithm was trained on a prospective population-based cohort from the UK, but multiple validation studies indicate that PREDICT gives reliable estimates also in many other populations⁶,⁸,²⁶, despite significant differences in the baseline survival rates between countries²⁷. Our analyses suggest, that the PREDICT ER-negative model is equally valid for management of *BRCA1/2* variant carriers with ER-negative breast

cancer, but sub-optimal for estimating the prognosis of ER-
positive breast cancer.

Previous validation analyses of PREDICT version 2 have
measured the discrimination with AUC (area under curve)
-statistics, ranging from 0.696 to 0.75 for the ER-negative model<sup>2,8</sup>.
The concordance in the *BRCA1* carrier data was lower: 0.65 in data
from CIMBA and 0.64 in data from BCAC, but sufficient to
discriminate especially the poor survival group of the *BRCA1*
patients (Fig. 2).

Despite the good discrimination, PREDICT seemed to over-
estimate the risk of breast cancer-specific death. The difference
between expected and observed mortality was about 7–8
percentage points ten years after diagnosis, but decreased with
a longer follow-up time of 15 years (Table 3, Fig. 2a,
<table><caption>**Table 3.** ER-negative PREDICT calibration measured by comparing the expected and observed breast 10-year cancer-specific mortality in patient groups with ER-negative breast cancer.</caption><thead><tr><th>Patient group</th><th>Expected BC mortality</th><th>Observed BC mortality</th><th>95% CI of observed BC mortality</th></tr></thead><tbody><tr><td>CIMBA BRCA1</td><td>0.23</td><td>0.16</td><td>0.13-0.19</td></tr><tr><td>Node-negative BRCA1<sup>a</sup></td><td>0.18</td><td>0.12</td><td>0.08-0.15</td></tr><tr><td>Node-positive BRCA1<sup>a</sup></td><td>0.50</td><td>0.45</td><td>0.33-0.55</td></tr><tr><td>Grade 2 BRCA1<sup>a,b</sup></td><td>0.21</td><td>0.16</td><td>0.07-0.24</td></tr><tr><td>Grade 3 BRCA1<sup>a,b</sup></td><td>0.23</td><td>0.16</td><td>0.13-0.19</td></tr><tr><td>Tumor size &lt;= 20 mm BRCA1<sup>a</sup></td><td>0.18</td><td>0.11</td><td>0.07-0.14</td></tr><tr><td>Tumor size &gt;20 mm BRCA1<sup>a</sup></td><td>0.30</td><td>0.24</td><td>0.18-0.29</td></tr><tr><td>HER2-negative<sup>a</sup></td><td>0.22</td><td>0.16</td><td>0.13-0.20</td></tr><tr><td>HER2-positive<sup>a</sup></td><td>0.27</td><td>0.11</td><td>0.02-0.19</td></tr><tr><td>Younger than 35 years BRCA1<sup>a</sup></td><td>0.22</td><td>0.13</td><td>0.08-0.18</td></tr><tr><td>35-44 years old BRCA1<sup>a</sup></td><td>0.23</td><td>0.16</td><td>0.12-0.20</td></tr><tr><td>45 years old or older BRCA1<sup>a</sup></td><td>0.24</td><td>0.17</td><td>0.12-0.22</td></tr><tr><td>CIMBA BRCA2</td><td>0.25</td><td>0.15</td><td>0.08-0.21</td></tr><tr><td>BCAC BRCA1</td><td>0.28</td><td>0.21</td><td>0.14-0.27</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Fig. 2 PREDICT calibration in CIMBA data.** Point estimates and pooled 95% confidence intervals of observed mortality against expected mortality for a BRCA1 carriers with ER-negative breast cancer, and b BRCA2 carriers with ER-positive breast cancer.</figcaption></figure>

Supplementary Fig. 2). Van Maaren et al. previously reasoned that a difference of this magnitude has clinical impact, because it is sufficiently large to affect the treatment choice, whether to administer adjuvant chemotherapy<sup>8</sup>. However, over-estimating mortality is less detrimental than underestimating, because it does not risk the access to a sufficiently efficient adjuvant therapy. Of the CIMBA BRCA1 carrier patients with ER-negative breast cancer, who had adjuvant therapy recorded in the data (none/any), about 90% had received adjuvant chemotherapy, even at the lowest risk category (PREDICT 0-20%ile). A beneficial treatment response is one possible explanation for the difference between expected and observed mortality, even though the expected benefit from adjuvant therapy was embedded in the PREDICT score. The difference may also have arisen from the imputation process. M-status was missing for a substantial number of patients (Table 1). Filtering the patients with imputed M-status may have caused loss of early events. However, the expected-observed difference

was equally large in BCAC, where the M-status was more frequently available. Thus, this does not appear as a major source of bias, though it warrants caution in interpretation. Furthermore, the expected-observed difference is in keeping with a recent study, where the survival of BRCA1 carriers breast cancer was nominally higher than survival of non-carriers in pathology- and treatment-adjusted analysis of patients with ER-negative breast cancer<sup>18</sup>.

BRCA2 variant carrier cancers are characteristically ER-positive. However, a recent study suggested that germline BRCA2 variants increase also the risk of triple-negative breast cancer, which is generally considered a poor-prognosis breast cancer subtype<sup>13</sup>. Our analyses on PREDICT in BRCA2 carriers with ER-negative breast cancer were indecisive. The discrimination was low (0.568, Table 2), and breast cancer-associated survival good, with lower than expected mortality, similarly to the BRCA1 carriers with ER-negative breast cancer (Table 3, Fig. 1).

<footnote><sup>a</sup>Subgroups of CIMBA BRCA1 patients with ER-negative breast cancer.</footnote>

<footnote><sup>b</sup>Due to low number of patients with grade 1 breast cancer (see Table 1), this subgroup was not separately analyzed for calibration.</footnote>
<table><caption>**Table 4.** Concordance of the PREDICT model for 5-, 10-, and 15-year follow-up for patients with ER-positive breast cancer.</caption><thead><tr><th rowspan="2">Patient group</th><th colspan="2">5-year concordance</th><th colspan="2">10-year concordance</th><th colspan="2">15-year concordance</th></tr><tr><th>ER+ PREDICT</th><th>reduced model<sup>a</sup></th><th>ER+ PREDICT</th><th>reduced model</th><th>ER+ PREDICT</th><th>reduced model</th></tr></thead><tbody><tr><td>CIMBA BRCA2</td><td>0.577<br>(0.560-0.589)<sup>b</sup></td><td>0.587<br>(0.574-0.596)</td><td>0.604<br>(0.596-0.613)</td><td>0.615<br>(0.608-0.622)</td><td>0.601<br>(0.592-0.610)</td><td>0.610<br>(0.603-0.616)</td></tr><tr><td>CIMBA BRCA1</td><td>0.620<br>(0.587-0.643)</td><td>0.593<br>(0.573-0.618)</td><td>0.565<br>(0.533-0.580)</td><td>0.564<br>(0.539-0.581)</td><td>0.551<br>(0.531-0.570)</td><td>0.556<br>(0.536-0.573)</td></tr><tr><td>BCAC BRCA2</td><td>0.665<br>(0.655-0.674)</td><td>0.657<br>(0.648-0.664)</td><td>0.653<br>(0.649-0.661)</td><td>0.657<br>(0.649-0.664)</td><td>0.648<br>(0.642-0.652)</td><td>0.658<br>(0.650-0.662)</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>**Fig. 3** Survival of patients at distinct ER-positive PREDICT score percentiles. Kaplan-Meier survival estimator curves of BRCA2 carriers with ER-positive breast cancer from merged a CIMBA, b BCAC studies, which provided known cause of death for at least 80% of deceased patients. BRCA1 carriers with ER-positive breast cancer from c CIMBA, d from BCAC.</figcaption></figure>

<footnote><sup>a</sup>Reduced model includes all factors of the ER + PREDICT model, except the tumor grade.</footnote>

<footnote><sup>b</sup>Interquartile range of concordance estimates from imputed datasets in parenthesis.</footnote>
In previous studies, validating the PREDICT model in cohorts of unselected breast cancer patients, the discrimination of the ER-positive model has consistently been higher than the discrimination of the ER-negative model, with AUC-statistics between 0.74 and 0.79<sup>2,8</sup>. In that respect, the PREDICT concordance of 0.60 in the CIMBA BRCA2 carriers with ER-positive breast cancer appeared strikingly low.

The offset- and the calibration-analyses indicated that especially the tumor grade appeared to confuse the PREDICT ER-positive score, when predicting the BRCA2 variant carrier survival, whereas the factors related to the stage of malignant progression, like tumor size and node involvement, retained their predictive potential. These observations were made in the CIMBA data, but as omitting grade from the score improved its discrimination also in the BCAC data, we can conclude that the same trend is present also there. Earlier studies have suggested that the survival of BRCA2 carrier patients does not vary by tumor grade, after other pathologic factors have been taken into account<sup>21-23</sup>. In our analysis, where tumor grade was an independent covariate, the hazard associated with grade 3 in comparison to grade 2 was nominally lower, with a pooled P-value close to the significance

threshold. However, in this kind of retrospective data, the observed survival differences cannot be separated from the treatment choice. Grade 3 BRCA2 carriers had received more often adjuvant chemotherapy or combined chemo-endocrine therapy than grade 2 patients (Supplementary Table 4), and the underlying differences in therapeutic practices for grade 2 and grade 3 ER-positive cancers may have contributed to the nominally lower survival of the grade 2 patients.

The overall calibration of the PREDICT in BRCA2 carriers with ER-positive breast cancer was good. However, the calibration varied by the magnitude of the PREDICT score, the observed mortality being higher than the expected, especially in the lower-risk groups (Table 6, Fig. 2b). A recent BCAC study, comparing the BRCA1/2 carrier survival to survival of population matched non-carriers, found BRCA2 pathogenic variants to be associated with decreased patient survival after ER-positive breast cancer<sup>18</sup>. Our analyses suggest that this difference would be emphasized in patient groups with milder clinical characteristics. Therefore, the PREDICT model does not appear well-suited for the management of BRCA2 carriers with ER-positive breast cancer. Similarly, the concordance of 0.55 does not provide much support for the PREDICT model in the management of BRCA1 variant carriers with ER-positive breast cancer, either.

As the purpose of the PREDICT is to aid in the decision on adjuvant therapy, the fundamental question in our study was, whether the BRCA1/2 carriers could be managed the same way as non-carriers, and especially, is the BRCA1/2 carrier status such vital information, that genotyping the patients prior to therapy would be advisable. Strong family history of breast and ovarian cancer indicates high likelihood of germline BRCA1 or BRCA2 pathogenic variant. However, genotyping to explore the causes of the familial risk may take place only after the management of the proband's primary cancer. Furthermore, not all carriers have such family structure or records that would reveal the increased hereditary risk. Therefore, it's likely that many variant carriers with breast cancer are treated without knowledge about the carrier status. The characteristic mutational signature of the BRCA1/2 variant carrier cancer, homologous recombination deficiency<sup>28</sup>, makes the cancers responsive to platinum-based therapy or PARP-

<table><caption>**Table 5.** Residual hazard associated with PREDICT covariates.</caption><thead><tr><th>Refitted factors</th><th>HR</th><th>95% CI</th><th>P</th></tr></thead><tbody><tr><td>Diagnosis age (years)</td><td>1.01</td><td>0.99-1.03</td><td>0.27</td></tr><tr><td>Diagnosis year (1990-2011)</td><td>0.98</td><td>0.94-1.02</td><td>0.37</td></tr><tr><td>Tumor grade</td><td>0.42</td><td>0.32-0.56</td><td>1.8e-06</td></tr><tr><td>Progesterone receptor expression</td><td>1.75</td><td>1.12-2.75</td><td>0.04</td></tr><tr><td>HER2 expression</td><td>0.65</td><td>0.34-1.25</td><td>0.29</td></tr><tr><td>Tumor size (mm, log-transformed)</td><td>0.71</td><td>0.54-0.93</td><td>0.046</td></tr><tr><td>Positive lymph node count</td><td>0.96</td><td>0.93-1.00</td><td>0.096</td></tr></tbody></table>

All covariates were refitted in a country-stratified multivariable Cox regression model, offsetting with the ER-positive PREDICT score. The model was fitted to data from CIMBA BRCA2 carriers with ER-positive breast cancer.

<table><caption>**Table 6.** ER-positive PREDICT calibration measured by comparing the expected and observed 10-year breast cancer-specific mortality in patient groups with ER-positive breast cancer.</caption><thead><tr><th>Patient group</th><th>Expected BC mortality</th><th>Observed BC mortality</th><th>95% CI of observed BC mortality</th></tr></thead><tbody><tr><td>CIMBA BRCA2</td><td>0.18</td><td>0.19</td><td>0.15-0.23</td></tr><tr><td>Node-negative BRCA2<sup>a</sup></td><td>0.10</td><td>0.16</td><td>0.12-0.20</td></tr><tr><td>Node-positive BRCA2<sup>a</sup></td><td>0.33</td><td>0.25</td><td>0.19-0.31</td></tr><tr><td>Grade 2 BRCA2<sup>a,b</sup></td><td>0.12</td><td>0.20</td><td>0.15-0.25</td></tr><tr><td>Grade 3 BRCA2<sup>a,b</sup></td><td>0.27</td><td>0.18</td><td>0.13-0.24</td></tr><tr><td>Tumor size &lt;= 20 mm BRCA2<sup>a</sup></td><td>0.10</td><td>0.13</td><td>0.08-0.17</td></tr><tr><td>Tumor size &gt;20 mm BRCA2<sup>a</sup></td><td>0.29</td><td>0.28</td><td>0.21-0.34</td></tr><tr><td>PgR-negative BRCA2<sup>a</sup></td><td>0.25</td><td>0.16</td><td>0.06-0.24</td></tr><tr><td>PgR-positive BRCA2<sup>a</sup></td><td>0.17</td><td>0.20</td><td>0.16-0.24</td></tr><tr><td>HER2-negative BRCA2<sup>a</sup></td><td>0.18</td><td>0.20</td><td>0.16-0.23</td></tr><tr><td>HER2-positive BRCA2<sup>a</sup></td><td>0.23</td><td>0.14</td><td>0.02-0.25</td></tr><tr><td>Younger than 35 years BRCA2<sup>a</sup></td><td>0.31</td><td>0.19</td><td>0.10-0.28</td></tr><tr><td>35-44 years old BRCA2<sup>a</sup></td><td>0.17</td><td>0.21</td><td>0.15-0.27</td></tr><tr><td>45 years old or older BRCA2<sup>a</sup></td><td>0.15</td><td>0.18</td><td>0.13-0.22</td></tr><tr><td>CIMBA BRCA1</td><td>0.17</td><td>0.13</td><td>0.08-0.18</td></tr><tr><td>BCAC BRCA2</td><td>0.20</td><td>0.22</td><td>0.16-0.27</td></tr></tbody></table>

<footnote><sup>a</sup>Subgroups of CIMBA BRCA2 patients with ER-positive breast cancer.</footnote>

<footnote><sup>b</sup>Due to low number of patients with grade 1 breast cancer (Table 1), this subgroup was not separately analyzed for calibration.</footnote>
inhibitors<sup>29,30</sup>, but most of the carriers are still treated according to standard indications<sup>31</sup>. Retrospective analyses have suggested that the benefit from the standard adjuvant chemotherapy regimens are similar for *BRCA1/2* carriers and non-carriers, but the benefit from adjuvant endocrine therapy is limited<sup>32,33</sup>. Instead, oophorectomy has recently been suggested to reduce breast cancer recurrence and mortality of both *BRCA1* and *BRCA2* variant carriers<sup>32-34</sup>. In our study, the breast cancer-associated mortality of *BRCA2* carriers with ER-positive breast cancer was higher than expected in a patient group, where adjuvant chemotherapy was less-frequently used, but lower than expected in the high-risk patient group where adjuvant chemotherapy was used more often (Supplementary Table 4).

The strengths of this study include a large number of cases with pathogenic *BRCA1/2* germline variants and a stratified analysis of multiple cohorts from Europe, Northern America, and Australia. The study limitations include late recruitment of some patients and notable proportion of missing pathology and treatment data. To alleviate these shortcomings, the collected data has been harmonized and curated. Especially, we ensured that the number of patients under observations right after diagnosis was suffi- ciently high for an unbiased survival analysis. Furthermore, we applied statistical methods, like multiple imputation and left truncation to achieve robust conclusions. However, we were not able to address all nuances related to breast cancer diagnosis and management, like the presence of micrometastases, the duration of endocrine therapy, or administration of neoadjuvant che- motherapy. It's worth noting that the PREDICT was not trained with a cohort that would have included patients treated with neoadjuvant therapy. We run a sensitivity analysis to exclude cases with known or imputed neoadjuvant chemotherapy. The results were essentially similar to the results of the main analyses, supporting the conclusions presented above.

The PREDICT ER-negative model gives reliable estimates, but the ER-positive model is less well-suited for *BRCA1/2* carriers. Especially, our analyses indicate *BRCA2* carriers a specific group of breast cancer patients, for whom the conventional prognostic estimation is not well-suited. Altogether, our findings encourage including the information on germ-line pathogenic *BRCA1/2* variants into the decision making for adjuvant therapy regimens of breast cancer patients.

# METHODS

## Study subjects

The study subjects included female breast cancer patients of European ethnic origin enrolled into studies participating in the CIMBA (Table 7). For these analyses, the *BRCA1/2* carrier patients were considered eligible, if they were diagnosed with primary breast cancer under the age of 70 years, at 1990 or later, and had the following data available: follow-up time after the first invasive breast cancer diagnosis, status (dead/alive) at the end of follow-up, time of DNA sample collection, diagnosis age, and diagnosis year. Study subjects with previous ovarian cancer diagnosis or those included in the BCAC studies (see below) were excluded from CIMBA. This yielded data from 2892 *BRCA1*, 1813 *BRCA2* pathogenic variant carriers with breast cancer. The number of patients under observation right after diagnosis was 836, reached maximum, 2066, at about 4 years after diagnosis, steadily decreasing to 800 under observation 15 years after diagnosis.

Separate validation was performed in an independent set of *BRCA1/2* variant carriers from the BCAC. The variant carrier status was confirmed in gene panel sequencing as a part of the BRIDGES project<sup>35</sup>. Patients with *BRCA1/2* pathogenic or likely pathogenic variants (class 4 and 5) were included in the analyses<sup>36</sup>. Variant classification was downloaded from ClinVar in June 2020. The BCAC data came from patients enrolled for their first invasive

breast cancer and included 316 *BRCA1* and 432 *BRCA2* variant carriers (Table 7). The number of patients under observation right after diagnosis was 229, reached maximum, 538, at about 4 years after diagnosis, steadily decreasing to 155 under observation 15 years after diagnosis.

The study was compliant with the Helsinki declaration. All participating studies were approved by their appropriate institu- tional review boards (Table 7), following their national guidelines for informed consent. The details on study-wise informed consent policies are provided in Table 7.

## Phenotype data

All available pathology, treatment, and follow-up data were retrieved from the consortium databases (CIMBA database version 2016, BCAC database release 13). Since these data were incomplete, we applied Multiple Imputation by Chained Equations (MICE) for imputation of the missing values, so that we were able to calculate the PREDICT scores for all patients with available survival data. We assumed that due to the complex relations between the variables, a maximally large sample of observed data would provide the best foundation for imputation. Therefore, additional data from 2138 *BRCA1/2* carriers from CIMBA as well as from 126 *BRCA1/2* carriers and 32912 non-carriers (including BRCA1/2 variants of unknown significance) from BCAC were included to support the imputation process (Table 7). Data management and statistical analyses were performed with R environment for statistical computing, version 4.0.0<sup>37</sup>.

We imputed missing data into 50 parallel data matrices with R library *mice*<sup>38</sup>. The pathology, treatment, and follow-up data from CIMBA and BCAC were harmonized in terms of variable names, types, and coding, and then combined. A Nelson-Aalen estimate of cumulative hazard of overall and breast cancer-specific survival until the end of follow-up time was calculated for all patients with available follow-up time and used in the imputation process. The Nelson-Aalen estimator for breast cancer-specific survival was defined on the basis of studies, which provided the cause of death (BC/other) for at least 80% of deceased patients. The prediction matrix, defining the relations of the imputed features, was initiated by pairwise correlation between the features, defined as Spearman rank correlation >0.125, and further modified as follows. Mutual prediction was forced between ER-status and PR-status, as well as ER-status and tumor morphology. Diagnosis year was not allowed to predict HER2-status. The tumor size category was predicted by correlated features, but tumor size in mm (log-scale) was predicted only by the size category (Supplementary Table 1). Data was post-processed so that adjuvant therapy subtypes were not positive, if the main type (chemo- or endocrine therapy) was not positive. Trastuzumab treatment was not allowed before year 1997. The imputed data was checked by cross-tabulation, to assure that the relations between covariables and the *BRCA1/2* specific features were retained.

## PREDICT scores

The PREDICT scores were calculated according to the functions presented at the PREDICT website (https://breast predict.nhs.uk/legal/algorithm, accessed 2021-09-17), including coefficients or functions for diagnosis age, tumor grade (1, 2, 3), tumor largest diameter in mm, positive lymph node count, HER2 status (dichotomous), and fixed coefficients for adjuvant chemo- and endocrine therapy<sup>2,24,25</sup>. Furthermore, the coefficients associated with positive progesterone expression was added as suggested in Grootes et al.<sup>9</sup>. Patients with positive M-status (metastasis at diagnosis) were excluded after multiple imputation, since PREDICT is not applicable for M1 patients. The M-status was missing for a very high proportion of patients (Table 1) and the least biased approach in the context of multiple imputation is to filter the data only after the imputation process. Ki67 data was not available, and
<table><caption>**Table 7.** Contributing studies.</caption><thead><tr><th>Consortium</th><th>Study</th><th>Acronym</th><th>Country</th><th>Study design</th><th>Name of the institutional review board (IRB)</th><th>Informed consent policy</th><th>Study subjects</th><th>Pathology data to support imputation</th></tr></thead><tbody><tr><td rowspan="12">CIMBA</td><td>Australia</td><td>Kathleen Cuningham Consortium for Research into Familial Breast Cancer</td><td>KCONFAB</td><td>Australia</td><td>Cancer clinic-based study</td><td>Peter MacCallum Cancer Centre Ethics Committee</td><td>WIC</td><td>315</td><td>246</td><td>64</td></tr><tr><td>Austria</td><td>Victorian Familial Cancer Trials Group</td><td>VFCTG</td><td>Australia</td><td>Cancer clinic-based study</td><td>Peter MacCallum Cancer Centre Ethics Committee</td><td>IRB<sup>a</sup></td><td>67</td><td>52</td><td>52</td></tr><tr><td>Austria</td><td>General Hospital Vienna</td><td>MUV</td><td>Austria</td><td>Cancer clinic-based study</td><td>Ethikkommission der Medizinischen Universität Wien</td><td>WIC</td><td>189</td><td>99</td><td>58</td></tr><tr><td>Denmark</td><td>Copenhagen Breast Cancer Study</td><td>CBCS</td><td>Denmark</td><td>Cancer clinic-based study</td><td>De Videnskabsetiske Komiteer I Region Hovedsladen</td><td>IRB<sup>b</sup></td><td>109</td><td>68</td><td>24</td></tr><tr><td>Denmark</td><td>Odense University Hospital</td><td>OUH</td><td>Denmark</td><td>Cancer clinic-based study</td><td>Den Videnskabsetiske Komité for Region Syddanmark</td><td>IRB<sup>b</sup></td><td>169</td><td>135</td><td>41</td></tr><tr><td>Finland</td><td>Helsinki Breast Cancer Study</td><td>HEBCS</td><td>Finland</td><td>Cancer clinic-based study</td><td>Helsingin ja uudenmaan saraanhoitopiriri (Helsinki University Central Hospital ethics committee)</td><td>WIC</td><td>48</td><td>56</td><td>13</td></tr><tr><td>Germany</td><td>German Familial Breast Group</td><td>GC-HBOC</td><td>Germany</td><td>Cancer clinic-based study</td><td>Ethik-Kommission der Medizinischen Fakultät der Universität zu Köln</td><td>WIC</td><td>398</td><td>231</td><td>1149</td></tr><tr><td>Netherlands</td><td>Hereditary Breast and Ovarian cancer study the Netherlands</td><td>HEBON</td><td>Netherlands</td><td>Cancer clinic-based study</td><td>Protocol Toetsingscommissie van het Nederlands Kanker Instituut/ Antoni van Leeuwenhoek Ziekenhuis</td><td>WIC</td><td>274</td><td>103</td><td>38</td></tr><tr><td>North America</td><td>Ontario site of the Breast Cancer Family Registry/Ontario Cancer Genetics Network</td><td>BCFR-ON/ OCGN</td><td>Canada</td><td>Cancer clinic- and population-based study</td><td>Mount Sinai Hospital Research Ethics Board</td><td>WIC</td><td>45</td><td>77</td><td>77</td></tr><tr><td>Poland</td><td>City of Hope Cancer Center</td><td>COH</td><td>USA</td><td>Cancer clinic-based study</td><td>City of Hope Institutional Review Board</td><td>WIC</td><td>94</td><td>6</td><td>6</td></tr><tr><td>Poland</td><td>Dana Farber Cancer Institute</td><td>DFCI</td><td>USA</td><td>Cancer clinic-based study</td><td>Dana Farber Cancer Institute Institutional Review Board</td><td>WIC</td><td>58</td><td>43</td><td>14</td></tr><tr><td>Poland</td><td>University of Pennsylvania</td><td>UPENN</td><td>USA</td><td>Cancer clinic-based study</td><td>University of Pennsylvania Institutional Review Board</td><td>WIC</td><td>101</td><td>145</td><td>145</td></tr><tr><td>Poland</td><td>University of Texas MD Anderson Cancer Center</td><td>UTMDACC</td><td>USA</td><td>Cancer clinic-based study</td><td>University of Texas MD Anderson Cancer Center Office of Protocol Research Institutional Review Board</td><td>WIC</td><td>61</td><td>38</td><td>38</td></tr><tr><td>Sweden</td><td>International Hereditary Cancer Centre</td><td>IHCC</td><td>Poland</td><td>Cancer clinic-based study</td><td>Komijsja Bioetyczna Pomorskiej Akademii Medycznej (Pomeranian Medical University Bioethics Committee)</td><td>WIC</td><td>244</td><td>10</td><td>10</td></tr><tr><td>Sweden</td><td>Swedish Breast Cancer Study</td><td>SWE-BRCA</td><td>Sweden</td><td>Cancer clinic-based study</td><td>Regionala Etikprövningsnämnden Stockholm</td><td>WIC</td><td>83</td><td>48</td><td>48</td></tr></tbody></table>
<table><caption>**Table 7 continued**</caption><thead><tr><th rowspan="2">Consortium<br>stratum</th><th rowspan="2">Study</th><th rowspan="2">Acronym</th><th rowspan="2">Country</th><th rowspan="2">Study design</th><th rowspan="2">Name of the institutional review<br>board (IRB)</th><th rowspan="2">Informed<br>consent<br>policy</th><th colspan="2">Study<br>subjects</th><th colspan="2">Pathology data to<br>support<br>imputation</th></tr><tr><th>BRCA1</th><th>BRCA2</th><th>BRCA1/2</th><th>Non-<br>carriers</th></tr></thead><tbody><tr><td>Iberia</td><td>Institut Català d'Oncologia</td><td>ICO</td><td>Spain</td><td>Cancer clinic-<br>based study</td><td>Catalan Institute of Oncology<br>Institutional Review Board</td><td>WIC</td><td>78</td><td>86</td><td>86</td><td></td></tr><tr><td></td><td>Portuguese Oncology Institute-<br>Porto Breast Cancer Study</td><td>IPOBCS</td><td>Portugal</td><td>Cancer clinic-<br>based study</td><td>Comissão de Ética para a Saúde<br>(CES) - IPO-Porto EPE</td><td>WIC</td><td>81</td><td>43</td><td></td><td></td></tr><tr><td>UK</td><td>Victorian Familial Cancer Trials<br>Group</td><td>EMBRACE</td><td>UK</td><td>Cancer clinic-<br>based study</td><td>Anglia &amp; Oxford MREC</td><td>WIC</td><td>604</td><td>620</td><td>232</td><td></td></tr><tr><td>BCAC</td><td>Kathleen Cuningham Foundation<br>Consortium for research into<br>Familial Breast Cancer/Australian<br>Ovarian Cancer Study</td><td>KCONFAB/<br>AOCS</td><td>Australia and<br>New Zealand</td><td>Clinic-based<br>recruitment of<br>familial breast<br>cancer patients</td><td>kConFab: Peter MacCallum Cancer<br>Centre Ethics Committee</td><td>WIC</td><td>9</td><td>2</td><td>1002</td><td></td></tr><tr><td></td><td>Melbourne Collaborative Cohort<br>Study</td><td>MCCS</td><td>Australia</td><td>Prospective<br>cohort study:<br>nested case-<br>control study</td><td>The Cancer Council Victoria<br>Human Research Ethics<br>Committee</td><td>WIC</td><td>3</td><td>7</td><td>1</td><td>652</td></tr><tr><td>Central<br>Europe</td><td>Breast Cancer Study of the<br>University of Heidelberg</td><td>BSUCH</td><td>Germany</td><td>Hospital-based<br>cases</td><td>Ethikkommission Medizinische<br>Fakultat Heidelberg, University of<br>Heidelberg</td><td>WIC</td><td>5</td><td>5</td><td>176</td><td></td></tr><tr><td></td><td>CECILE Breast Cancer Study</td><td>CECILE</td><td>France</td><td>Population-based<br>case-control study</td><td>Comité Consultatif de Protection<br>des Personnes dans la Recherche<br>Biomédicale de Bicêtre (Le<br>Kremlin-Bicêtre FR-94270)</td><td>WIC</td><td>7</td><td>12</td><td>3</td><td>833</td></tr><tr><td></td><td>German Consortium for<br>Hereditary Breast &amp; Ovarian<br>Cancer</td><td>GC-HBOC</td><td>Germany</td><td>Clinic-based case<br>study and<br>prospective<br>cohort study</td><td>Ethik-Kommission der<br>Medizinischen Fakultat der<br>Universität zu Köln</td><td>WIC</td><td>4</td><td>16</td><td>2099</td><td></td></tr><tr><td></td><td>Gene Environment Interaction<br>and Breast Cancer in Germany</td><td>GENICA</td><td>Germany</td><td>Population-based<br>case-control study</td><td>Ethikkommission Rheinische<br>Friedrich-Wilhelms-Universität Bonn</td><td>WIC</td><td>9</td><td>15</td><td>691</td><td></td></tr><tr><td></td><td>Genetic Epidemiology Study of<br>Breast Cancer by Age 50</td><td>GESBC</td><td>Germany</td><td>Population-based<br>study of women<br>&lt;50 years</td><td>Medizinische Fakultat Heidelberg</td><td>WIC</td><td>23</td><td>17</td><td>510</td><td></td></tr><tr><td></td><td>Mammary Carcinoma Risk Factor<br>Investigation</td><td>MARIE</td><td>Germany</td><td>Population-based<br>case-control study</td><td>Ethikkommission<br>Medizinische Fakultat Heidelberg<br>Ethikkommission; Ethik-<br>Kommission der Arztekammer<br>Hamburg</td><td>WIC</td><td>9</td><td>27</td><td>1</td><td>1999</td></tr><tr><td></td><td>Städtisches Klinikum Karlsruhe<br>Deutsches<br>Krebsforschungszentrum Study</td><td>SKKDKFZS</td><td>Germany</td><td>Hospital-based<br>breast cancer<br>cohort</td><td>Medizinische Fakultat Heidelberg<br>Ethikkommission</td><td>WIC</td><td>12</td><td>16</td><td>1</td><td>700</td></tr><tr><td>Denmark</td><td>Copenhagen General Population<br>Study</td><td>CGPS</td><td>Denmark</td><td>Population-based<br>case-control study</td><td>Medizinische Fakultat Heidelberg<br>Ethikkommission</td><td>WIC</td><td>34</td><td>24</td><td>4</td><td>1893</td></tr></tbody></table>
<table><caption>**Table 7 continued**</caption><thead><tr><th>Consortium</th><th>Country</th><th>Study</th><th>Acronym</th><th>Country</th><th>Study design</th><th>Name of the institutional review board (IRB)</th><th>Informed consent policy</th><th>Study subjects</th><th>Pathology data to support imputation</th></tr></thead><tbody><tr><td rowspan="2">Finland</td><td rowspan="2">Country stratum</td><td>Helsinki Breast Cancer Study</td><td>HEBCS</td><td>Finland</td><td>Hospital-based case-control study, plus additional familial cases</td><td>Helsingin ja uudenmaan Sairaanhoitopiiri (Helsinki University Hospital Ethics Committee)</td><td>WIC</td><td>8</td><td>13</td><td>1</td><td>1499</td></tr><tr><td>Kuopio Breast Cancer Project</td><td>KBCP</td><td>Finland</td><td>Population-based prospective clinical cohort</td><td>Pohjois-Savon Sairaanhoitopiirin Kuntayhtyma Tutkimuseettinen Toimikunta</td><td>WIC</td><td>3</td><td>3</td><td>5</td><td>404</td></tr><tr><td rowspan="2">Netherlands</td><td rowspan="2">Amsterdam Breast Cancer Study</td><td rowspan="2">ABCS</td><td rowspan="2">Netherlands</td><td>Hospital-based consecutive cases</td><td>Leiden University Medical Center (LUMC) Commissie Medische Ethiek; Protocol Toetsingscommissie van Het Nederlands Kanker Instituut-Antoni van Leeuwenhoek Ziekenhuis</td><td>WIC</td><td>27</td><td>26</td><td>26</td><td>962</td></tr><tr><td>Amsterdam Breast Cancer Study - ABCS-F Familial</td><td>Netherlands</td><td>Clinical Genetic Center-based cases</td><td>Leiden University Medical Center (LUMC) Commissie Medische Ethiek; Protocol Toetsingscommissie van Het Nederlands Kanker Instituut-Antoni van Leeuwenhoek Ziekenhuis</td><td>WIC</td><td>5</td><td>2</td><td>2</td><td>894</td></tr><tr><td rowspan="2">North America</td><td rowspan="2">Ontario Familial Breast Cancer Registry</td><td rowspan="2">OFBCR</td><td rowspan="2">RBCS</td><td rowspan="2">Netherlands</td><td>Hospital-based case-control study, Rotterdam area</td><td>Medische Ethische Toetsings Commissie Erasmus Medisch Centrum</td><td>WIC</td><td>1</td><td>3</td><td>354</td><td>119</td></tr><tr><td>The Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial</td><td>USA</td><td>Population-based familial case-control study</td><td>Mount Sinai Hospital Research Ethics Board</td><td>WIC</td><td>2</td><td>15</td><td>1</td><td>1188</td></tr><tr><td rowspan="2">Poland</td><td rowspan="2">NCI Polish Breast Cancer Study</td><td rowspan="2">PBCS</td><td rowspan="2">Poland</td><td rowspan="2">USA</td><td>Prospective cohort study: nested case-control</td><td>National Cancer Institute Special Studies Institutional Review Board (NCI-SSIRB)</td><td>WIC</td><td>2</td><td>15</td><td>1</td><td>486</td></tr><tr><td>Utah Breast Cancer Study</td><td>Pedigrees including multiple cases; Hospital-based cases; Population-based cases</td><td>University of Utah Institutional Review Board</td><td>WIC</td><td>5</td><td>7</td><td>5</td><td>1460</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>National Cancer Institute Special Studies Institutional Review Board (NCI-SSIRB)</td><td>WIC</td><td>52</td><td>29</td><td>6</td><td>1460</td></tr></tbody></table>
<table><caption>Table 7 continued</caption><thead><tr><th rowspan="2">Consortium<br>Country<br>stratum</th><th rowspan="2">Study</th><th rowspan="2">Acronym</th><th rowspan="2">Country</th><th rowspan="2">Study design</th><th rowspan="2">Name of the institutional review<br>board (IRB)</th><th rowspan="2">Informed<br>consent<br>policy</th><th colspan="2">Study subjects</th><th rowspan="2">Pathology data to<br>support<br>imputation</th></tr><tr><th>BRCA1</th><th>BRCA2</th></tr></thead><tbody><tr><td>Scandinavia</td><td>Karolinska Breast Cancer Study</td><td>KARBAC</td><td>Sweden</td><td>Population and<br>hospital-based<br>cases</td><td>Regionala Etikprovningsnamnden<br>i Stockholm (Regional Ethical<br>Review Board in Stockholm)</td><td>WIC</td><td>2</td><td>4</td><td>285</td></tr><tr><td></td><td>Karolinska Mammography Project<br>for Risk Prediction of Breast<br>Cancer - Cohort Study</td><td>KARMA</td><td>Sweden</td><td>Cohort study</td><td>Regionala Etikprovningsnamnden<br>i Stockholm (Regional Ethical<br>Review Board in Stockholm)</td><td>WIC</td><td>13</td><td>19</td><td>2</td><td>2425</td></tr><tr><td></td><td>Norwegian Breast Cancer Study</td><td>NBCS</td><td>Norway</td><td>Hospital-based<br>case-control study</td><td>Regionale Komitere for Medisinsk<br>og Helsefaglig Forskningsetikk</td><td>WIC</td><td>1</td><td>3</td><td></td><td>424</td></tr><tr><td></td><td>Singapore and Sweden Breast<br>Cancer Study</td><td>SASBAC</td><td>Sweden</td><td>Population-based<br>case-control study</td><td>Regionala Etikprovningsnamnden<br>i Stockholm (Regional Ethical<br>Review Board in Stockholm)</td><td>WIC</td><td>7</td><td>3</td><td></td><td>903</td></tr><tr><td>Southern<br>Europe</td><td>Breast Oncology Galicia Network</td><td>BREOGAN</td><td>Spain</td><td>Population-based<br>case-control</td><td>Comité Autonómico de Ética de la<br>Investigación de Galicia</td><td>WIC</td><td>8</td><td>10</td><td>4</td><td>489</td></tr><tr><td></td><td>Crete Cancer Genetics Program</td><td>CCGP</td><td>Greece</td><td>Hospital-based<br>case-control study</td><td>Epistimoniko Symvoulio (Scientific<br>Council of the University General<br>hospital of Heraklion)</td><td>WIC</td><td>8</td><td>2</td><td></td><td>387</td></tr><tr><td></td><td>Milan Breast Cancer Study Group</td><td>MBCSG</td><td>Italy</td><td>Clinic-based<br>familial/early<br>onset breast<br>cancer patients</td><td>Comitato Etico Indipendente della<br>Fondazione IRCCS "Istituto<br>Nazionale dei Tumori"</td><td>WIC</td><td>2</td><td></td><td></td><td>425</td></tr><tr><td>UK</td><td>Predicting the Risk Of Cancer At<br>Screening Study</td><td>PROCAS</td><td>UK</td><td>Population based<br>study</td><td>NRES Committee North West -<br>Greater Manchester Central</td><td>WIC</td><td>4</td><td>1</td><td></td><td>322</td></tr><tr><td></td><td>Study of Epidemiology and Risk<br>factors in Cancer Heredity</td><td>SEARCH</td><td>UK</td><td>Population-based<br>case-control study</td><td>Multi Centre Research Ethics<br>Committee (MREC)</td><td>WIC</td><td>72</td><td>151</td><td>47</td><td>9331</td></tr></tbody><tfoot><tr><td colspan="11">WIC: All participants gave a written informed consent; IRB: Informed consent was waived by the ethical committee.<br><sup>a</sup>The majority of participants in VFCTG have prospectively signed to give specific consent but a group of mutation carriers were included in the group retrospectively (in 2012) with a waiver of consent approved by the HREC at Peter MacCallum Cancer Centre: HREC/12/PMCC/29_project 12/11.<br><sup>b</sup>Informed consent is not necessary in Denmark for this kind of studies. The study has been approved by the regulatory and juridical authorities.</td></tr></tfoot></table>
the corresponding coefficients were excluded from the score. The expected breast cancer mortality was calculated based on the PREDICT scores and baseline risks for breast cancer and other cause mortality³.

## Statistical analysis

The analyses were performed in parallel in the 50 imputed datasets, and the final results, e.g., regression model coefficients, model-based predictions, concordance, and expected or observed mortality were pooled according to Rubin's rules or as recommended for event history analysis in Marshall et al.³⁹,⁴⁰. The PREDICT risk categories, defined by PREDICT score percentiles were pooled by voting—the pooled category for a patient was the category, which the patient received most frequently in the 50 imputed datasets. Survival analyses were performed with R library *survival*⁴¹,⁴². The 15-year follow-up started at the first breast cancer diagnosis, and left-truncation was applied to account for delayed entry. Patients were censored at the end of follow-up, if lost from follow-up, or at non-breast-cancer-related death.

The PREDICT ER-negative and ER-positive scores were tested separately in the corresponding subgroups of *BRCA1* and *BRCA2* carrier patients from CIMBA and BCAC. First, the PREDICT score was tested as a linear covariate in a Cox regression model, using the model concordance as a measure of the model fit. The Gönen & Heller unbiased concordance was estimated using R library *CPE*⁴³ and pooled to median. Second, the PREDICT score was used as an offset factor in a Cox regression model, where all the score components were included as independent covariates. Here, the diagnosis age was modeled with a spline with three degrees of freedom, grade (1,2,3) and the number of affected lymph nodes as numerical variables, tumor size (mm) as a log-scale linear variable, and PgR and Her2 statuses as dichotomous variables (positive vs. negative status). If any of the covariates was associated with significant residual hazard, a reduced score, excluding these covariates, was calculated. The reduced score was then used as an offset, and the hazard associated with these covariates was estimated with a multivariable Cox regression. All Cox regression models were stratified by country, to account for differences in the baseline risk due to differences in treatment practices. The offset models were further adjusted for diagnosis year (linear, continuous), to account for any residual improvement in therapy over the years.

The PREDICT calibration was studied separately in CIMBA and BCAC studies as a merged cohorts. The calibration was assessed by splitting the patient data into four (primarily) or three (if low number of cases) risk categories based on PREDICT percentiles (0–20%ile, 20–50%ile, 50–80%ile, 80–100%ile or 0–30%ile, 30–70% ile, 70–100%ile) and plotting the expected breast cancer mortality against the observed breast cancer mortality in the quantiles. Optimally, the calibration should be examined by comparing the expected and observed number of events. However, in right-censored, left-truncated data this was not possible, and the observed mortality and the respective cumulative hazard of state-transition was retrieved from Kaplan–Meier survival estimator, within each pooled dataset, after which the point-estimates and standard errors were pooled according to Rubin's rules. The expected mortality was calculated separately in each imputed dataset based on the average PREDICT score and the baseline cumulative hazard of breast cancer death. The cumulative hazard estimates were pooled to average and transformed to expected mortality. The difference between expected and observed mortality was considered significant, if the expected mortality point-estimate was outside the pooled 95% confidence interval of the observed mortality. The breast cancer-specific survival of patients in the PREDICT risk categories was visualized with Kaplan–Meier graphs, separately in those CIMBA and BCAC studies, that provided cause of death information for at least 80% of deceased patients.

## Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

## DATA AVAILABILITY

The consortia study participant phenotype data used in the current study are not publicly available due to protection of participant privacy and confidentiality, and data ownership belonging to the contributing institutions. But data can be made available in an anonymized form from the CIMBA and BCAC consortia on a reasonable request and after approval from the contributing studies. Requests for data can be made to the CIMBA and BCAC Data Access Coordination Committees (DACC; https://cimba.ccge.medschl.cam.ac.uk/projects/data-access-requests/; https://bcac.ccge.medschl.cam.ac.uk/bcacdata/). The contact person for data access requests is Manjeet Bolla (mkh39@medschl.cam.ac.uk) Data Manager, Department of Public Health and Primary Care, University of Cambridge. The imputed datasets are available from the corresponding author upon the DACC approval.

## CODE AVAILABILITY

All statistical analyses were performed within the R environment for statistical computing version 4.0.0, including libraries *mice*, *survival*, and *CPE*. Custom code, used for pooling the imputed results, calculating the PREDICT scores and baseline risk is available in GitHub (https://github.com/TaruMuranen/PREDICT_for_BRCA1-2).

Received: 13 November 2022; Accepted: 28 April 2023;
Published online: 12 May 2023

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carried out. The CIMBA data management and data analysis were supported by Cancer Research - UK grants C12292/A20861, C12292/A11174. BCAC is funded by the European Union Horizon 2020 Research and Innovation Programme (grant number 634935), and the PERSPECTIVE project, funded by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research, the Ministère de l'Economie et de Innovation du Québec through Genome Québec, the Quebec Breast Cancer Foundation. The EU Horizon 2020 Research and Innovation Programme funding source had no role in study design, data collection, data analysis, data interpretation, or writing of the report. Additional funding for BCAC is provided via the Confluence project which is funded with intramural funds from the National Cancer Institute Intramural Research Program, National Institutes of Health. The BRIDGES panel sequencing was supported by the European Union Horizon 2020 research and innovation program BRIDGES (grant number, 634935) and the Wellcome Trust (v203477/Z/16/Z). HEBCS (T.A.M. and H.Ne.) was funded by the Helsinki University Hospital Research Fund, the Sigrid Jusélius Foundation, and the Cancer Foundation Finland. Detailed acknowledgements of individual studies with funding information are provided in Supplementary Note 1.

## AUTHOR CONTRIBUTIONS

Study conception, interpretation of results, and writing group: T.A.M., A.Morr., S.Kh., A.C.A., G.C-T., P.D.P.P., M.K.S., C.Bl., H.Ne. Data analyses: T.A.M., A.Morr., S.Kh. Drafting of the manuscript: T.A.M. Data management and quality control: T.A.M., D.R.B., M.K.B., J.Den., R.Ke., G.Le., M.T.P., Q.W. Provision of DNA samples and/or phenotypic data: T.U.A., K.Ai., I.L.A., B.K.A., S.Be., K.Bia., S.E.B., N.J.C., J.C-C., K.Cz., P.D., S.M.D., A.M.D., C.En., D.G.E., M.G-D., M.G-C., A-M.G., G.Gl., P.Gu., E.Hah., U.H., H.Han., M.J.H., R.H., L.I., A.Jak., P.A.J., V.N.K., F.La., G.J.L., A.Man., S.Mar., S.L.N., W.G.N., P.Pe., K-A.P., M.A.P., J.Ran., K.R., E.S., R.K.S., A.Sc., C.F.S., M.Su., YY.T., M.R.T., M.Th., M.Ti., V.T., B.Wa., E.Zh., D.F.E., G.C-T., P.D.P.P., M.K.S., C.Bl., H.Ne., HEBON Consortium coordination and administrative support: M.K.B., G.Le., Q.W., D.F.E., A.C.A., G.C-T., P.D.P.P., M.K.S. All authors revised the manuscript and approved the final version.

## COMPETING INTERESTS

H.Ne. has had an AstraZeneca consultancy. The other authors declare no competing interests.

## ADDITIONAL INFORMATION

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41523-023-00546-x.

**Correspondence** and requests for materials should be addressed to Taru A. Muranen.

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© The Author(s) 2023

## ACKNOWLEDGEMENTS

We thank all the individuals who took part in these studies and all the researchers, clinicians, technicians and administrative staff who have enabled this work to be

<footnote n="1">¹Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland. ²Research Program in Systems Oncology, Department of Biochemistry and Developmental Biology, University of Helsinki, Helsinki, Finland. ³Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands. ⁴Department of Genetics, HUSLAB, HUS Diagnostic Center, Helsinki University Hospital, University of Helsinki, Helsinki, Finland. ⁵Individualized Drug Therapy Research Program, University of Helsinki, Helsinki, Finland. ⁶Department of Clinical Pharmacology, University of Helsinki, Helsinki, Finland. ⁷Centre for Cancer Genetic</footnote>
Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. ⁸Population Health Division, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia. ⁹Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA. ¹⁰Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland. ¹¹Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, Ontario, Canada. ¹²Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada. ¹³Department of Breast Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA. ¹⁴Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany. ¹⁵Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland. ¹⁶Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark. ¹⁷Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark. ¹⁸Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. ¹⁹Department of Internal Medicine and Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA. ²⁰Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany. ²¹Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. ²²Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands. ²³Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands. ²⁴Basser Center for BRCA, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, USA. ²⁵Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK. ²⁶Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany. ²⁷LIFE - Leipzig Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany. ²⁸Prevent Breast Cancer Research Unit, The Nightingale Centre, Manchester University Hospital Foundation NHS Trust, Manchester, UK. ²⁹Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK. ³⁰Clinical Genetics Service, Manchester Centre for Genomic Medicine, Manchester University Hospitals Foundation Trust, Manchester, UK. ³¹Manchester Breast Centre, Oglesby Cancer Research Centre, The Christie, University of Manchester, Manchester, UK. ³²Health Research Institute of Santiago de Compostela Foundation (FIDIS), SERGAS, Cancer Genetics and Epidemiology Group Santiago de Compostela, Santiago de Compostela, Spain. ³³Department of Clinical Genetics, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark. ³⁴Team "Exposome and Heredity", CESP, Gustave Roussy, INSERM, University Paris-Saclay, UVSQ, Villejuif, France. ³⁵Center for Familial Breast and Ovarian Cancer, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany. ³⁶Center for Integrated Oncology (CIO), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany. ³⁷Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany. ³⁸SouthWest Thames Centre for Genomics, St George's University Hospital's NHS Foundation Trust, London, UK. ³⁹Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands. ⁴⁰Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany. ⁴¹University of Tübingen, Tübingen, Germany. ⁴²Clinical Genetics, Guy's and St Thomas' NHS Foundation Trust, London, UK. ⁴³Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland. ⁴⁴Parkville Familial Cancer Centre, The Royal Melbourne Hospital and Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia. ⁴⁵Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway. ⁴⁶Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway. ⁴⁷Cancer Biology and Stem Cells Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia. ⁴⁸Department of Medical Oncology, Peter MacCallum Cancer Center, Melbourne, Victoria, Australia. ⁴⁹Department of Medicine, Royal Melbourne Hospital, University of Melbourne, Parkville, Victoria, Australia. ⁵⁰Translational Cancer Research Area, University of Eastern Finland, Kuopio, Finland. ⁵¹Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland. ⁵²Biobank of Eastern Finland, Kuopio University Hospital, Kuopio, Finland. ⁵³Department of Oncology, Stockholm South General Hospital (Södersjukhuset), Stockholm, Sweden. ⁵⁴Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden. ⁵⁵Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, CA, USA. ⁵⁶North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK. ⁵⁷Genome Diagnostics Program, IFOM ETS - The AIRC Institute of Molecular Oncology, Milan, Italy. ⁵⁸Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia. ⁵⁹Department of Medicine, St Vincent's Hospital, The University of Melbourne, Fitzroy, Victoria, Australia. ⁶⁰Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia. ⁶¹Translational Research Laboratory, IDIBELL (Bellvitge Biomedical Research Institute), Catalan Institute of Oncology, CIBERONC, Barcelona, Spain. ⁶²Clinical Genetics, Karolinska Institutet, Stockholm, Sweden. ⁶³Department of Clinical Genetics, University Hospital of Southern Denmark, Vejle Hospital, Vejle, Denmark. ⁶⁴Department of Oncology, University Hospital of Larissa, Larissa, Greece. ⁶⁵National Center for Tumor Diseases, University Hospital and German Cancer Research Center, Heidelberg, Germany. ⁶⁶Molecular Biology of Breast Cancer, University Womens Clinic Heidelberg, University of Heidelberg, Heidelberg, Germany. ⁶⁷Dept of OB/GYN and Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria. ⁶⁸Department of Genetics, Portuguese Oncology Institute, Porto, Portugal. ⁶⁹School of Medicine and Biomedical Sciences (ICBAS), University of Porto, Porto, Portugal. ⁷⁰Department of Clinical Genetics, Odense University Hospital, Odense C, Denmark. ⁷¹Department of Medical Genetics, National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK. ⁷²Cancer Division, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia. ⁷³Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands. ⁷⁴Department of Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands. ⁷⁵Department of Oncology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland. *A list of authors and their affiliations appears at the end of the paper.

## HEBON INVESTIGATORS

Peter Devilee¹⁰,²²,²³, Maartje J. Hooning³⁹ and Marjanka K. Schmidt¹⁰,³⁷,³⁹,⁴⁷

A full list of members and their affiliations appears in the Supplementary Information.