Abstract
Background
Breast cancer and ovarian cancer patients increasingly undergo germline genetic testing. However, little is known about cancer-specific mortality among carriers of a pathogenic variant (PV) in BRCA1/2 or other genes in a population-based setting.
Methods
Georgia and California Surveillance Epidemiology and End Results (SEER) registry records were linked to clinical genetic testing results. Women were included who had stages I-IV breast cancer or ovarian cancer diagnosed in 2013-2017, received chemotherapy, and were linked to genetic testing results. Multivariable Cox proportional hazard models were used to examine the association of genetic results with cancer-specific mortality.
Results
22 495 breast cancer and 4320 ovarian cancer patients were analyzed, with a median follow-up of 41 months. PVs were present in 12.7% of breast cancer patients with estrogen and/or progesterone receptor-positive, HER2-negative cancer, 9.8% with HER2-positive cancer, 16.8% with triple-negative breast cancer, and 17.2% with ovarian cancer. Among triple-negative breast cancer patients, cancer-specific mortality was lower with BRCA1 (hazard ratio [HR] = 0.49, 95% confidence interval [CI] = 0.35 to 0.69) and BRCA2 PVs (HR = 0.60, 95% CI = 0.41 to 0.89), and equivalent with PVs in other genes (HR = 0.65, 95% CI = 0.37 to 1.13), vs noncarriers. Among ovarian cancer patients, cancer-specific mortality was lower with PVs in BRCA2 (HR = 0.35, 95% CI = 0.25 to 0.49) and genes other than BRCA1/2 (HR = 0.47, 95% CI = 0.32 to 0.69). No PV was associated with higher cancer-specific mortality.
Conclusions
Among breast cancer and ovarian cancer patients treated with chemotherapy in the community, BRCA1/2 and other gene PV carriers had equivalent or lower short-term cancer-specific mortality than noncarriers. These results may reassure newly diagnosed patients, and longer follow-up is ongoing.
Genetic testing for inherited pathogenic variants (PVs) in cancer susceptibility genes has an established role in cancer treatment (1) and is relevant for secondary cancer risk reduction and testing of relatives (2). We and others reported that patients diagnosed with breast and/or ovarian cancer increasingly undergo germline sequencing of many genes (3-6). In this context, patients may experience a positive genetic test result as a worrisome second diagnosis (7,8) and wonder whether having a PV increases their chance of dying from their cancer. It is important to know whether cancer mortality is associated with germline PVs to inform treatment decision-making and how clinicians counsel patients about prognosis.
Prior studies have investigated breast and ovarian cancer-specific mortality among carriers of PVs in BRCA1 and/or BRCA2 (BRCA1/2), with mixed results. Some showed lower cancer-specific mortality in BRCA1/2 PV carriers, particularly among cohorts treated with chemotherapy, which may reflect a greater chemosensitivity of BRCA1/2 PV carriers vs noncarriers due to dysfunctional DNA repair (9-12). Additional studies showed higher cancer-specific mortality in BRCA1/2 PV carriers (13-16), whereas others showed no difference from patients who tested negative (17-23). Several prior studies were from single institutions or academic networks, which may introduce selection bias. Few studies analyzed results according to breast cancer subtypes or looked beyond BRCA1/2 to consider the many other genes now evaluated in clinical practice.
We studied cancer-specific mortality among a population-based cohort comprising all women diagnosed with breast cancer or ovarian cancer in Georgia or California and reported to statewide Surveillance Epidemiology and End Results (SEER) cancer registries from 2013 to 2017, together with their results of clinical germline genetic sequencing provided by testing laboratories. Given concerns that SEER underreports chemotherapy (24), we excluded patients with no chemotherapy reported, because of uncertainty about their actual treatment history; thus, we limited the study to patients with documented chemotherapy receipt. Based on studies suggesting high chemosensitivity in BRCA1/2 PV carriers (9-11,13,22,25), our hypothesis was that patients with a PV in BRCA1/2 or another cancer susceptibility gene would have lower cancer-specific mortality than patients having negative or uncertain genetic testing results.
Methods
Study Cohort and Dataset
All women diagnosed with breast cancer or ovarian cancer from January 1, 2013, to December 31, 2017, in California and Georgia and reported to SEER registries in California (the Los Angeles Cancer Surveillance Program, the Greater Bay Area Cancer Registry, and the Cancer Registry of Greater California) and in Georgia (the Georgia Cancer Registry) were linked to clinical germline genetic testing results from 4 laboratories (Ambry Genetics, Aliso Viejo, CA; GeneDx, Gaithersburg, MD; Invitae, San Francisco, CA; Myriad Genetics, Salt Lake City, UT) that performed the substantial majority of such testing as determined by genetic counselor and patient surveys (3,4,6). Probabilistic methods were used to optimize ascertainment and linkage accuracy, as previously reported (3,6). The analytic dataset combined genetic results from the 4 laboratories, from reports dated 2012 through the first quarter of 2019, with SEER variables.
Patients were included in the analytic cohort if they linked to a genetic result, had stages I-IV breast cancer or epithelial ovarian cancer, and received chemotherapy. Exclusion criteria included those ages younger than 20 years, more than 1 primary tumor, and diagnosed only on death certificate. Missingness was less than 5% for all variables except grade for ovarian cancer (20.6% missing). All observations with missing values were excluded except for ovarian cancer missing grade. Patients with nonepithelial ovarian cancer (eg, germ cell, sarcoma, and other histologies) were excluded because of their different epidemiology, genetics, and clinical course (Supplementary Figure 1, available online). The analytic file included both registry and laboratory information and was stripped of protected health information [as defined by the Health Information Portability and Accountability Act Privacy Rule (26)]. The study was approved by institutional review boards associated with the SEER registries.
Test Results from Laboratories
Germline genetic testing results were provided by laboratories at the level of the affected gene and consisted of the interpretation according to American College of Medical Genetics criteria that was returned to the ordering clinician: PV or likely PV (analyzed together as PV), variant of uncertain significance (VUS), and benign or likely benign (analyzed together as negative). Results from all laboratories were combined to ensure anonymity, and gene-specific results were analyzed only for those genes tested by 2 or more laboratories (n = 86).
Measures
Demographic and clinical measures were selected that were conceptually appropriate based on previously demonstrated relationships to cancer-related mortality, including social determinants of health (eg, race and ethnicity, poverty), tumor biologic features (eg, grade, subtype), and treatments (Tables 1 and 2). SEER registries provided diagnosis age, race and ethnicity (non-Hispanic White, Black, Asian, Native American and Alaskan Native, Hispanic), percent poverty at the census tract level (<10%, 10%-19%, ≥20%), marital status, tumor stage and grade, breast cancer subtype defined by expression of estrogen and/or progesterone receptors (ER/PR) and HER2 [ER/PR-positive, HER2-negative; HER2-positive with any ER/PR status, defined hereafter as HER2-positive; and ER/PR-negative and HER2-negative, defined hereafter as triple-negative breast cancer (TNBC)], and ovarian cancer histology (serous, mucinous, endometrioid, clear cell, or other adenocarcinoma). SEER registries provided information on breast cancer first-course treatment including surgery (breast-conserving surgery, unilateral or bilateral mastectomy), chemotherapy, radiotherapy, endocrine therapy, or HER2-directed therapy. First-course ovarian cancer treatment information included type of surgery, specifically debulking surgery; other surgery (SEER codes 17, 25-28, 35-37, 50-52, 55-57); or no surgery and radiotherapy. SEER registries provided date and cause of death. Overall, cancer-specific and other-cause mortality data were available through December 31, 2019, and patients alive then were coded as censored. Patients who died of other cancers or noncancer were coded as censored at date of death.
Table 1.
Characteristics of tested breast cancer patients and ovarian cancer patients who received chemotherapy
| Characteristic | Breast cancer No. (%)a |
Ovarian cancer No. (%)a |
|---|---|---|
| State | ||
| California | 15 390 (68.4) | 3129 (72.4) |
| Georgia | 7105 (31.6) | 1191 (27.6) |
| Age at cancer diagnosis, y | ||
| 20-29 | 588 (2.6) | 47 (1.1) |
| 30-39 | 3549 (15.8) | 184 (4.3) |
| 40-49 | 8086 (35.9) | 642 (14.9) |
| 50-59 | 5641 (25.1) | 1189 (27.5) |
| 60-69 | 3475 (15.4) | 1287 (29,8) |
| 70-79 | 1059 (4.7) | 765 (17.7) |
| 80 and older | 97 (<1) | 206 (4.8) |
| Race and ethnicity | ||
| Non-Hispanic White | 13 043 (58.0) | 2916 (67.5) |
| Black | 3324 (14.8) | 319 (7.4) |
| Native American or Alaskan Native | 71 (<1) | 10 (<1) |
| Asian or Pacific Islander | 2406 (10.7) | 464 (10.7) |
| Hispanic | 3651 (16.2) | 611 (14.1) |
| Poverty level | ||
| High (poverty ≥20%) | 4970 (22.1) | 894 (20.7) |
| Medium (10%-19%) | 7248 (32.2) | 1403 (32.5) |
| Low (poverty <10%) | 10 277 (45.7) | 2023 (46.8) |
| Marital status | ||
| Married | 14 123 (62.8) | 2433 (56.3) |
| Not married | 8372 (37.2) | 1887 (43.7) |
| Stage | ||
| I | 5530 (24.6) | 667 (15.4) |
| II | 11 188 (49.7) | 392 (9.1) |
| III | 4563 (20.3) | 2046 (47.4) |
| IV | 1214 (5.4) | 1215 (28.1) |
| Gradeb | ||
| 1 | 1474 (6.6) | 199 (5.8) |
| 2 | 8125 (36.1) | 398 (11.6) |
| 3 | 12 896 (57.3) | 1370 (39.9) |
| 4 (ovarian cancer only) | NA | 1465 (42.7) |
| Subtype (breast cancer only) | ||
| ER/PR-positive, HER2-negative | 10,956 (48.7) | NA |
| HER2-positive, any hormone receptor status | 6078 (27.0) | NA |
| ER/PR-negative and HER2-negative (triple-negative) | 5461 (24.3) | NA |
| Histology (ovarian cancer only) | ||
| Serous | NA | 3099 (71.7) |
| Mucinous | NA | 104 (2.4) |
| Endometrioid | NA | 438 (10.1) |
| Clear cell | NA | 310 (7.2) |
| Other adenocarcinoma | NA | 369 (8.6) |
Percentages may not sum to 100 because of rounding. ER = estrogen receptor; PR = progesterone receptor; NA = not applicable.
Grade was missing for 888 ovarian cancer patients.
Table 2.
Genetic testing results, treatment, and mortality of tested breast cancer patients and ovarian cancer patients treated with chemotherapy
| Characteristic | Breast cancer, No. (%) |
Ovarian cancer No. (%) |
||
|---|---|---|---|---|
| ER/PR-positive, HER2-negative | HER2-positive, any ER/PR status | ER/PR-negative, HER2-negative (triple-negative) | ||
| Total No. | 10 946 | 6089 | 5460 | 4320 |
| Genetic testing results | ||||
| Negative | 7549 (69.0) | 4286 (70.4) | 3619 (66.3) | 2737 (63.4) |
| BRCA1 PV | 320 (2.9) | 90 (1.5) | 517 (9.5) | 328 (7.6) |
| BRCA2 PV | 499 (4.6) | 142 (2.3) | 217 (4.0) | 242 (5.6) |
| Other genea PV | 567 (5.2) | 363 (6.0) | 182 (3.3) | 174 (4.0) |
| VUS | 2011 (18.4) | 1208 (19.8) | 925 (16.9) | 839 (19.4) |
| Surgery, breast | ||||
| Breast-conserving surgery | 3718 (34.0) | 2148 (35.3) | 2214 (40.5) | NA |
| Unilateral mastectomy | 2866 (26.2) | 1359 (22.3) | 1042 (19.1) | NA |
| Bilateral mastectomy | 2785 (25.4) | 1528 (25.1) | 1359 (24.9) | NA |
| Other surgery | 894 (8.2) | 496 (8.1) | 376 (6.9) | NA |
| No surgery | 683 (6.2) | 558 (9.2) | 469 (8.6) | NA |
| Surgery, ovarian | ||||
| Debulking surgery | NA | NA | NA | 2287 (52.9) |
| Other surgeryb | NA | NA | NA | 1675 (38.8) |
| No surgery | NA | NA | NA | 358 (8.3) |
| Radiation therapy | 6549 (59.8) | 3073 (50.5) | 2920 (53.5) | 47 (1.1) |
| Other systemic therapy | ||||
| Endocrine therapy | 8117 (74.2) | 3149 (51.7) | 276 (5.1) | 55 (1.3) |
| HER2-directed therapy | 433 (4.0) | 5090 (83.6) | 141 (2.6) | 209 (4.8) |
| Died from cancerc | 662 (6.0) | 246 (4.0) | 765 (14.0) | 1244 (28.8) |
| Average time at risk, days | 1156 | 1158 | 1111 | 1034 |
Other genes in which PVs were found are listed in Supplementary Table 1 (available online). ER = estrogen receptor; NA = not applicable; PR = progesterone receptor; PV = pathogenic variant; VUS = variant of uncertain significance.
Other surgeries recorded by Surveillance Epidemiology and End Results (SEER) for ovarian cancer treatment include local tumor destruction not otherwise specified, total removal of tumor or single ovary, unilateral or bilateral salpingo-oophorectomy with or without hysterectomy, and unilateral or bilateral salpingo-oophorectomy with omentectomy (SEER codes 17, 25-28, 35-37, 50-52, 55-57).
Median follow-up was 41 months (range = 1-85 months).
Statistical Analysis
Our question was whether PVs were associated with risk beyond that accounted for by known risk factors: thus, the base model included known correlates of breast and ovarian cancer-specific mortality (described in Measures). As covariates were selected based on known mortality associations, we did not refine the model further by excluding covariates based on P values or effect size. Genetic test results were then added, with the primary result being the magnitude and precision of resulting coefficients.
Separate models were specified for patients with each breast cancer subtype and ovarian cancer, because treatments and relationships of predictor variables to outcomes likely differ between these groups. We used multivariable Cox proportional hazard survival models to examine the association between genetic results, demographic and clinical factors with breast, and ovarian cancer-specific mortality. Competing risks of noncancer deaths were treated as censored. Date of chemotherapy initiation was used as the starting point for survival, and treatments that occurred after chemotherapy were coded as time-varying covariates to account for immortal time bias. Ovarian cancer grade was imputed using multiple imputation techniques.
Sensitivity Analysis
Proportional hazards assumptions were tested by including time-dependent covariates of all independent variables and testing for significance. All interactions between key covariates were tested. To assess generalizability to the nontested population, we examined a model with weights for test receipt. Weights were generated from a logistic regression model of genetic testing receipt across all patients (tested and not tested), using clinical and demographic measures as covariates. The inverse of the predicted probabilities of test receipt were used as weights. We examined respecification of competing mortality risks using a Fine and Gray analysis. To address potential effects of test timing and treatment selection, we excluded patients tested after treatment initiation. To account for potential error in reported cause of death, we evaluated overall rather than cancer-specific mortality.
Results
Study Population
Supplementary Figure 1 (available online) shows flow of patients into the analytic cohort, and Table 1 shows characteristics of genetically tested breast (n = 22 495) and ovarian cancer patients (n = 4320). Among breast cancer patients, 58.0% were non-Hispanic White, 14.8% Black, 16.2% Hispanic, 10.7% Asian or Pacific Islander, and less than 1% Native American or Alaskan Native, with a similar distribution in ovarian cancer patients. Approximately one-fifth of patients lived in high-poverty areas and half in low-poverty areas. Breast cancer subtype distribution was 48.7% ER/PR-positive, HER2-negative (n = 10 956), 27.0% HER2-positive (n = 6078), and 24.3% TNBC (n = 5461). Most (71.7%) ovarian cancer patients had serous histology and high (3 and 4, 81.6%) grades. The median follow-up was 41 (range = 1-85) months. For breast cancer patients, genetic testing occurred before diagnosis in 4.6% (n = 1037) and before chemotherapy initiation in 64.0% (n = 14 411); for ovarian cancer patients, these proportions were 2.9% (n = 124) and 18.9% (n = 857), respectively.
Genetic Testing Results, Treatment, and Mortality
Genetic results are summarized in Table 2. PVs were present in 12.6% (n = 1386) of patients with ER/PR-positive, HER2-negative breast cancer; 9.7% (n = 595) with HER2-positive breast cancer; 16.8% (n = 916) with TNBC; and 17.2% (n = 744) with ovarian cancer (Supplementary Table 1, available online). PVs were most common in BRCA1/2. Among breast cancer patients, other common PVs were, with ER/PR-positive, HER2-negative disease (n = 567 other gene PVs): CHEK2 (n = 214), PALB2 (n = 120), ATM (84), BRIP1 (n = 32), and TP53 (n = 22); with HER2-positive disease (n = 363 other gene PVs): CHEK2 (n = 156), ATM (n = 71), TP53 (n = 64), and PALB2 (n = 31); and with TNBC (n = 182 other gene PVs): PALB2 (n = 66), RAD51C (n = 23), BRIP1 (n = 23), CHEK2 (n = 23), ATM (n = 19), and RAD51D (n = 13). Among ovarian cancer patients, other common PVs (n = 174 other gene PVs) were in BRIP1 (n = 35), CHEK2 (n = 27), RAD51C (n = 24), ATM (n = 19), and RAD51D (n = 17).
Treatment receipt is shown according to breast cancer subtype (Table 2) and genetic testing results (Table 3). Death from the diagnosed cancer occurred in 6.0% of breast cancer patients with ER/PR-positive, HER2-negative disease; 4.0% with HER2-positive disease; 14.0% with TNBC; and 28.8% of ovarian cancer patients (Table 2). BRCA1/2 PV carriers were more likely than other patients to receive bilateral mastectomy and debulking surgery (Table 3).
Table 3.
Treatments received by genetic test results among tested breast cancer patients and ovarian cancer patients treated with chemotherapy
| Treatment | Genetic test result |
P b | ||||
|---|---|---|---|---|---|---|
| Negative No. (%) |
VUS only No. (%) |
BRCA1 PV No. (%) |
BRCA2 PV No. (%) |
Other genea PV No. (%) |
||
| Breast cancer | ||||||
| Surgery | <.001 | |||||
| No surgery | 1151 (8.0) | 330 (8.0) | 74 (8.0) | 73 (8.5) | 87 (7.8) | |
| Lumpectomy | 5942 (41.5) | 1582 (38.2) | 119 (12.8) | 113 (13.2) | 311 (28.0) | |
| Unilateral mastectomy | 3631 (25.4) | 990 (23.9) | 170 (18.3) | 200 (23.3) | 265 (23.8) | |
| Bilateral mastectomy | 3588 (25.1) | 908 (21.9) | 453 (48.9) | 383 (44.6) | 352 (31.7) | |
| Other surgery | 1142 (8.0) | 334 (8.1) | 111 (12.0) | 89 (10.4) | 97 (8.7) | |
| Radiation therapy | 8943 (62.5) | 2406 (58.1) | 304 (32.8) | 369 (43.0) | 524 (47.1) | <.001 |
| Other systemic therapy | ||||||
| Endocrine therapy | 7930 (55.4) | 2269 (54.8) | 238 (25.7) | 443 (51.6) | 674 (60.6) | <.001 |
| HER2-directed therapy | 3921 (27.4) | 1168 (28.2) | 95 (10.2) | 136 (15.9) | 342 (30.8) | <.001 |
| Ovarian Cancer | ||||||
| Surgery | .01 | |||||
| No surgery | 227 (1.6) | 81 (2.0) | 15 (1.6) | 24 (2.8) | 11 (1.0) | |
| Debulking surgery | 1458 (10.2) | 416 (10) | 203 (21.9) | 126 (14.7) | 84 (7.6) | |
| Other surgery | 1052 (7.4) | 342 (8.3) | 110 (11.9) | 92 (10.7) | 79 (7.1) | |
| Radiation therapy | 29 (0.2) | 13 (0.3) | 4 (0.4) | 0 (0) | 1 (0.1) | .31 |
| Other systemic therapy | ||||||
| Endocrine therapy | 33 (0.2) | 13 (0.3) | 3 (0.3) | 2 (0.2) | 4 (0.4) | .60 |
| HER2-directed therapy | 127 (0.9) | 42 (1.0) | 20 (2.2) | 11 (1.3) | 9 (0.8) | .83 |
Other genes in which PVs were found are listed in Supplementary Table 1 (available online). PV = pathogenic variant; VUS = variant of uncertain significance.
A 2-sided χ2 test was used to calculate the P values.
Breast Cancer-Specific Mortality
Multivariable model results are shown in Table 4. Among TNBC patients, those with BRCA1 PVs had lower cancer-specific mortality (hazard ratio [HR] = 0.49, 95% confidence interval [CI] = 0.35 to 0.69) vs those testing negative, as did BRCA2 PV carriers (HR = 0.60, 95% CI = 0.41 to 0.89). Equivalent cancer-specific mortality was observed among TNBC patients with other gene PVs (HR = 0.65, 95% CI = 0.37 to 1.13) vs those testing negative. Among patients with HER2-positive or ER/PR-positive, HER2-negative subtypes, there was no association of cancer-specific mortality with genetic test results. Other factors associated with increased cancer-specific mortality included higher stage, surgical procedure other than breast-conserving surgery, higher neighborhood poverty, and Black race, whereas Asian and Pacific Islander race and ethnicity was associated with lower cancer-specific mortality.
Table 4.
Breast cancer-specific mortality of patients in a multivariable proportional hazards survival model, by breast cancer subtypea
| Characteristic | ER/PR-positive, HER2-negative HR (95% CI) |
HER2-positive, any ER/PR status HR (95% CI) |
ER/PR-negative, HER2-negative (triple-negative) HR (95% CI) |
|---|---|---|---|
| Genetic testing results | |||
| Negative | 1 (Referent) | 1 (Referent) | 1 (Referent) |
| BRCA1 PV | 1.06 (0.71 to 1.58) | 0.92 (0.29 to 2.91) | 0.49 (0.35 to 0.69) |
| BRCA2 PV | 0.73 (0.50 to 1.04) | 0.39 (0.12 to 1.24) | 0.60 (0.41 to 0.89) |
| Other geneb PV | 0.73 (0.49 to 1.10) | 0.59 (0.26 to 1.32) | 0.65 (0.37 to 1.13) |
| VUS only | 0.81 (0.64 to 1.02) | 0.70 (0.49 to 1.01) | 0.79 (0.64 to 0.98) |
| Age (per 10-year difference) | 1.06 (0.99 to 1.14) | 1.04 (0.93 to 1.16) | 0.95 (0.90 to 1.02) |
| Race and ethnicity | |||
| Non-Hispanic White | 1 (Referent) | 1 (Referent) | 1 (Referent) |
| Native American or Alaskan Native | 2.40 (0.77 to 7.54) | –c | 0.56 (0.14 to 2.26) |
| Asian or Pacific Islander | 0.91 (0.68 to 1.21) | 1.20 (0.80 to 1.79) | 0.66 (0.47 to 0.93) |
| Black | 1.43 (1.14 to 1.79) | 2.03 (1.43 to 2.88) | 1.02 (0.84 to 1.23) |
| Hispanic | 1.07 (0.84 to 1.37) | 1.16 (0.79 to 1.71) | 0.94 (0.75 to 1.16) |
| Poverty level | |||
| Low (poverty <10%) | 1 (Referent) | 1 (Referent) | 1 (Referent) |
| Medium (10%-19%) | 0.95 (0.79 to 1.14) | 1.03 (0.75 to 1.43) | 1.07 (0.89 to 1.28) |
| High (poverty ≥20%) | 1.11 (0.90 to 1.36) | 1.59 (1.15 to 2.20) | 1.35 (1.12 to 1.62) |
| Married (vs not married) | 0.77 (0.65 to 0.90) | 0.90 (0.69 to 1.17) | 0.95 (0.82 to 1.11) |
| Stage | |||
| I | 1 (Referent) | 1 (Referent) | 1 (Referent) |
| II | 1.36 (0.96 to 1.92) | 1.63 (0.99 to 2.68) | 1.99 (1.51 to 2.63) |
| III | 4.42 (3.13 to 6.23) | 4.67 (2.81 to 7.78) | 6.89 (5.16 to 9.20) |
| IV | 16.59 (11.38 to 24.17) | 11.38 (6.69 to 19.38) | 16.58 (11.89 to 23.12) |
| Grade | |||
| 1 | 1 (Referent) | 1 (Referent) | 1 (Referent) |
| 2 | 1.58 (1.07 to 2.31) | 1.12 (0.40 to 3.10) | 1.04 (0.38 to 2.83) |
| 3 | 4.13 (2.84 to 6.01) | 1.80 (0.66 to 4.88) | 1.23 (0.46 to 3.29) |
| Surgery | |||
| Breast conserving | 1 (Referent) | 1 (Referent) | 1 (Referent) |
| Unilateral mastectomy | 2.03 (1.58 to 2.62) | 2.02 (1.32 to 3.09) | 2.26 (1.80 to 2.83) |
| Bilateral mastectomy | 1.60 (1.22 to 2.09) | 1.42 (0.90 to 2.26) | 1.70 (1.35 to 2.14) |
| No surgery | 3.70 (2.68 to 5.09) | 3.13 (1.95 to 5.03) | 4.26 (3.24 to 5.61) |
| Other surgery | 1.59 (1.08 to 2.34) | 3.34 (1.96 to 5.69) | 2.07 (1.49 to 2.88) |
| Radiotherapy (vs none) | 1.25 (1.03 to 1.51) | 1.44 (1.08 to 1.93) | 1.32 (1.12 to 1.57) |
| Endocrine therapy (vs none) | 0.79 (0.65 to 0.94) | 0.47 (0.35 to 0.63) | 1.08 (0.80 to 1.47) |
| HER2-directed therapy (vs none) | 0.82 (0.60 to 1.13) | 0.61 (0.44 to 0.84) | 1.21 (0.86 to 1.69) |
| Year of diagnosis | 1.05 (0.98 to 1.13) | 1.15 (1.02 to 1.29) | 1.04 (0.98 to 1.10) |
| California (vs Georgia) | 0.83 (0.69 to 1.01) | 0.86 (0.63 to 1.18) | 0.78 (0.66 to 0.93) |
Results presented are from a single multivariable proportional hazards model for each breast cancer subtype. CI = confidence interval; ER = estrogen receptor; HR = hazard ratio; PR = progesterone receptor; PV = pathogenic variant; VUS = variant of uncertain significance.
Other genes in which PVs were found are listed in Supplementary Table 1 (available online).
No observations for this group.
Ovarian Cancer-Specific Mortality
Multivariable model results are shown in Table 5. Compared with patients testing negative, lower mortality was seen in patients with PVs in BRCA2 (HR = 0.35, 95% CI = 0.25 to 0.49) and other tested genes (HR = 0.47, 95% CI = 0.32 to 0.69) but not with BRCA1 PVs. Other factors associated with higher cancer-specific mortality included older age, Native American and Alaskan Native race and ethnicity, higher stage, and no surgery.
Table 5.
Ovarian cancer-specific mortality of patients in a multivariable proportional hazards survival model
| Characteristic | Hazard ratio (95% CI) |
|---|---|
| Genetic testing results | |
| Negative | 1 (Referent) |
| BRCA1 PV | 0.85 (0.68 to 1.07) |
| BRCA2 PV | 0.35 (0.25 to 0.49) |
| Other gene PVa | 0.47 (0.32 to 0.69) |
| VUS only | 0.84 (0.72 to 0.98) |
| Age (per 10-year difference) | 1.16 (1.09 to 1.22) |
| Race and ethnicity | |
| Non-Hispanic White | 1 (Referent) |
| Black | 0.86 (0.69 to 1.07) |
| Native American or Alaskan Native | 2.88 (1.18 to 6.99) |
| Asian or Pacific Islander | 0.89 (0.72 to 1.10) |
| Hispanic | 0.91 (0.75 to 1.10) |
| Poverty level | |
| Low (poverty <10%) | 1 (Referent) |
| Medium (10%-19%) | 1.08 (0.95 to 1.23) |
| High (poverty ≥20%) | 0.97 (0.82 to 1.14) |
| Married (vs not married) | 0.93 (0.83 to 1.04) |
| Stage | |
| I | 1 (Referent) |
| II | 1.85 (1.18 to 2.90) |
| III | 5.88 (4.10 to 8.42) |
| IV | 8.18 (5.69 to 11.77) |
| Gradeb | |
| 1 | 1 (Referent) |
| 2 | 1.12 (0.76 to 1.64) |
| 3 | 1.16 (0.82 to 1.65) |
| 4 | 1.19 (0.83 to 1.69) |
| Serous (vs not serous)c | 0.80 (0.69 to 0.94) |
| Surgery | |
| No surgery | 1 (Referent) |
| Debulking surgery | 0.61 (0.50 to 0.73) |
| Other surgeryd | 0.47 (0.38 to 0.57) |
| Year of diagnosis | 1.07 (1.02 to 1.12) |
| California (vs Georgia) | 0.82 (0.73 to 0.93) |
Other genes in which PVs were found are listed in Supplementary Table 1 (available online). CI = confidence interval; PV = pathogenic variant; VUS = variant of unknown significance.
Multiple imputation was used for grade because 22% of patients had missing grade data.
Collapsed to serous vs not serous because of smaller numbers in subgroups of nonserous histology.
Other surgeries recorded by Surveillance Epidemiology and End Results (SEER) for ovarian cancer treatment include local tumor destruction not otherwise specified, total removal of tumor or single ovary, unilateral or bilateral salpingo-oophorectomy with or without hysterectomy, and unilateral or bilateral salpingo-oophorectomy with omentectomy (SEER codes 17, 25-28, 35-37, 50-52, 55-57).
Sensitivity Analysis
All covariates satisfied proportional hazards assumptions except stage (for TNBC and ER/PR-positive, HER2-negative disease), surgery (for TNBC and HER2-positive disease), endocrine therapy (for HER2-positive and ER/PR-positive, HER2-negative disease), grade (for ER/PR-positive, HER2-negative disease), radiation (for TNBC), marital status (for ovarian cancer), and histology (for ovarian cancer). Models were created with interactions between these variables and time: across all models and variables, the coefficients for the primary covariate of test result did not change in statistical significance or size. Models including interactions between test result and all other covariates found no statistically significant effects.
In models weighted for probability of genetic testing (Supplementary Tables 2 and 3, available online), PVs in genes other than BRCA1/2 were associated with lower breast cancer-specific mortality among patients with ER/PR-positive, HER2-negative disease (HR = 0.47, 95% CI = 0.30 to 0.75; Supplementary Table 2, available online). A Fine and Gray analysis accounting for competing mortality found similar results (ER/PR-positive, HER2-negative: HR = 0.63, 95% CI = 0.42 to 0.95; Supplementary Table 4, available online). Results for ovarian cancer patients did not change statistically significantly (Supplementary Tables 3 and 4, available online).
Sensitivity analyses of excluding patients tested after starting treatment (Supplementary Table 5, available online), overall mortality (Supplementary Table 6, available online), and stratifying by stage (Supplementary Table 7, available online) found no substantial difference in results. No PV was associated with higher cancer-specific or overall mortality in any analysis.
Discussion
We studied short-term cancer-specific mortality associated with germline genetic testing results among 22 495 breast cancer patients and 4320 ovarian cancer patients treated with chemotherapy in the population-based setting of 4 SEER registries comprising the statewide populations of California and Georgia. Consistent with our hypothesis, we found that compared with those testing negative for PVs, TNBC patients with PVs in BRCA1/2 and ovarian cancer patients with PVs in BRCA2 or other genes (notably BRIP1, CHEK2, RAD51C, and ATM) had lower cancer-specific mortality at 41 months’ median follow-up time. To our knowledge, this is the first population-based study to report lower short-term cancer-specific mortality associated with germline PVs in genes other than BRCA1/2. These findings can inform discussions about prognosis between patients and their oncologists.
Our findings add to an extensive literature on outcomes of BRCA1/2-associated breast cancer (13,14). There has been little consensus, with some studies reporting higher (14–16,27–29) and others lower cancer-specific mortality (30) among BRCA1/2 PV carriers vs noncarriers, whereas others found no difference (17,19,22,23,31). Focusing on higher-risk subtypes and chemotherapy recipients has offered more clarity: some studies found lower cancer-specific mortality among BRCA1/2 PV carriers who had TNBC and/or received chemotherapy (9,10,18,25,32). These results are consistent with clinical trials such as GeparSixto and INFORM, which showed that BRCA1/2 PV–associated cancers respond well to various chemotherapy regimens (33,34). Our finding of lower BRCA1/2-associated cancer-specific mortality with TNBC only may reflect its more aggressive biology than other subtypes, which might confer an earlier and greater benefit from chemotherapy. BRCA1/2 PV carriers might also have received more intensive chemotherapy—with more agents and/or of longer duration—than other patients. We found that BRCA1/2 PV carriers more often received bilateral mastectomy (and debulking surgery for ovarian cancer); however, multivariable modeling controlled for surgical procedure, so this variation does not account for the observed results. A limitation is that SEER does not report risk-reducing salpingo-oophorectomy, which may have contributed to the lower breast cancer-specific mortality observed in BRCA1/2 PV carriers. A further consideration is the short median follow-up of this study (41 months), because TNBC is prone to early recurrence and mortality (35). Longer follow-up is needed to determine whether lower cancer-specific mortality with BRCA1/2 PVs emerges for late-recurring subtypes, such as ER/PR-positive, HER2-negative disease (36). Future studies should also include cases diagnosed more recently, because poly (ADP-ribose) polymerase [PARP] inhibitors were not approved for BRCA1/2-associated metastatic disease until 2018, and thus their effects are unlikely to be substantial in this 2013-2017 diagnosis cohort (37–39).
We found no evidence of higher short-term cancer-specific mortality among TNBC patients with PVs in genes other than BRCA1/2. Furthermore, in 2 sensitivity analyses, we observed statistically significantly lower cancer-specific mortality with other gene PVs (most commonly CHEK2, PALB2, and ATM) among patients with ER/PR-positive, HER2-negative breast cancer. A study of CHEK2 PV carriers including all breast cancer subtypes found equivalent cancer-specific mortality to noncarriers in the first 6 years postdiagnosis, but twofold higher mortality afterward (40). Two hospital-based series found that overall mortality was higher among PALB2 PV carriers than noncarriers (16,41). The PATTERN trial of adjuvant chemotherapy for TNBC found no difference in disease-free survival between non-BRCA1/2 PV carriers and noncarriers (23). Although longer follow-up is essential, our early findings suggest that carriers of PVs in genes other than BRCA1/2 are not more likely (and may be less likely) than noncarriers to die of their breast cancer.
In contrast to breast cancer, prior ovarian cancer studies including some in population-based settings more consistently reported lower short-term cancer-specific mortality associated with BRCA1/2 PVs (11,12,14,42,43). However, longer-term studies reported attenuation or reversal of this advantage (44,45). Our finding of lower short-term ovarian cancer-specific mortality with BRCA2 PVs is largely consistent with prior studies (42). Our results may also reflect an emerging contribution from PARP inhibitors, which were approved in 2014 (46). However, longer follow-up is necessary.
We found lower short-term ovarian cancer-specific mortality with PVs in genes other than BRCA1/2 (notably BRIP1, RAD51C, CHEK2, and ATM), consistent with results of the Gynecologic Oncology Group 218 trial (12). To our knowledge, this has not previously been reported in community practice. High-grade, serous ovarian cancer often has a homologous recombination-deficient phenotype conferring sensitivity to chemotherapy, particularly platinum agents, and PARP inhibitors (47). However, there is greater treatment responsiveness among ovarian cancer patients who do vs who do not carry BRCA1/2 PVs (11,48). This enhanced response might also pertain to carriers of PVs in other genes and contribute to their lower cancer-specific mortality.
Our study has limitations. The relatively few deaths and few PVs in each gene limited statistical power to analyze the association of cancer-specific mortality with specific genes; larger subsequent analyses with longer follow-up may achieve this. Although there were many patients from most racial and ethnic groups, there were fewer Native American and Alaskan Natives. Additionally, we lack information on specific chemotherapy agents received. However, because breast cancer trials such as INFORM and GeparSixto found that BRCA1/2 PV carriers responded well regardless of specific drugs used (33,34), this limitation seems unlikely to affect our conclusions. Although we lack information on PARP inhibitor use, the study period overlaps with their Food and Drug Administration approval, and thus their impact was probably limited, especially for breast cancer (37,38). Results for patients who underwent clinical genetic testing, potentially because of family cancer history, may not be generalizable to patients who did not; however, we found that a sensitivity analysis accounting for selection into testing offered no evidence of higher cancer-specific mortality, and additional evidence of lower cancer-specific mortality, among PV carriers. We lack data on other prognostic factors including prediagnostic screening, comorbidities, extent of surgical debulking, and metastatic recurrence. As noted previously, the median follow-up time of 41 months is short, yet it encompasses a period that matters to patients as they plan for their immediate future. The study’s limitations are balanced by considerable strengths, including a large, diverse, contemporary population-based sample; genetic results obtained directly from testing laboratories; and uniform ascertainment of treatment and mortality data by SEER registries that have near-total capture of all cancers statewide, minimizing selection bias.
This study’s results have substantial implications for patients newly diagnosed with breast cancer or ovarian cancer. We found that no PV, whether in BRCA1/2 or another gene, was associated with any increase in short-term cancer-specific or overall mortality among patients treated with chemotherapy. This may help reassure cancer patients that testing positive for a PV does not mean they are more likely to die within the first several years following their cancer diagnosis.
Funding
Research reported in this publication was supported by the National Cancer Institute (NCI) of the National Institutes of Health under award numbers P01 CA163233 to the University of Michigan and R01 CA225697 to Stanford University. The collection of cancer incidence data in Georgia was supported by contract HHSN261201800003I, Task Order HHSN26100001 from the NCI and cooperative agreement 5NU58DP006352-03-00 from the Centers for Disease Control and Prevention (CDC). The collection of cancer incidence data used in this study was supported by the California Department of Public Health pursuant to California Health and Safety Code Section 103885; CDC’s National Program of Cancer Registries, under cooperative agreement 5NU58DP006344; the NCI’s Surveillance, Epidemiology, and End Results Program under contract HHSN261201800032I awarded to the University of California, San Francisco; contract HHSN261201800015I awarded to the University of Southern California; and contract HHSN261201800009I awarded to the Public Health Institute, Cancer Registry of Greater California.
Notes
Role of the funders: The funders had no role in the design of the study; the collection, analysis, and interpretation of the data; the writing of the manuscript; and the decision to submit the manuscript for publication.
Disclosures: Research funding to her institution for an unrelated project was provided to Allison Kurian, MD, MSc, by Myriad Genetics. The authors have no conflicts of interest to declare.
Author contributions: Conceptualization: AK, SK, KW; Formal analysis: PA, IB, TH; Data curation: PA; Resources: KW, AH, SG, DD; Writing—original draft: AK, SK, KW; Writing—review and editing: AK, PA, IB, AH, DD, SG, MM, JB, TH, SK, KW; Funding acquisition: AK, SK
Acknowledgements: We thank Lynne S. Penberthy, MD, and Valentina I. Petkov, MD, at the National Cancer Institute; Nicola Schussler at Information Management Services; and colleagues at Ambry Genetics, Bioreference/GeneDx, Invitae, and Myriad Genetics for their collaboration on the genetic test data linkage to Surveillance, Epidemiology, and End Results data. Written permission was obtained to include the names of all acknowledged individuals.
Disclaimer: The ideas and opinions expressed herein are those of the authors and do not necessarily reflect the opinions of the State of California, Department of Public Health, the National Cancer Institute, and the Centers for Disease Control and Prevention or their Contractors and Subcontractors.
Prior presentations: A preliminary version of this work was presented in partial form at the American Society of Clinical Oncology Annual Meeting, June 2021.
Data Availability
The data underlying this article cannot be shared publicly at this time due to agreements with participating testing laboratories.
Supplementary Material
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Supplementary Materials
Data Availability Statement
The data underlying this article cannot be shared publicly at this time due to agreements with participating testing laboratories.