Abstract
Background
Whether outcomes of germline BRCA1/2 (gBRCA1/2)-associated breast cancer differ compared with sporadic tumors is controversial. We explored the impact of gBRCA1/2 pathogenic variant (PV) status beyond established prognostic features.
Methods
We conducted 2 retrospective, matched cohort studies comparing gBRCA1/2 PV carriers and noncarriers with HER2-negative, stage I-III breast cancer (Clinical Outcomes Quality Database [COQD] cohort: 185 carriers, 555 noncarriers; Young Women’s Breast Cancer Study [YWS] cohort: 113 carriers, 226 noncarriers). Matching factors were age, stage, hormone receptor status, and year of diagnosis. Clinicopathological features, treatments, and survival outcomes were compared between carriers and noncarriers.
Results
Most patients in COQD had stage I-II disease (87.2%), and more carriers than noncarriers had genetic testing before diagnosis (33% vs 5.6%, P < .001). In YWS, 22.7% of patients had stage III tumors, and few were tested before diagnosis (14.8% of carriers vs 1.7% of noncarriers; P < .001). Carriers in COQD received chemotherapy more often than noncarriers (81.1 vs 67.0%, P < .001), including platinum (P = .010); the proportion was similar for carriers and noncarriers in YWS. After adjusting for chemotherapy, relapse-free survival was longer in carriers than noncarriers in COQD (adjusted hazard ratio = 0.48 [95% CI = 0.26 to 0.87], P = .016), and a favorable trend was observed for other survival outcomes in both cohorts. Triple-negative tumors appeared to drive the differences.
Conclusions
We observed a trend toward improved outcomes in gBRCA1/2 PV carriers compared with noncarriers. These findings suggest that carriers should not receive more aggressive treatment solely based on their germline mutation status. Prospective clinical trials in this population are warranted.
Introduction
Approximately 10% of breast tumors occur in individuals harboring germline pathogenic variants in cancer susceptibility genes, with BRCA1/2 pathogenic or likely pathogenic variants (PV) being the most frequent.1 Tumors in germline BRCA1/2 (gBRCA1/2) PV carriers are associated with distinct clinicopathological features. In gBRCA1 carriers, tumors are often triple negative, have a high tumor grade, and are diagnosed at a younger age.2-4 Germline BRCA2 PV-associated breast cancers are more frequently hormone receptor-positive/HER2-negative (HR+/HER2−) and diagnosed at an older age than gBRCA1-associated cancer, although earlier than in noncarriers.2
There are conflicting data regarding treatment patterns, prognosis, and response to different therapies for gBRCA1/2-related breast cancers compared with sporadic tumors. Multiple studies have been published, but most are retrospective, included few patients, used outdated treatment regimens, and did not account for variables known to influence breast cancer outcomes. Some studies have reported better outcomes while others have reported equal or worse prognosis for gBRCA PV carriers compared with noncarriers.5-8 Most randomized trials have either not reported gBRCA1/2 status or excluded patients with gBRCA PV.
To gain deeper insight into how gBRCA1/2 status influences treatment patterns and clinical outcomes, we conducted 2 matched cohort studies comparing carriers of gBRCA1/2 PVs and noncarriers across 2 prospective cohorts with distinct clinicopathological characteristics and follow-up durations. In the Clinical Outcomes Quality Database (COQD) cohort, we included patients of all ages treated between 2016 and 2021, focusing on outcomes associated with contemporary treatment regimens. In the Young Women’s Breast Cancer Study (YWS), we analyzed patients with young-onset breast cancer treated between 2006 and 2016 to evaluate long-term outcomes.
Methods
Patient population
COQD cohort
The prospective, institutional COQD was queried to identify all consecutive patients who underwent surgery at Dana-Farber Brigham Cancer Center between January 2016 and August 2021. Information on genetic status was integrated with data from a prospective dataset curated by the Division of Cancer Genetics and Prevention at Dana-Farber Cancer Institute.
Clinicopathological features, treatment patterns, and outcomes were obtained from the medical records. We included patients with a first diagnosis of stage I-III, HER2-negative early breast cancer with known gBRCA1/2 status. Patients with known germline PV in other genes associated with breast cancer (ATM, BARD1, CDH1, CHEK2, NF1, PALB2, PTEN, RAD51C, RAD51D, STK11, TP53) were excluded from both carriers and noncarriers, although panel testing (eg, germline testing including genes other than BRCA1 and BRCA2) was not required.
YWS cohort
The YWS is a large multisite prospective cohort of women diagnosed with breast cancer at age ≤40 years between 2006 and 2016. Clinicopathological, genetic, treatment, and outcome data collected from patient surveys and medical records as part of the YWS study as previously described were retrieved.9 Patients with stage I-III, HER2-negative breast cancer with known gBRCA1/2 status were included. As in the COQD cohort, patients with known germline PV in the abovementioned genes associated with breast cancer were excluded, and panel testing was not required.
Statistical analysis
The YWS and COQD cohorts were analyzed separately given the differences between inclusion criteria and data collection. The Nearest Neighbor matching approach was used to match patients with gBRCA1/2 PV with patients with gBRCA1/2 wild-type in both cohorts separately. The following matching factors were considered: age at diagnosis (in decades), stage at diagnosis, year of diagnosis, and receptor subtype (HR+/HER2− vs triple-negative breast cancer [TNBC]). The matching criteria were expanded by using the nearest year of diagnosis or age group, if needed. We used a carrier to noncarrier ratio of 1:3 for the COQD cohort and 1:2 for the YWS cohort, due to the lack of enough noncarriers satisfying the subtype matching criteria in the latter. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cohort studies were followed.10
Clinicopathological features and treatment patterns were compared between carriers and noncarriers using Fisher’s exact test for categorical and Wilcoxon rank sum test for continuous variables.
Invasive disease-free survival (iDFS), relapse-free survival (RFS), and distant relapse-free survival (DRFS) were defined according to STEEP 2.0 criteria.11 iDFS was defined as time from surgery to local recurrence, distant recurrence, any secondary cancer, or death from any cause. RFS was defined as time from surgery to local recurrence, regional recurrence, distant recurrence, or death from any cause. DRFS was defined as time from surgery to distant recurrence or death from any cause. Overall survival (OS) was defined as time from surgery to death from any cause. Those without an event were censored at last date of follow-up for all endpoints of interest. The Cox proportional hazards model adjusted for receipt of chemotherapy, continuous age, and grade was used to compare iDFS and RFS between carriers and noncarriers. In the YWS cohort, outcomes between gBRCA PV carriers with HR+/HER2− and with TNBC were also compared. The associated Wald test was used to estimate the P-value for each independent variable in the adjusted model, and the log-rank test P-value was estimated for the whole model. Given the relatively short follow-up of patients included in the COQD cohort, we estimated OS in the YWS cohort only. Survival curves were plotted using the Kaplan-Meier method. As a subgroup analysis, we analyzed gBRCA1 and gBRCA2 PV carriers and noncarriers separately. All P-values <.05 were considered statistically significant.
This study was conducted in accordance with the Declaration of Helsinki. The Dana-Farber/Harvard Cancer Center (DF/HCC) Institutional Review Board (IRB) classified this retrospective, matched cohort study as exempt from IRB approval and included a waiver of informed consent in accordance with the US Common Rule (DF/HCC IRB #22 to 181).
Results
Baseline characteristics
Overall, 185 gBRCA PV (105 gBRCA1, 80 gBRCA2) carriers and 555 matched noncarriers were identified from COQD, and 113 gBRCA PV (74 gBRCA1, 39 gBRCA2) carriers and 226 matched noncarriers from the YWS (Figures S1-S2). Clinicopathological characteristics of carriers and noncarriers are reported in Table 1 separately for the 2 cohorts. Overall, matching variables—age at diagnosis, stage, tumor subtype, year of diagnosis—were well-balanced in both cohorts except for age in COQD, due to an insufficient number of younger noncarriers matching the other criteria. Median age at diagnosis was 43 years (range = 25 to 81) in carriers and 46 years (range = 21 to 83) in noncarriers in COQD (P = .018), and 36 years in both carriers and noncarriers (P = .32) in the YWS. Most patients in COQD had stage I (42%) or II (45.1%) disease at diagnosis. In YWS, 31% of patients had stage I, 46.3% had stage II, and 22.7% had stage III, accounting for an overall higher stage at diagnosis when compared with the COQD cohort. The proportion of patients diagnosed by screening imaging in COQD was similar between carriers and noncarriers (37.3% vs 39.3%, P = .66), but more carriers than noncarriers had had genetic testing performed before diagnosis (33% vs 5.6%, P < .001). Among patients tested after diagnosis, median time from diagnosis to germline testing was 13 days for carriers (range = 2 to 2041 days) and 17 days for noncarriers (range = 0 to 2232 days) (P < .001). As expected by the young age and the stage II-III disease at presentation, most patients in YWS presented at physical exam or with symptoms at time of diagnosis (93.4%), regardless of gBRCA1/2 status (P = .80), and only 6% of patients had undergone genetic testing before diagnosis (14.8% of carriers and 1.7% of noncarriers; P < .001). Similar to what was observed in COQD, median time from diagnosis to germline testing for patients tested after diagnosis was 14 days for carriers (range = 0 to 780 days) and 18 days for noncarriers (range = 0 to 2143 days) (P = .45).
Table 1.
Baseline characteristics in the COQD (left) and YWS (right) cohorts.
| COQD cohort |
YWS cohort |
||||||||
|---|---|---|---|---|---|---|---|---|---|
| Total (n = 740) | gBRCA1/2 PV carriers (n = 185) | gBRCA1/2 wild type (n = 555) | P | Total (n = 339) | gBRCA1/2 PV carriers (n = 113) | gBRCA1/2 wild type (n = 226) | P | ||
| Agea | .018 | Agea | .32 | ||||||
| Median | 45.5 | 43.0 | 46.0 | Median | 36.0 | 36.0 | 36.0 | ||
| Range | 21.0 to 83.0 | 25.0 to 81.0 | 21.0 to 83.0 | Range | 21.0 to 40.0 | 23.0 to 40.0 | 21.0 to 40.0 | ||
| Sex | .037 | Sex | – | ||||||
| Female | 734 (99.2%) | 181 (97.8%) | 553 (99.6%) | Female | 339 (100.0%) | 113 (100.0%) | 226 (100.0%) | ||
| Male | 6 (0.8%) | 4 (2.2%) | 2 (0.4%) | Male | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | ||
| Race | .55 | Race | .19 | ||||||
| White | 636 (89.8%) | 156 (88.1%) | 480 (90.4%) | White | 295 (87.0%) | 104 (92.0%) | 191 (84.5%) | ||
| African American | 36 (5.1%) | 11 (6.2%) | 25 (4.7%) | Black, Haitian, or African American | 9 (2.7%) | 1 (0.9%) | 8 (3.5%) | ||
| Asian or Pacific Islander | 34 (4.8%) | 9 (5.1%) | 25 (4.7%) | Asian | 21 (6.2%) | 3 (2.7%) | 18 (8.0%) | ||
| American Indian, Aleutian, Eskimo | 2 (0.3%) | 1 (0.6%) | 1 (0.2%) | Multiracial | 6 (1.8%) | 2 (1.8%) | 4 (1.8%) | ||
| Missing | 32 | 8 | 24 | Other/Unknown | 8 (2.4%) | 3 (2.7%) | 5 (2.2%) | ||
| Menopausal status at diagnosis | .86 | Menopausal status at baseline | >.99 | ||||||
| Premenopausal | 476 (65.3%) | 119 (66.1%) | 357 (65.0%) | Premenopausal | 323 (99.4%) | 107 (99.1%) | 216 (99.5%) | ||
| Postmenopausal | 253 (34.7%) | 61 (33.9%) | 192 (35.0%) | Postmenopausal | 2 (0.6%) | 1 (0.9%) | 1 (0.5%) | ||
| Unknown | 11 | 5 | 6 | Unknown | 14 | 5 | 9 | ||
| Tumor histology | .53 | Tumor histologyb | |||||||
| Invasive ductald | 598 (80.8%) | 149 (80.5%) | 449 (80.9%) | Ductal | 236 (76.6%) | 83 (81.4%) | 153 (74.3%) | .20 | |
| Invasive lobular | 41 (5.5%) | 13 (7.0%) | 28 (5.0%) | Lobular | 11 (3.6%) | 3 (2.9%) | 8 (3.9%) | >.99 | |
| Other | 101 (13.6%) | 23 (12.4%) | 78 (14.1%) | Ductal and lobular | 30 (9.7%) | 9 (8.8%) | 21 (10.2%) | .84 | |
| Other | 38 (12.3%) | 9 (8.8%) | 29 (14.1%) | .20 | |||||
| Not reported | 31 | 11 | 20 | ||||||
| Tumor grade | <.001 | Tumor grade | .014 | ||||||
| I | 89 (12.2%) | 7 (3.8%) | 82 (14.9%) | I | 17 (5.0%) | 2 (1.8%) | 15 (6.6%) | ||
| II | 190 (26.0%) | 38 (20.8%) | 152 (27.7%) | II | 71 (20.9%) | 17 (15.0%) | 54 (23.9%) | ||
| III | 453 (61.9%) | 138 (75.4%) | 315 (57.4%) | III | 251 (74.0%) | 94 (83.2%) | 157 (69.5%) | ||
| Missing | 8 | 2 | 6 | ||||||
| Subtypea | >.99 (HR-positive vs TNBC) | Subtypea | .91 (HR-positive vs TNBC) | ||||||
| HR-positive | 401 (54.2%) | 100 (54.1%) | 301 (54.2%) | HR-positive | 165 (48.7%) | 54 (47.8%) | 111 (49.1%) | ||
| HR >10% | 346 (46.8%) | 85 (45.9%) | 261 (47.0%) | TNBC | 174 (51.3%) | 59 (52.2%) | 115 (50.9%) | ||
| HR 1 to 10%b | 55 (7.4%) | 15 (8.1%) | 40 (7.2%) | ||||||
| TNBC | 339 (45.8%) | 85 (45.9%) | 254 (45.8%) | ||||||
| HER2 IHC score | .53 | ||||||||
| Negative (0) | 351 (48.6%) | 89 (49.4%) | 262 (48.3%) | ||||||
| Negative (1+) | 242 (33.5%) | 55 (30.6%) | 187 (34.5%) | ||||||
| Equivocal (2+) | 129 (17.9%) | 36 (20.0%) | 93 (17.2%) | ||||||
| Missing | 18 | 5 | 13 | ||||||
| Oncotype DX Recurrence Score | <.001 | Oncotype DX Recurrence Score | <.001 | ||||||
| Median | 20.0 | 25.0 | 18.0 | Median | 22.0 | 31.0 | 19.0 | ||
| Range | 1.0 to 71.0 | 10.0 to 71.0 | 1.0 to 50.0 | Range | 5.0 to 75.0 | 12.0 to 70.0 | 5.0 to 75.0 | ||
| N/A | N/A | ||||||||
| Not performed | 223 | 56 | 167 | Missing | 37 | 18 | 19 | ||
| Not HR+ | 339 | 85 | 254 | Not HR+ | 174 | 59 | 115 | ||
| Tumor stagea,c | >.99 | Tumor stagea | .69 | ||||||
| Stage I | 311 (42.0%) | 78 (42.2%) | 233 (42.0%) | Stage I | 105 (31.0%) | 38 (33.6%) | 67 (29.6%) | ||
| Stage II | 334 (45.1%) | 84 (45.4%) | 250 (45.0%) | Stage II | 157 (46.3%) | 49 (43.4%) | 108 (47.8%) | ||
| Stage III | 95 (12.8%) | 23 (12.4%) | 72 (13.0%) | Stage III | 77 (22.7%) | 26 (23.0%) | 51 (22.6%) | ||
| Nodal status | .86 | Nodal status | .64 | ||||||
| Negative | 500 (67.6%) | 124 (67.0%) | 376 (67.7%) | Negative | 181 (53.4%) | 58 (51.3%) | 123 (54.4%) | ||
| Positive | 240 (32.4%) | 61 (33.0%) | 179 (32.3%) | Positive | 158 (46.6%) | 55 (48.7%) | 103 (45.6%) | ||
| Triggering event | .66 | Triggering event | .80 | ||||||
| Abnormal mammogram/imaging | 287 (38.8%) | 69 (37.3%) | 218 (39.3%) | Screening mammogram | 19 (6.6%) | 7 (7.4%) | 12 (6.2%) | ||
| Physical exam/symptoms | 453 (61.2%) | 116 (62.7%) | 337 (60.7%) | Physical exam/symptoms | 269 (93.4%) | 87 (92.6%) | 182 (93.8%) | ||
| Missing | 51 | 19 | 32 | ||||||
| Genetic testing before diagnosis | <.001 | Genetic testing before diagnosis | <.001 | ||||||
| Yes | 92 (12.4%) | 61 (33.0%) | 31 (5.6%) | Yes | 16 (6.0%) | 13 (14.8%) | 3 (1.7%) | ||
| No | 623 (84.2%) | 118 (63.8%) | 505 (91.0%) | No | 249 (94.0%) | 75 (85.2%) | 174 (98.3%) | ||
| Unknown | 25 (3.4%) | 6 (3.2%) | 19 (3.4%) | Missing | 74 | 25 | 49 | ||
| Gene altered | - | Gene altered | – | ||||||
| BRCA1 | 105 (56.8%) | 105 (56.8%) | 0 | BRCA1 | 74 (65.5%) | 74 (65.5%) | 0 | ||
| BRCA2 | 80 (43.2%) | 80 (43.2%) | 0 | BRCA2 | 39 (34.5%) | 39 (34.5%) | 0 | ||
Matching variable.
Survey-based; patients may have selected more than 1 choice.
ER 1 to 10% and/or PR 1 to 10%.
Clinical stage if neoadjuvant only or both; pathological stage if adjuvant only or neither.
Includes DCIS with microinvasion.
“Missing” categories were excluded from P-value computation.
Abbreviations: COQD = Clinical Outcomes Quality Database; DCIS = ductal carcinoma in situ; ER = estrogen receptor; gBRCA1/2 = germline BRCA1/2; HR = hormone receptor; IHC = immunohistochemistry; N/A = not applicable; PR = progesterone receptor; PV = pathogenic variant; TNBC = triple-negative breast cancer; YWS = Young Women’s Breast Cancer Study.
Tumor subtype was well balanced between carriers and noncarriers in both cohorts. In COQD, slightly more patients had HR+/HER2− (54.2%) than TNBC (45.8%). In YWS, approximately half of all patients had TNBC (51.3%) and half had HR+/HER2− (48.7%) tumors.
In both cohorts, carriers had a higher tumor grade than noncarriers and, among those with HR+/HER2− and the Oncotype DX (ODX) test performed (44.4% of patients with HR+/HER2− in COQD; 77.6% in the YWS), a higher recurrence score (RS) (COQD: median = 25 vs 18, P < .001; YWS: median = 31 vs 19, P < .001).
Treatment patterns
In COQD, bilateral mastectomy was more common among carriers than noncarriers (75.1% vs 18.2%; P < .001), and carriers received radiation therapy after surgery less frequently (45.4% vs 81.4%, P < .001). When restricting the analysis to individuals who did not undergo mastectomy, most patients in both groups received radiation therapy, but the rate remained significantly lower in carriers compared with noncarriers (80% vs 94.7%, P = .008). Similar trends were observed in the YWS cohort, although the overall rate of bilateral mastectomy was higher in both carriers and noncarriers (84.1% vs 38.5%, P < .001) compared with the COQD cohort. Among patients in YWS who did not undergo mastectomy, all received radiation.
Patients with gBRCA1/2 PV in COQD received chemotherapy more often than noncarriers (81.1% vs 67.0%, P < .001), including platinum-based (18% vs 9.4%, P = .010) but not anthracycline-based regimens (81.3% vs 79.8%, P = .81). In the YWS cohort, the proportion of carriers and noncarriers who received chemotherapy was similar (91.2% vs 86.7%, P = .29), as was the administration of anthracycline-based (94.2% vs 90.8%, P = .38) and platinum-based regimens (10.7% vs 9.7%, P = .84), in line with the younger age and higher stage at presentation of patients included in this cohort (Table 2). Endocrine therapy was less often administered to carriers compared with noncarriers with HR+/HER2- tumors in both COQD (77.0% vs 88.4%, P = .008) and YWS (80.9% vs 93.3%, P = .041). PARP inhibitors were administered to 11 (7.3%) gBRCA1/2 PV carriers, and 3 additional carriers (2%) were enrolled in the OlympiA trial investigating adjuvant olaparib vs placebo in the COQD cohort. Only 1 gBRCA1/2 PV carrier in the YWS received a PARP inhibitor.
Table 2.
Administration and type of chemotherapy in carriers and noncarriers according to tumor subtype in the COQD (left) and YWS (right) cohorts.
| COQD cohort |
YWS cohort |
|||||||
|---|---|---|---|---|---|---|---|---|
|
gBRCA1/2 PV carriers
|
gBRCA1/2 wild type
|
gBRCA1/2 PV carriers
|
gBRCA1/2 wild type
|
|||||
| Type of chemotherapy | HR+/HER2− (n = 100) | TNBC (n = 85) | HR+/HER2− (n = 301) | TNBC (n = 254) | HR+/HER2− (n = 54) | TNBC (n = 59) | HR+/HER2− (n = 111) | TNBC (n = 115) |
| Chemotherapy | ||||||||
| Yes | 68 (68.0%) | 82 (96.5%) | 136 (45.2%) | 236 (92.9%) | 47 (87%) | 56 (94.9%) | 83 (74.8%) | 113 (98.3%) |
| No | 32 (32.0%) | 3 (3.5%) | 165 (54.8%) | 18 (7.1%) | 7 (13%) | 3 (5.1%) | 28 (25.2%) | 2 (1.7%) |
| Anthracycline-containing chemotherapy | ||||||||
| Yes | 52 (76.5%) | 70 (85.4%) | 108 (79.4%) | 189 (80.1%) | 44 (93.6%) | 53 (94.6%) | 73 (88%) | 105 (92.9%) |
| No | 16 (23.5%) | 12 (14.6%) | 28 (20.6%) | 47 (19.9%) | 3 (6.4%) | 3 (5.4%) | 10 (12%) | 8 (7.1%) |
| N/A (Did not receive chemo) | 32 | 3 | 165 | 18 | 7 | 3 | 28 | 2 |
| Platinum-containing chemotherapy | ||||||||
| Yes | 4 (5.9%) | 23 (28.0%) | 2 (1.5%) | 33 (14.0%) | 2 (4.3%) | 9 (16.1%) | 0 | 19 (16.8%) |
| No | 64 (94.1%) | 59 (72.0%) | 134 (98.5%) | 203 (86.0%) | 45 (95.7%) | 47 (83.9%) | 83 (100%) | 94 (83.2%) |
| N/A (Did not receive chemo) | 32 | 3 | 165 | 18 | 7 | 3 | 28 | 2 |
Abbreviations: COQD = Clinical Outcomes Quality Database; gBRCA1/2 = germline BRCA1/2; HR = hormone receptor; IHC = immunohistochemistry; N/A = not applicable; PV = pathogenic variant; TNBC = triple-negative breast cancer; YWS = Young Women’s Breast Cancer Study.
Survival outcomes
Median follow-up was 4.5 years (IQR = 2.9-6.2) for the COQD cohort and 11.1 years (IQR = 9.9-13.6) for the YWS cohort. Given the different follow-up, the iDFS and RFS rates at the 3-year landmark were estimated for the COQD cohort, and the 3-year, 5-year, and 8-year landmark rates as well as OS were estimated for the YWS cohort.
In the COQD cohort, the 3-year iDFS rate was similar between carriers and noncarriers (88.4% [95% CI = 83.2 to 93.9%] vs 88.9% [95% CI = 86.1 to 91.7%], adjusted hazard ratio = 0.72 [95% CI = 0.43 to 1.20], P = .20; Figure 1, Table S1, Table 3). Results were similar for HR+/HER2− and TNBC (Figure 1), even though a more pronounced trend toward lower risk of invasive disease recurrence was observed for carriers with TNBC compared with their matched noncarriers (3-year iDFS = 91.9% vs 84.3%; adjusted hazard ratio = 0.49 [95% CI = 0.22 to 1.11], P = .088) (Figure 1, Table S1). The distribution of iDFS events was different in carriers and noncarriers. Second primary malignancies and regional recurrences were more common among carriers; local recurrences, distant recurrences, and deaths were more frequent in noncarriers (Table S2).
Figure 1.
Invasive disease-free survival (left) and relapse-free survival (right) comparing gBRCA carriers vs noncarriers in the overall population (A and D) and according to tumor subtype (B and E: HR+; C and F: TN) in the COQD cohort.
Abbreviations: COQD = Clinical Outcomes Quality Database; gBRCA = germline BRCA; HR+ = hormone receptor positive; TN = triple negative.
Table 3.
Adjusted iDFS and RFS analyses in the COQD (left) and YWS (right) cohorts.
| Variable | iDFS hazard ratio | 95% CI | P | RFS hazard ratio | 95% CI | P | iDFS hazard ratio | 95% CI | P | RFS hazard ratio | 95% CI | P |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| COQD cohort a (n = 732) | YWS cohort (n = 339) | |||||||||||
| (.002) | (<.001)c | (.55) | (.16)c | |||||||||
| gBRCA1/2 - PV vs wt (ref) | 0.72 | 0.43 to 1.20 | .20 | 0.48 | 0.26 to 0.87 | .016 | 0.83 | 0.54 to 1.29 | .41 | 0.71 | 0.44 to 1.17 | .18 |
| Age | 1.00 | 0.98 to 1.01 | .72 | 0.99 | 0.97 to 1.01 | .37 | 1.00 | 0.96 to 1.05 | .90 | 0.98 | 0.93 to 1.03 | .32 |
| Chemotherapy Yes vs No (ref) | 1.02 | 0.50 to 2.07 | .96 | 1.21 | 0.54 to 2.69 | .64 | 1.34 | 0.61 to 2.91 | .47 | 1.53 | 0.63 to 3.72 | .34 |
| Grade—I vs III (ref)b | 0.18 | 0.05 to 0.66 | .010 | 0.15 | 0.03 to 0.68 | .015 | 0.41 | 0.09 to 1.78 | .24 | 0.27 | 0.04 to 2.06 | .21 |
| Grade—II vs III (ref) | 0.43 | 0.22 to 0.81 | .009 | 0.37 | 0.19 to 0.76 | .006 | 1.20 | 0.74 to 1.94 | .47 | 1.36 | 0.81 to 2.28 | .24 |
| COQD cohort—HR+/HER2− (n = 397) | YWS cohort HR+/HER2− (n = 165) | |||||||||||
| (.009) | (.001)c | (.52) | (.30)c | |||||||||
| gBRCA1/2 - PV vs wt (ref) | 0.96 | 0.48 to 1.91 | .90 | 0.70 | 0.32 to 1.50 | .35 | 0.93 | 0.49 to 1.79 | .84 | 0.66 | 0.30 to 1.42 | .29 |
| Age | 1.00 | 0.98 to 1.03 | .96 | 0.99 | 0.96 to 1.02 | .60 | 1.05 | 0.97 to 1.13 | .25 | 1.02 | 0.94 to 1.10 | .68 |
| Chemotherapy Yes vs No (ref) | 0.99 | 0.43 to 2.26 | .97 | 1.17 | 0.47 to 2.95 | .74 | 1.24 | 0.52 to 2.97 | .63 | 1.50 | 0.55 to 4.10 | .43 |
| Grade—I vs III (ref)b | 0.17 | 0.04 to 0.63 | .008 | 0.13 | 0.03 to 0.63 | .012 | 0.43 | 0.09 to 1.99 | .28 | 0.29 | 0.04 to 2.35 | .25 |
| Grade—II vs III (ref) | 0.40 | 0.19 to 0.86 | .018 | 0.33 | 0.14 to 0.75 | .008 | 1.26 | 0.67 to 2.36 | .48 | 1.48 | 0.73 to 2.97 | .28 |
| COQD cohort—TNBC (n = 335) | YWS cohort—TNBC (n = 174) | |||||||||||
| (.34) | (.11)c | (.66) | (.27)c | |||||||||
| gBRCA1/2 - PV vs wt (ref) | 0.49 | 0.22 to 1.11 | .088 | 0.28 | 0.10 to 0.78 | .015 | 0.79 | 0.43 to 1.43 | .44 | 0.81 | 0.43 to 1.55 | .53 |
| Age | 0.99 | 0.97 to 1.02 | .61 | 0.99 | 0.96 to 1.02 | .42 | 0.97 | 0.92 to 1.03 | .33 | 0.94 | 0.89 to 1.00 | .062 |
| Chemotherapy Yes vs No (ref) | 1.55 | 0.20 to 12.11 | .67 | 1.37 | 0.17 to 10.81 | .76 | 1.54 | 0.21 to 11.25 | .67 | 1.14 | 0.15 to 8.38 | .90 |
| Grade—I vs III (ref)b | – | – | – | – | – | – | – | – | – | – | – | – |
| Grade—II vs III (ref) | 0.40 | 0.09 to 1.72 | .22 | 0.44 | 0.10 to 1.92 | .28 | 1.68 | 0.51 to 5.52 | .40 | 2.33 | 0.70 to 7.80 | .17 |
Eight patients with missing grade were excluded.
No events observed among patients with grade 1 tumors and TNBC in the COQD cohort and in the YWS cohort.
Global log-rank test P-value.
P-values in bold are statistically significant.
Abbreviations: COQD = Clinical Outcomes Quality Database; HR = hormone receptor; iDFS = invasive disease-free survival; PV = pathogenic variant; RFS = recurrence-free survival; TNBC = triple-negative breast cancer; YWS = Young Women’s Breast Cancer Study; wt = wild type.
In the YWS cohort, the 3-, 5-, and 8-year iDFS rates were 83.8% (95% CI = 77.2% to 90.9%), 79.5% (95% CI = 72.1% to 87.6%), and 73.5% (95% CI = 64.8% to 83.2%) for carriers and 82.4% (95% CI = 77.5% to 87.6%), 76.6% (95% CI = 71.2% to 82.5%), and 68.4% (95% CI = 62.0% to 75.4%) for noncarriers (adjusted hazard ratio = 0.83 [95% CI = 0.54 to 1.29], P = .41; Figure 2, Table 3, Table S3). Results were similar for HR+/HER2− and TNBC (Table 4, Figure S3). As in the COQD cohort, second primary malignancies were more common among carriers, whereas distant recurrences were more frequent among noncarriers (Table S4).
Figure 2.
Invasive disease-free survival (A), relapse-free survival (B), and overall survival (C) comparing gBRCA carriers vs noncarriers in the YWS cohort.
Abbreviations: gBRCA = germline BRCA; YWS = Young Women’s Breast Cancer Study.
Table 4.
3-year, 5-year, 8-year, and 10-year iDFS (top), RFS (middle), and OS (bottom) rates among gBRCA1/2 carriers according to tumor subtype from the YWS cohort.
| Tumor subtype | 3-year iDFS (95% CI) | 5-year iDFS (95% CI) | 8-year iDFS (95% CI) | 10-year iDFS (95% CI) |
|---|---|---|---|---|
| HR+/HER2− TNBC | 90.5 (82.9 to 98.8) | 84.0 (74.3 to 94.9) | 70.6 (56.9 to 87.6) | 56.2 (37.7 to 83.7) |
| 77.6 (67.6 to 89.1) | 75.4 (64.9 to 87.6) | 75.4 (64.9 to 87.6) | 70.9 (58.6 to 85.9) | |
| 3-year RFS (%) | 5-year RFS (%) | 8-year RFS (%) | 10-year RFS (%) | |
| HR+/HER2− TNBC | 92.3 (85.4 to 99.9) | 90.0 (82.1 to 98.8) | 82.6 (70.8 to 96.4) | 68.9 (50.8 to 93.4) |
| 79.3 (69.5 to 90.5) | 79.3 (69.5 to 90.5) | 79.3 (69.5 to 90.5) | 79.3 (69.5 to 90.5) | |
| 3-year OS (%) | 5-year OS (%) | 8-year OS (%) | 10-year OS (%) | |
| HR+/HER2− TNBC | 96.2 (91.2 to 100) | 96.2 (91.2 to 100) | 94.2 (88.0 to 100) | 94.2 (88.0 to 100) |
| 86.2 (77.7 to 95.6) | 80.6 (71.0 to 91.6) | 80.6 (71.0 to 91.6) | 80.6 (71.0 to 91.6) |
Abbreviations: gBRCA1/2 = germline BRCA1/2; HR = hormone receptor; iDFS = invasive disease-free survival; OS = overall survival; RFS = recurrence-free survival; TNBC = triple-negative breast cancer; YWS = Young Women’s Breast Cancer Study.
Given the role of bilateral mastectomy in gBRCA PV carriers, we examined the incidence of in-breast events and locoregional recurrences as the first iDFS event, separately for carriers who did (n = 139 in COQD, n = 95 in YWS) or did not (n = 46 in COQD, n = 18 in YWS) undergo bilateral mastectomy. In the COQD cohort, locoregional recurrence rates were low across both groups (∼2%), and only 1 (2.2%) new primary breast cancer occurred among carriers treated with breast-conserving surgery. In the YWS cohort, locoregional recurrence rate was 5.6% among carriers who had breast-conserving surgery and 7.4% in carriers who had mastectomy; 2 (11.1%) new primary breast cancers were diagnosed in carriers receiving breast-conserving surgery and 1 (1.1%) in carriers receiving mastectomy.
In COQD, the 3-year RFS rate was 92.1% (95% CI = 87.6% to 96.8%) in carriers vs 89.3% (95% CI = 86.6% to 92.1%) in noncarriers, which was significantly different between the 2 groups (adjusted hazard ratio = 0.48 [95% CI = 0.26 to 0.87], P = .016; Figure 1, Table 3, Table S5). When analyzing tumor subtypes separately, the difference remained significant for TNBC (Table 3, Figure 1). In YWS, RFS was not statistically different between carriers and noncarriers (adjusted hazard ratio = 0.71 [95% CI = 0.44 to 1.17], P = .18), although carriers had a numerically lower risk of recurrence than noncarriers, especially in longer follow-up (8-year RFS rate = 81.2% [95% CI = 73.6% to 89.5%] for carriers vs 72.1% [95% CI = 66.0% to 78.8%] for noncarriers) (Figure 2; Tables S3 and S6). Results were similar for HR+/HER2− and TNBC (Table 4). DRFS was significantly lower for carriers vs noncarriers in COQD (adjusted hazard ratio = 0.47 [95% CI = 0.25 to 0.91], P = .024), and a similar but nonsignificant trend was observed in YWS (adjusted hazard ratio = 0.74 [95% CI = 0.43 to 1.25], P = .26) (Table S7).
A total of 60 deaths were recorded in the YWS cohort (16 in carriers, 44 in noncarriers), and OS was not significantly different between the 2 groups (adjusted hazard ratio = 0.71 [95% CI = 0.40 to 1.26], P = .24, Figure 2; Table S3) or in the subgroup analyses by tumor subtype (Table 4, Table S8).
Among gBRCA1/2 PV carriers in the YWS cohort, who had a long enough follow-up to investigate the dynamics of recurrence over time, we observed that most recurrences in carriers with TNBC occurred in the first 3 years, whereas most carriers with HR+/HER2− disease experienced recurrence more than 5 years after diagnosis (Table 4, Figure 3). However, there was no significant interaction of tumor subtype and RFS time (P = .29), and similar results were observed when testing the interaction of subtype and iDFS time (P = .18) or OS time (P = .32).
Figure 3.
Invasive disease-free survival (top) and relapse-free survival (bottom) of gBRCA1/2 PV carriers (blue) and noncarriers (yellow) according to tumor subtype (HR+/HER2− vs TNBC) in the YWS cohort.
Abbreviations: gBRCA1/2 = germline BRCA1/2; HR = hormone receptor; iDFS = invasive disease-free survival; RFS = recurrence-free survival; TNBC = triple-negative breast cancer; YWS = Young Women’s Breast Cancer Study.
gBRCA1 and gBRCA2 subgroup analyses
For both the COQD and the YWS cohorts, we performed a sensitivity analysis and investigated separately gBRCA1 carriers vs their matched noncarriers and gBRCA2 carriers vs their matched noncarriers.
The matching variables were well-balanced between carriers and noncarriers, except for age among gBRCA1 carriers in COQD (P = .009) due to the lack of young patients among noncarriers. The populations of gBRCA1 and gBRCA2 PV carriers were generally distinct: patients with gBRCA1 PV were generally younger and had more often triple-negative and grade 3 tumors; gBRCA2 PV carriers were usually diagnosed with HR+/HER2− tumors, more often had lobular tumors and were stage III at diagnosis (Tables S9-S10). The proportion of patients who underwent genetic testing before diagnosis was ∼30% for both gBRCA1 and gBRCA2 PV carriers in COQD, whereas 20% of gBRCA1 and only 3% of gBRCA2 PV carriers were tested before diagnosis in YWS.
Regarding treatment patterns, some differences emerged between gBRCA1 and gBRCA2 PV carriers (Tables S11-S12). The proportion of patients who received chemotherapy was >90% for gBRCA1 PV carriers in both COQD and YWS; 84.6% for gBRCA2 PV carriers in YWS, and 67.5% for gBRCA2 PV carriers in COQD. Anthracycline-containing regimens were administered to a similar proportion of gBRCA1 and gBRCA2 PV carriers (∼80% in COQD, >90% in YWS); platinum was more frequently given to gBRCA1 carriers (14.3% in YWS, 21.9% in COQD) than gBRCA2 carriers (3% in YWS, 11.1% in COQD).
In the survival analyses, iDFS and RFS were similar between gBRCA2 PV carriers and their noncarriers in both cohorts (data not shown). However, gBRCA1 PV carriers had a statistically significantly lower risk of recurrence than noncarriers in COQD (3-year iDFS = 92.5% [95% CI = 86.7% to 98.7%] vs 87.1% [95% CI = 83.2% to 91.2%], adjusted hazard ratio = 0.44 [95% CI = 0.20 to 0.99], P = .046; 3-year RFS = 94.6% [95% CI = 89.3% to 100%] vs 87.1% [95% CI = 83.2% to 91.2%], adjusted hazard ratio = 0.25 [95% CI = 0.09 to 0.70], P = .009; Figure 4), although the same was not observed in YWS. OS in the YWS cohort was similar between carriers and noncarriers in both subgroup analyses by gBRCA1 vs gBRCA2 status.
Figure 4.
Relapse-free survival of gBRCA1 PV carriers vs noncarriers in the COQD cohort.
Abbreviations: COQD = Clinical Outcomes Quality Database; gBRCA1 = germline BRCA1; RFS = relapse-free survival.
Discussion
Although gBRCA1/2 PV-associated breast cancer represents up to 10% of new diagnoses, few studies have clarified whether carrier status affects disease outcomes or have addressed how to best treat these patients. Previous efforts comparing outcomes between carriers and noncarriers led to conflicting results,5,6,8,12 and most included patients diagnosed more than 20 years ago, not reflecting contemporary treatment approaches.8,12 Current guidelines recommend adjuvant olaparib for high-risk patients,13-16 and historically the perception has also been that these patients derive greater benefit from systemic chemotherapy than noncarriers.17 However, data on the differential benefit of other systemic therapies beyond olaparib and platinum between carriers and noncarriers remain limited, because clinical trial subgroup analyses by gBRCA status are rarely reported. To assess whether gBRCA PV carriers might benefit from a tailored therapy approach due to distinct biology and risk of recurrence, we compared outcomes between carriers and matched noncarriers, further adjusting for prognostic variables that differed between the 2 groups.
In the COQD cohort including patients of all ages, we observed a lower risk of relapse for gBRCA PV carriers than matched patients with sporadic tumors. When considering HR+/HER2− and TNBC separately, we observed a trend toward a lower risk of recurrence for carriers compared with noncarriers with HR+/HER2− disease, and a significant 10% difference in the absolute risk of relapse at 3 years favoring gBRCA-associated over sporadic TNBC. This is in line with previous findings from the POSH study, in which an OS advantage was observed for gBRCA-associated compared with sporadic TNBC, even though no differences were observed in terms of distant disease-free survival (DDFS). Carriers more often received platinum and PARP inhibitors, which are highly effective in gBRCA-associated tumors and may have contributed to the improved outcome in this subgroup of patients. However, a recent secondary analysis from the BRCA BCY registry failed to show a DDFS or OS benefit from the addition of platinum for patients with TNBC,18 highlighting the need for clinical trials or powered subgroup analyses to identify the best treatment strategy for gBRCA1/2 carriers.
In the YWS cohort, survival outcomes were comparable for young women with or without gBRCA PV, although a numerical trend toward better long-term outcomes was observed for gBRCA PV carriers vs noncarriers (3-year RFS = 85.5% vs 83.3%, 8-year RFS = 81.2% vs 72.1%). The difference remained nonsignificant when considering separately HR+/HER2− and TNBC, and a similar numerical trend toward better outcomes was observed for gBRCA PV carriers vs noncarriers for both subtypes. Although it is likely that the relatively small sample size limited the power to detect significant differences, it is noteworthy that the trend favoring better outcome for HR+/HER2− tumors in both COQD and YWS is in contrast with recent results from the large CARRIERS study, in which patients with gBRCA-associated tumors had worse overall and breast cancer-specific survival than those with sporadic HR+ tumors.19 Lack of granular information on key prognostic features—such as HER2 status, treatment types, and genomic features—limits the understanding of the population included in CARRIERS and, in particular, whether additional adjustment was needed to optimally compare carriers and noncarriers.
Our findings regarding patterns of recurrence of gBRCA1/2 PV carriers by HR status warrant further discussion. Recent data from the large BRCA BCY collaboration registry comparing outcomes between young gBRCA PV carriers with HR+/HER2− vs TNBC reported a higher risk of recurrence for patients with TNBC in the first 5 years after diagnosis, whereas no difference was observed beyond 5 years.12 In the YWS cohort, which had long enough follow-up to look at pattern of relapse over time, we also observed a numerically lower risk of recurrence for HR+/HER2− than TNBC among gBRCA PV carriers up to ∼7 years from surgery, after which the pattern reversed (Figure 3). However, the interaction between survival time and tumor subtype was not significant, possibly due to the small sample size.
Although we purposely decided not to merge or compare the 2 cohorts due to differences in patient population, treatments, and data collection methods, the differences in short-term outcomes among gBRCA carriers are noteworthy. The low risk of relapse observed among gBRCA carriers with TNBC in COQD (3-year RFS = 94.4%) compared with YWS (3-year RFS = 79.3%) may be explained by the higher prevalence of stage I tumors and the more frequent use of platinum and chemotherapy in the COQD cohort, or different tumor biology. We observed instead a similar 3-year RFS of 90.3% in COQD and 92.3% in YWS for patients with HR+/HER2− disease, even though the short follow-up of COQD limits the analysis of long-term outcomes, and whether the rate of late recurrences among individuals with HR+/HER2− differs in COQD vs YWS is unknown.
Given the ongoing discussion around the role of bilateral mastectomy for gBRCA1/2 carriers, we examined the incidence of in-breast and locoregional events separately among carriers who did or did not undergo bilateral mastectomy. In the COQD cohort, very few local events were recorded overall and across both groups, consistent with what was observed in the control arm of OlympiA after 6.5 years of follow-up (new breast primaries = 3.9%, local recurrences = 1.3%).20 In contrast, the YWS cohort showed a higher overall frequency of local events. However, carriers who underwent bilateral mastectomy had lower rates of second breast primaries (1.1%) and locoregional recurrences (7.4%) compared with carriers who did not receive bilateral mastectomy (locoregional recurrences = 5.6%, second breast primaries = 11.1%). Findings from YWS align with recently published data from the large BRCA BCY collaboration that also included women younger than 40, for whom a significantly lower incidence of in-breast events was observed for patients receiving risk-reducing mastectomy,21 highlighting the role of screening and preventive surgery in this population. Differences in patient characteristics, baseline risk, and duration of follow-up likely account for the different rates of local events observed between COQD and YWS.
Our study has some limitations. First, as a retrospective analysis, it is subject to inherent bias due to lack of randomization leading to heterogeneity in clinicopathological features and treatments. Second, the YWS was conducted between 2006 and 2016, before the availability of adjuvant PARP inhibitors, CDK4/6 inhibitors, or immunotherapy. Similarly, only a few patients in COQD received adjuvant olaparib or immunotherapy, and only 1 in the YWS. Third, information related to prophylactic oophorectomy or screening for other gBRCA1/2-related cancer was not available for this analysis. Fourth, our matching approach was constrained by the limited number of eligible noncarriers meeting matching criteria, even though we further adjusted for known prognostic variables. Finally, follow-up in the COQD cohort was relatively short, limiting our ability to investigate long-term outcomes, and the relatively small sample size of the YWS cohort likely limited the power to detect significant differences.
In conclusion, to our knowledge, this is one of the largest matched studies comparing outcomes between gBRCA1/2 PV carriers and noncarriers treated with contemporary systemic therapies. After adjusting for differences in clinical features and treatments, we observed a significantly lower risk of relapse among carriers compared with matched noncarriers in the COQD cohort, with a favorable trend in other survival outcomes observed across both cohorts. The benefit in COQD appears to be driven primarily by triple-negative tumors. These findings underscore the importance of treatment tailoring according to tumor biology. For instance, gBRCA carriers with TNBC might be more sensitive to available treatment options compared with noncarriers and potentially benefit from de-escalation strategies that warrant further investigation. Moreover, our results provide risk estimates that may inform the design and stratification of future clinical trials involving gBRCA1/2 carriers.
Supplementary Material
Acknowledgments
The funders had no role in the design of the study; the collection, analysis, or interpretation of the data; the writing of the manuscript; or the decision to submit the manuscript for publication. These data were presented in part at the 2023 ESMO Congress and at the 2024 San Antonio Breast Cancer Symposium. We would like to thank Kaitlyn T. Bifolck for editing and submission assistance (she is a full-time employee of Dana-Farber Cancer Institute).
Contributor Information
Stefania Morganti, Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States; Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Broad Institute of MIT and Harvard, Cambridge, MA, United States.
Se E Kim, Data Science, Dana-Farber Cancer Institute, Boston, MA, United States.
Qingchun Jin, Data Science, Dana-Farber Cancer Institute, Boston, MA, United States.
Jaeyoon Cha, Harvard Medical School, Boston, MA, United States.
Jacqueline E Zeigler, Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States; Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, United States.
Alexandra B Newman, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, United States.
Gregory J Kirkner, Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States; Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, United States.
Craig Snow, Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States; Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, United States.
Yue Zheng, Data Science, Dana-Farber Cancer Institute, Boston, MA, United States.
Julie Vincuilla, Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, United States; Division of Breast Surgery, Department of Surgery, Brigham and Women’s Hospital, Boston, MA, United States.
Tonia Parker, Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, United States; Division of Breast Surgery, Department of Surgery, Brigham and Women’s Hospital, Boston, MA, United States.
Ryan M Buehler, Division of Cancer Genetics and Prevention, Dana-Farber Cancer Institute, Boston, MA, United States.
Brittany L Bychkovsky, Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States; Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Division of Cancer Genetics and Prevention, Dana-Farber Cancer Institute, Boston, MA, United States.
Kate E Dibble, Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States; Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
Tal Sella, Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States; Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
Kathryn J Ruddy, Department of Oncology, Mayo Clinic, Rochester, MN, United States.
Shoshana M Rosenberg, Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States; Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
Laura C Collins, Department of Pathology and Laboratory Medicine, Weill Cornell Medicine/New York Presbyterian Hospital, New York, NY, United States.
Jeffrey Peppercorn, Harvard Medical School, Boston, MA, United States; Division of Hematology/Oncology, Massachusetts General Hospital, Boston, MA, United States.
Lidia Schapira, Division of Medical Oncology, Department of Medicine, Stanford Cancer Institute, Palo Alto, CA, United States.
Virginia F Borges, Division of Medical Oncology, Department of Medicine, University of Colorado, Denver, CO, United States.
Yaileen D Guzman-Arocho, Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, United States.
Steven E Come, Harvard Medical School, Boston, MA, United States; Division of Hematology/Oncology, Massachusetts General Hospital, Boston, MA, United States.
Ellen Warner, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
Philip D Poorvu, Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States; Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
Matteo Lambertini, Medical Oncology Unit, Ente Ospedaliero Ospedali Galliera, Genova, Italy; Department of Internal Medicine and Medical Specialties (DIMI), School of Medicine, University of Genova, Genoa, Italy.
Giuseppe Curigliano, Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology IRCCS, Milan, Italy; Department of Oncology and Haemato-Oncology, University of Milano, Milan, Italy.
Melissa E Hughes, Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States; Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, United States.
Elizabeth A Mittendorf, Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Division of Breast Surgery, Department of Surgery, Brigham and Women’s Hospital, Boston, MA, United States.
Tari A King, Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Division of Breast Surgery, Department of Surgery, Brigham and Women’s Hospital, Boston, MA, United States.
Nancy U Lin, Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States; Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
Judy E Garber, Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States; Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Department of Medicine, Brigham and Women’s Hospital, Boston, MA, United States.
Nabihah Tayob, Harvard Medical School, Boston, MA, United States; Broad Institute of MIT and Harvard, Cambridge, MA, United States.
Sara M Tolaney, Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States; Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
Ann H Partridge, Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States; Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
Filipa Lynce, Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States; Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
Author contributions
Stefania Morganti (Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Software, Supervision, Visualization, Writing—original draft, Writing—review & editing), Se E. Kim (Data curation, Formal analysis, Methodology, Software, Validation, Visualization, Writing—review & editing), Qingchun Jin (Data curation, Formal analysis, Methodology, Software, Validation, Visualization, Writing—review & editing), Jaeyoon Cha (Data curation, Investigation, Writing—review & editing), Jacqueline E. Zeigler (Data curation, Investigation, Writing—review & editing), Alexandra B. Newman (Data curation, Investigation, Writing—review & editing), Gregory J. Kirkner (Data curation, Writing—review & editing), Craig Snow (Data curation, Project administration, Supervision, Writing—review & editing), Yue Zheng (Data curation, Formal analysis, Methodology, Software, Validation, Writing—review & editing), Julie Vincuilla (Data curation, Investigation, Writing—review & editing), Tonia Parker (Data curation, Investigation, Writing—review & editing), Ryan M. Buehler (Data curation, Writing—review & editing), Brittany L. Bychkovsky (Writing—review & editing), Kate E. Dibble (Data curation, Writing—review & editing), Tal Sella (Writing—review & editing), Kathryn J. Ruddy (Writing—review & editing), Shoshana M. Rosenberg (Writing—review & editing), Laura C. Collins (Writing—review & editing), Jeffrey Peppercorn (Writing—review & editing), Lidia Schapira (Writing—review & editing), Virginia F. Borges (Writing—review & editing), Yaileen D. Guzman-Arocho (Writing—review & editing), Steven E. Come (Writing—review & editing), Ellen Warner (Writing—review & editing), Philip D. Poorvu (Writing—review & editing), Matteo Lambertini (Writing—review & editing), Giuseppe Curigliano (Supervision, Writing—review & editing), Melissa E. Hughes (Project administration, Supervision, Writing—review & editing), Elizabeth A. Mittendorf (Funding acquisition, Resources, Writing—review & editing), Tari A. King (Funding acquisition, Resources, Writing—review & editing), Nancy U. Lin (Funding acquisition, Resources, Supervision, Writing—review & editing), Judy E. Garber (Funding acquisition, Resources, Supervision, Writing—review & editing), Nabihah Tayob (Formal analysis, Methodology, Software, Supervision, Validation, Visualization, Writing—review & editing), Sara M. Tolaney (Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing—review & editing), Ann H. Partridge (Funding acquisition, Methodology, Project administration, Resources, Writing—review & editing), and Filipa Lynce (Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing—original draft, Writing—review & editing)
Supplementary material
Supplementary material is available at JNCI Cancer Spectrum online.
Funding
We are grateful for the funding support from METAvivor (S.M.), Breast Cancer Research Foundation (S.M.T.), the Elaine and Eduardo Saverin Foundation, and the Helen Gurley Brown Foundation (K.E.D).
Conflicts of interest
S.M. reports institutional research support from Precede Biosciences and Merck; consulting role at Daiichi-Sankyo. T.S. reports speaker honoraria for Stemline, Roche, Lilly, Novartis, AstraZeneca, MSD; travel support from Roche, Gilead, Pfizer, AstraZeneca, Oncotest, Stemline; compensated service on scientific advisory boards for Roche and Novartis. E.A.M. reports compensated service on scientific advisory boards for AstraZeneca, BioNTech, and Merck; uncompensated service on steering committees for Bristol Myers Squibb and Roche/Genentech; speaker honoraria and travel support from Merck Sharp & Dohme; and institutional research support from Roche/Genentech (via SU2C grant) and Gilead. E.A.M. also reports research funding from Susan Komen for the Cure, for which she serves as a scientific advisor, and uncompensated participation as a member of the American Society of Clinical Oncology Board of Directors. T.A.K. reports speaker honoraria for Exact Sciences, compensated service on the FES Steering Committee for GE Healthcare, compensated service for a Prosigna advisory board role for Veracyte, compensated service on the advisory board for Ataraxia AI, and compensated service as faculty for PrecisCa cancer information service. N.U.L. reports institutional research support from Genentech, Pfizer, Merck, Seattle Genetics, Zion Pharmaceuticals, Olema Pharmaceuticals, AstraZeneca; consulting honoraria from Seattle Genetics, Daiichi Sankyo, AstraZeneca, Olema Pharmaceuticals, Stemline/Menarini, Artera Inc., Eisai, and Shorla Oncology; royalties from Up to Date (book); and travel support from Olema, AstraZeneca, and Daiichi Sankyo. S.M.T. reports consulting or advisory roles at Novartis, Pfizer/SeaGen, Merck, Eli Lilly, AstraZeneca, Genentech/Roche, Eisai, Bristol Myers Squibb/Systimmune, Daiichi Sankyo, Gilead, Blueprint Medicines, Reveal Genomics, Sumitovant Biopharma, Artios Pharma, Menarini/Stemline, Aadi Bio, Bayer, Jazz Pharmaceuticals, Natera, Tango Therapeutics, eFFECTOR, Hengrui USA, Cullinan Oncology, Circle Pharma, Arvinas, BioNTech, Launch Therapeutics, Zuellig Pharma, Johnson & Johnson/Ambrx, Bicycle Therapeutics, BeiGene Therapeutics, Mersana, Summit Therapeutics, Avenzo Therapeutics, Aktis Oncology, Celcuity, Boehringer Ingelheim, Samsung Bioepis, and Olema Pharmaceuticals; institutional research funding from Genentech/Roche, Merck, Exelixis, Pfizer, Lilly, Novartis, Bristol Myers Squibb, AstraZeneca, NanoString Technologies, Gilead, SeaGen, OncoPep, Daiichi Sankyo, Menarini/Stemline, Jazz Pharmaceuticals, and Olema Pharmaceuticals; and travel support from Lilly, Gilead, Jazz Pharmaceuticals, Pfizer, Arvinas, and Roche. S.M.R. reports grant funding from Pfizer/Conquer Cancer. M.L. reports advisory role for Roche, Lilly, Novartis, AstraZeneca, Pfizer, Seagen, Gilead, MSD, Pierre Fabre, Menarini, and Exact Sciences; receiving speaker honoraria from Roche, Lilly, Novartis, Pfizer, Sandoz, Libbs, Daiichi Sankyo, Takeda, Ipsen, Menarini, and AstraZeneca; receiving travel grants from Gilead, Roche, and Daiichi Sankyo; receiving research funding (to his institution) from Gilead; and having nonfinancial interests as a member of the national council of the Italian Association of Medical Oncology (AIOM).
Data availability
Data can be requested from the corresponding author for academic use, subject to approval of a research plan, a data transfer agreement, and ethics committee approval.
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Data can be requested from the corresponding author for academic use, subject to approval of a research plan, a data transfer agreement, and ethics committee approval.