Search

The early months of the COVID-19 pandemic led to reduced cancer screenings and delayed cancer surgeries. We used insurance claims data to understand how breast cancer incidence and treatment after diagnosis changed nationwide over the course of the pandemic.Using the Optum Research Database from January 2017 to March 2021, including approximately 19 million US adults with commercial health insurance, we identified new breast cancer diagnoses and first treatment after diagnosis. We compared breast cancer incidence and proportion of newly diagnosed patients receiving pre-operative systemic therapy pre-COVID, in the first 2 months of the COVID pandemic and in the later part of the COVID pandemic.Average monthly breast cancer incidence was 19.3 (95% CI 19.1-19.5) cases per 100,000 women and men pre-COVID, 11.6 (95% CI 10.8-12.4) per 100,000 in April-May 2020, and 19.7 (95% CI 19.3-20.1) per 100,000 in June 2020-February 2021. Use of pre-operative systemic therapy was 12.0% (11.7-12.4) pre-COVID, 37.7% (34.9-40.7) for patients diagnosed March-April 2020, and 14.8% (14.0-15.7) for patients diagnosed May 2020-January 2021. The changes in breast cancer incidence across the pandemic did not vary by demographic factors. Use of pre-operative systemic therapy across the pandemic varied by geographic region, but not by area socioeconomic deprivation or race/ethnicity.In this US-insured population, the dramatic changes in breast cancer incidence and the use of pre-operative systemic therapy experienced in the first 2 months of the pandemic did not persist, although a modest change in the initial management of breast cancer continued.

Window of opportunity trials (WOT) are increasingly common in oncology research. In WOT participants receive a drug between diagnosis and anti-cancer treatment, usually for the purpose of investigating that drugs effect on cancer biology. This qualitative study aimed to understand patient perspectives on WOT.We recruited adults diagnosed with early-stage breast cancer awaiting definitive therapy at a single-academic medical center to participate in semi-structured interviews. Thematic and content analyses were performed to identify attitudes and factors that would influence decisions about WOT participation.We interviewed 25 women diagnosed with early-stage breast cancer. The most common positive attitudes toward trial participation were a desire to contribute to research and a hope for personal benefit, while the most common concerns were the potential for side effects and how they might impact fitness for planned treatment. Participants indicated family would be an important normative factor in decision-making and, during the COVID-19 pandemic, deemed the absence of family members during clinic visits a barrier to enrollment. Factors that could hinder participation included delay in standard treatment and the requirement for additional visits or procedures. Ultimately, most interviewees stated they would participate in a WOT if offered (N = 17/25).In this qualitative study, interviewees weighed altruism and hypothetical personal benefit against the possibility of side effect from a WOT. In-person family presence during trial discussion, challenging during COVID-19, was important for many. Our results may inform trial design and communication approaches in future window of opportunity efforts.

Background: The COVID-19 pandemic imposed great burden on the healthcare system and has required patients and their physicians to make unprecedented choices about cancer care. Hospital-based retrospective reviews have suggested changes in breast cancer management during 2020 compared to previous years, including greater use of preoperative therapy. We used insurance claims data to understand the impact of the pandemic on breast cancer diagnosis and treatment at a national level. Methods: We identified new diagnoses of breast cancer from 2017-2020 in the Optum Clinformatics claims data set, consisting of claims records linked to electronic health records. The overall population (enrolled in Optum for at least 6 months with at least one diagnosis of any condition and no prior breast cancer diagnosis) included an average of 8 million adult Americans per year. A new breast cancer diagnosis was defined as a first-ever ICD code for breast cancer with a breast diagnostic biopsy procedure code (considered the cancer diagnosis date) within 6 months before to 3 months after that ICD code. Each year’s cohort of breast cancer cases was limited to those diagnosed between February 1 and May 30, with follow-up through June 30 of the diagnosis year. First treatment after diagnosis was classified as either endocrine therapy, chemotherapy, or surgery. Geographic area was defined by the 9 Census Bureau regions. We used a Poisson regression to compare the rate of breast cancer diagnosis in 2020 compared to 2017-2019 and a Chi-squared test to compare the distribution of first treatment in 2020 compared to 2017-2019. To investigate differences in the impact of the pandemic on rate of diagnosis (Poisson regression) or use of preoperative therapy (logistic regression) by race/ethnicity, income, or geographic area, we included each of these covariates as well as its interaction with year (2020 vs 2017-2019) in separate models. Results: There were 2,841 breast cancer diagnoses February-May 2020 (0.037% of overall population), compared to 3,880 in 2019 (0.045%), 3,509 in 2018 (0.043%), and 2,999 in 2017 (0.041%). In 2020 compared to 2017-2019, new breast cancer diagnoses decreased by 12.3% (95% CI 8.6%-15.9%; p < 0.0001). No significant differences were observed in this reduction in diagnoses by race/ethnicity, income level, or geographic area. Median date of diagnosis was earlier in 2020 (March 11) compared to 2017-2019 (March 29, April 1, and April 1 respectively), a result of a larger drop in diagnoses later in the time interval in 2020. Among patients who received treatment during follow-up (83.1% in 2017-2019 vs 86.2% in 2020, a difference likely reflecting this shift in diagnosis date), there was a marked reduction in surgery as first treatment in 2020 compared to previous years (88.7% in 2017-2019 vs 69.3% in 2020), while both preoperative chemotherapy (6.1% in 2017-2019 vs 10.7% in 2020) and preoperative endocrine therapy (5.2% in 2017-2019 vs 20.1% in 2020) increased (p < 0.0001). There were no differences in the shift toward preoperative therapy by race/ethnicity or income, but there was a significant difference by geographic area (p=0.0003): the Mountain region had least change in use of preoperative therapy (odds ratio 2.46 [95% CI 1.75-3.47] of preoperative therapy during vs before the pandmic) while the Middle Atlantic region had the greatest (odds ratio 5.64 [95% CI 3.79-8.38]). Conclusions: Among insured U.S. patients, new breast cancer diagnoses decreased by 12.3% during February-May 2020 compared to the same period in the previous three years, and use of preoperative therapy, largely endocrine, increased by 2.7-fold. The impact of the pandemic on choice of first treatment differed by geographic area, but not by race/ethnicity or income in this insured population. We will monitor with continued follow-up of claims data to assess the longer-term impact of these pandemic-related changes on treatment patterns, cost, and patient outcomes. Citation Format: Jennifer L Caswell-Jin, Maryam N Shafaee, Lan Xiao, Mina Liu, Natasha Purington, Esther M John, Melissa L Bondy, Allison W Kurian. National claims data analysis of breast cancer diagnosis and treatment before versus during the COVID-19 pandemic [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P5-14-01.

Neuroendocrine carcinomas (NEC) of the breast are exceedingly rare tumors, which are classified in the WHO system as small cell (SCNEC) and large cell (LCNEC) carcinoma based on indistinguishable features from their lung counterparts. In contrast to lung and enteropancreatic NEC, the genomics of breast NEC have not been well-characterized. In this study, we examined the clinicopathologic, immunohistochemical, and genetic features of 13 breast NEC (7 SCNEC, 4 LCNEC, 2 NEC with ambiguous small versus large cell morphology [ANEC]). Co-alterations of TP53 and RB1 were identified in 86% (6/7) SCNEC, 100% (2/2) ANEC, and 50% (2/4) LCNEC. The one SCNEC without TP53/RB1 alteration had other p53 pathway aberrations (MDM2 and MDM4 amplification) and was immunohistochemically RB negative. PIK3CA/PTEN pathway alterations and ZNF703 amplifications were each identified in 46% (6/13) NEC. Two tumors (1 SCNEC, 1 LCNEC) were CDH1 mutated. By immunohistochemistry, 100% SCNEC (6/6) and ANEC (2/2) and 50% (2/4) LCNEC (83% NEC) showed RB loss, compared to 0% (0/8) grade 3 neuroendocrine tumors (NET) (p

BACKGROUND The IBIS/Tyrer-Cuzick model is used clinically to guide breast cancer screening and prevention, but was developed primarily in non-Hispanic White women. Little is known about its long-term performance in a racially/ethnically diverse population. METHODS The Women's Health Initiative study enrolled postmenopausal women from 1993-1998. Women were included who were aged

Addition of HER2-targeted agents to neoadjuvant chemotherapy has dramatically improved pathological complete response (pCR) rates in early stage Human epidermal growth factor receptor 2 (HER2)-positive breast cancer. Still, up to 50% of patients have residual disease following treatment and biomarkers predictive of response are urgently needed. We performed spatial proteomic characterization using NanoString GeoMX Digital Spatial Profiling (DSP) of 122 samples from 57 HER2-positive breast tumors from the neoadjuvant TRIO-US B07 clinical trial (discovery cohort, n=28; validation cohort, n=29) sampled pre-treatment, after 14-21 days of HER2-targeted therapy and at surgery. In situ quantification of 40 tumor and immune proteins across multiple pancytokeratin-enriched regions per sample revealed that treatment results in decreased HER2 signaling and increased immune infiltration after several weeks of therapy. These changes were more dramatic in tumors that ultimately undergo pCR, and a classifier trained to predict pCR using on-treatment and pre-treatment DSP protein levels had a cross-validation mean Area Under the Receiver Operating Characteristics (AUROC) of 0.733 in the discovery cohort and validated in an independent cohort (AUROC = 0.725). Thus, we demonstrate robust stratification of sensitive tumors early during neoadjuvant HER2-targeted therapy using a multiplex spatial proteomic biomarker with implications for tailoring subsequent therapy.

Breast cancer comprises a heterogeneous group of tumor subtypes, whether defined by immunohistochemistry of key proteins, RNA expression profiles, or genetic alterations, and each of these subtypes may benefit from a distinct treatment approach. However, there can be striking heterogeneity within tumors, which may pose challenges to the development of personalized approaches to therapy. Intratumor heterogeneity can be divided into three main categories: genetic, phenotypic, and microenvironmental. Here, we review technologies to interrogate these three categories of heterogeneity in patient samples, as well as the current state of understanding of these categories in breast cancer, from cell to cell, across different regions of the same tumor mass, across treatment, and across metastasis. Efforts to characterize tumor heterogeneity longitudinally will be crucial to the development of personalized oncology for breast cancer.

Background: Neoadjuvant HER2-targeted therapy in combination with chemotherapy is a standard treatment approach for early-stage HER2-positive breast cancer. Proposed biomarkers to predict pathologic complete response (pCR), and thereby inform which patients may benefit from de-escalation of therapy, include expression-based subtyping and immune enrichment scores. Little is known about how tumors and their microenvironment may change with HER2-targeted therapy alone, and whether these changes may predict outcome. Methods: The TRIO-US B07 phase II trial randomized 128 participants with stage I-III HER2-positive breast cancer to trastuzumab (N=34), lapatinib (N=36), or the combination (N=58) for three weeks, followed by six cycles of docetaxel and carboplatin with continued HER2-targeted therapy. Fresh-frozen core biopsies of the tumor prior to therapy (N=110) and after 14-21 days of HER2-targeted therapy alone (N=89) were collected, and RNA was extracted and subjected to Agilent Whole Human Genome 44K 2-color chip. The pre-treatment tumor RNA was normalized against a mixed breast tumor reference, and the on-treatment tumor RNA against the matched pre-treatment sample. Absolute intrinsic molecular subtyping was used to determine intrinsic subtype, the iC10 expression-based classifier to determine integrative subtype, gene set enrichment analysis (GSEA) to assess signature changes across treatment, single-sample GSEA to compare individual gene signature scores between tumors, and CIBERSORT to quantify immune cell populations before and on treatment. Results: Primary trial results have been reported previously and showed a pCR rate of 47% with trastuzumab, 25% with lapatinib, and 52% with the combination. Prior to treatment, 56% of tumors classified as the HER2-enriched intrinsic subtype and 78% as the iC5 integrative subtype. HER2-enriched tumors trended toward a higher rate of pCR relative to other intrinsic subtypes (50% vs 33%, P=0.12), as did iC5 tumors relative to other integrative subtypes (48% vs 25%, P=0.08). However, in multivariate analysis, HER2 FISH ratio (P=0.04) and hormone receptor status (P=0.02), each associated themselves with intrinsic and integrative subtype, proved the most valuable in predicting pCR, with little information added by expression-based subtyping. Immune cell signatures correlated with pCR in the trastuzumab-containing arms only. Of 65 gene signatures tested, 47 changed across HER2-targeted therapy with false discovery rate < 0.1, driven by decreasing tumor proliferation, increasing immune cell signatures, and increasing stromal cell/epithelial mesenchymal transition signatures. Quantification of immune cell populations suggested the immune changes were both anti-tumor (CD8+ T cells) and pro-tumor (M2 macrophages). Intrinsic subtype changed in 54% of tumors (79% of these converting to normal-like) and integrative subtype changed in 26%. Change in subtype, proliferation, or immune infiltration with targeted therapy did not correlate with pCR. A higher proportion of tumors treated with trastuzumab alone maintained their proliferation (42%), compared with lapatinib alone (20%; P=0.16) or the combination (16%; P=0.04). Conclusions: In the TRIO-US B07 study, the biomarkers most predictive of response to neoadjuvant HER2-targeted therapy were hormone receptor status in combination with HER2 FISH ratio. Multiple changes in the tumor and its microenvironment occurred with HER2-targeted therapy, but these changes did not predict pCR. Tumors treated with lapatinib tended to decrease proliferation more than tumors treated with trastuzumab, despite trastuzumab being more effective in preventing recurrence, an observation with implications for window of opportunity studies. Citation Format: Jennifer L. Caswell-Jin, Katherine L. McNamara, Judy Dering, Hsiao-Wang Chen, Robert Dichmann, Alejandra Perez, Ravindranath Patel, Eran Kotler, Jason J. Zoeller, Joan S. Brugge, Michael F. Press, Dennis J. Slamon, Christina Curtis, Sara A. Hurvitz. Tumor expression and microenvironment in HER2-positive breast cancer before and on HER2-targeted therapy: Analysis of microarray expression data from the TRIO-US B07 trial [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-07-01.

Background: While introduction of HER2-targeted therapies has dramatically improved outcomes for patients with HER2-positive disease, even with the addition of HER2-targeted agents, 40-50% of patients do not achieve a pCR (pathologic complete response) following neoadjuvant therapy implying that clinical or molecular differences may be present in responders versus non-responders. While recent bulk expression studies have identified several biomarkers associated with response to HER2-targeted therapies in the neoadjuvant setting, these studies are limited in their ability to assign observed changes to specific geographic or phenotypic cell populations, such as the malignant tumor core or the surrounding microenvironment. Methods: Here we used the Digital Spatial Profiler (DSP, NanoString Technologies, Inc.) to profile regions-of-interest containing pancytokeratin (panCK)+ tumor cells and infiltrated immune cells that are co-localized with the tumor cells. Using this technology, we assayed archival tissue from 28 patients with HER2-positive breast cancer from the TRIO-B07 (NCT00769470) clinical trial, who were treated with trastuzumab, lapatinib, or both, followed by standard chemotherapy plus HER2-targeted therapy. Tissue specimens were collected from the pre-treatment diagnostic biopsy (Baseline) and after one cycle of targeted therapy (Runin). To study regional heterogeneity, we selected an average of four panCK-enriched tissue regions from each sample. Using DSP, we performed multiplexed quantification of 38 tumor and immune protein markers and 96 RNA markers on the selected tissue regions and compared our findings to bulk mRNA expression data from the same cohort. Results: Within the panCK-enriched regions, DSP revealed significant treatment-associated decreases in HER2 protein levels and the downstream PI3K-Akt signaling pathway in Runin compared to Baseline samples. In tandem, we observed a significant increase in infiltrating leukocytes, with CD45, a pan-leukocyte marker, and CD8, a marker for T cells that mediate tumor cell killing, showing the most dramatic changes. These changes in Runin compared to Baseline were more significant in the subset of cases that achieved a pCR versus those that do not, independent of ER status. Comparison of Runin samples to matched Baseline samples from the same patient enabled improved prediction of patient outcome (pCR) compared with analysis of a single timepoint alone. We also found that the DSP panCK enrichment strategy captures additional signal not observed in bulk expression data. For instance, using bulk expression, a decrease in HER2 RNA levels between Baseline and Runin was evident but there was no difference in the degree of decrease in HER2 mRNA between pCR and no pCR cases. Using DSP, we observed that the significant decrease in HER2 levels at Runin is more pronounced in cases that achieved a pCR. Across both tumor and immune markers, regional heterogeneity increased at Runin compared to Baseline. Conclusions: In this study, we used DSP and a panCK enrichment strategy to retrospectively delineate the changes that occurred in tumor cells and co-localized immune cells during HER2-targeted therapy. In comparison to traditional or multiplexed IHC, DSP allows for simultaneous profiling of a large number of markers, enabling the characterization of multiple cancer signaling pathways and immune markers on a single tissue specimen. This study demonstrates the utility of pancytokeratin-enriched spatial proteomic profiling to characterize treatment-associated changes and identify predictive biomarkers. NanoString’s Digital Spatial Profiler is for Research Use Only. Not to be used for diagnostic procedures. Citation Format: Katherine Lee McNamara, Jennifer L. Caswell-Jin, Zhicheng Ma, Jason J. Zoeller, Michelle Kriner, Zoey Zhou, Jason Reeves, Margaret Hoang, Joseph Beechem, Dennis J. Slamon, Michael F. Press, Joan Brugge, Sara A. Hurvitz, Christina Curtis. Characterizing the tumor and immune microenvironment through treatment to predict response to neoadjuvant HER2-targeted therapy using the Digital Spatial Profiler [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-10-12.

Objectives Most population-based cancer databases lack information on metastatic recurrence. Electronic medical records (EMR) and cancer registries contain complementary information on cancer diagnosis, treatment and outcome, yet are rarely used synergistically. To construct a cohort of metastatic breast cancer (MBC) patients, we applied natural language processing techniques within a semisupervised machine learning framework to linked EMR-California Cancer Registry (CCR) data. Materials and Methods We studied all female patients treated at Stanford Health Care with an incident breast cancer diagnosis from 2000 to 2014. Our database consisted of structured fields and unstructured free-text clinical notes from EMR, linked to CCR, a component of the Surveillance, Epidemiology and End Results Program (SEER). We identified de novo MBC patients from CCR and extracted information on distant recurrences from patient notes in EMR. Furthermore, we trained a regularized logistic regression model for recurrent MBC classification and evaluated its performance on a gold standard set of 146 patients. Results There were 11 459 breast cancer patients in total and the median follow-up time was 96.3 months. We identified 1886 MBC patients, 512 (27.1%) of whom were de novo MBC patients and 1374 (72.9%) were recurrent MBC patients. Our final MBC classifier achieved an area under the receiver operating characteristic curve (AUC) of 0.917, with sensitivity 0.861, specificity 0.878, and accuracy 0.870. Discussion and Conclusion To enable population-based research on MBC, we developed a framework for retrospective case detection combining EMR and CCR data. Our classifier achieved good AUC, sensitivity, and specificity without expert-labeled examples.

PURPOSE Treatment and monitoring options for patients with metastatic breast cancer (MBC) are increasing, but little is known about variability in care. We sought to improve understanding of MBC care and its correlates by analyzing real-world claims data using a search engine with a novel query language to enable temporal electronic phenotyping. METHODS Using the Advanced Cohort Engine, we identified 6,180 women who met criteria for having estrogen receptor–positive, human epidermal growth factor receptor 2–negative MBC from IBM MarketScan US insurance claims (2007-2014). We characterized treatment, monitoring, and hospice usage, along with clinical and nonclinical factors affecting care. RESULTS We observed wide variability in treatment modality and monitoring across patients and geography. Most women received first-recorded therapy with endocrine (67%) versus chemotherapy, underwent more computed tomography (CT) (76%) than positron emission tomography-CT, and were monitored using tumor markers (58%). Nearly half (46%) met criteria for aggressive disease, which were associated with receiving chemotherapy first, monitoring primarily with CT, and more frequent imaging. Older age was associated with endocrine therapy first, less frequent imaging, and less use of tumor markers. After controlling for clinical factors, care strategies varied significantly by nonclinical factors (median regional income with first-recorded therapy and imaging type, geographic region with these and with imaging frequency and use of tumor markers; P < .0001). CONCLUSION Variability in US MBC care is explained by patient and disease factors and by nonclinical factors such as geographic region, suggesting that treatment decisions are influenced by local practice patterns and/or resources. A search engine designed to express complex electronic phenotypes from longitudinal patient records enables the identification of variability in patient care, helping to define disparities and areas for improvement.

In this multicenter, open-label, randomized phase II investigator-sponsored neoadjuvant trial with funding provided by Sanofi and GlaxoSmithKline (TRIO-US B07, Clinical Trials NCT00769470), participants with early-stage HER2-positive breast cancer (N = 128) were recruited from 13 United States oncology centers throughout the Translational Research in Oncology network. Participants were randomized to receive trastuzumab (T; N = 34), lapatinib (L; N = 36), or both (TL; N = 58) as HER2-targeted therapy, with each participant given one cycle of this designated anti-HER2 therapy alone followed by six cycles of standard combination chemotherapy with the same anti-HER2 therapy. The primary objective was to estimate the rate of pathologic complete response (pCR) at the time of surgery in each of the three arms. In the intent-to-treat population, we observed similar pCR rates between T (47%, 95% confidence interval [CI] 30–65%) and TL (52%, 95% CI 38–65%), and a lower pCR rate with L (25%, 95% CI 13–43%). In the T arm, 100% of participants completed all protocol-specified treatment prior to surgery, as compared to 69% in the L arm and 74% in the TL arm. Tumor or tumor bed tissue was collected whenever possible pre-treatment (N = 110), after one cycle of HER2-targeted therapy alone (N = 89), and at time of surgery (N = 59). Higher-level amplification of HER2 and hormone receptor (HR)-negative status were associated with a higher pCR rate. Large shifts in the tumor, immune, and stromal gene expression occurred after one cycle of HER2-targeted therapy. In contrast to pCR rates, the L-containing arms exhibited greater proliferation reduction than T at this timepoint. Immune expression signatures increased in all arms after one cycle of HER2-targeted therapy, decreasing again by the time of surgery. Our results inform approaches to early assessment of sensitivity to anti-HER2 therapy and shed light on the role of the immune microenvironment in response to HER2-targeted agents., HER2+ breast cancer patients can often develop resistance to trastuzumab and therefore potential combination therapies need to be explored. Here, the authors report the results of a multi-center randomized phase II clinical trial evaluating the pathological and molecular responses associated with trastuzumab and/or lapatinib in combination with chemotherapy in HER2+ breast cancer patients.

Background: Recent studies have demonstrated that women with early stage ER-positive (ER+) and HER2-negative (HER2-) breast cancer have a persistent risk of recurrence and cancer related death up to 20 years post diagnosis, highlighting the chronic nature of ER+ breast cancer and critical need to identify tumor characteristics that are more predictive of risk of recurrence than standard clinical covariates. However, progress in delineating the dynamics of breast cancer relapse and biomarkers of late recurrence has been hindered by the lack of large cohorts with long-term clinical follow-up and molecular information. Methods: We report the results of a cohort of 3,240 breast cancer patients from the United Kingdom and Canada with 20 years of follow-up (median 9.75 years), including 1,980 with accompanying molecular data from the primary breast tumor. Information for each patient on loco-regional recurrence (LR), distant recurrence (DR), and site(s) of metastases was collected. We developed a non-homogenous Markov chain model that accounted for different clinical endpoints and timescales, as well as competing risks of mortality and the distinct baseline hazards that characterize different molecular subgroups. This approach enabled robust analysis of the spatio-temporal dynamics of breast cancer recurrence across the clinical subgroups, PAM50 subgroups and the integrative clusters, while also enabling individual risk of relapse predictions. Results: We employed our multistate model to compute the probability of experiencing a LR or DR, as well as the baseline transition probabilities from surgery, LR or DR at various time intervals for average individuals in each of the clinical/molecular subgroups. These analyses reveal four late-recurring ER+ (predominantly HER2-) subgroups, together accounting for 26% of all ER+ tumors, with high (median 42-55%) risk of recurrence up to 20 years post-diagnosis. Each of these four subgroups maps to one of the Integrative Clusters, defined based on genomic copy number alterations and gene expression, and is enriched for a characteristic copy number amplification events: 11q13 (CCND1, RSF1), 8p12 (FGFR1, ZNF703), 17q23 (RPS6KB1) and 8q24 (MYC). These four molecular subgroups are superior in predicting late DR than standard clinical variables. Conclusions: A detailed understanding of the rates and routes of metastasis and their variability across the distinct molecular subtypes is essential for devising personalized approaches to breast cancer care. We describe a molecularly characterized breast cancer cohort with long-term clinical follow-up and a statistical modeling framework, enabling delineation of the dynamics of breast cancer recurrence at unprecedented resolution. These analyses reveal four late recurring ER+ subgroups and accompanying biomarkers that collectively define the quarter of ER+ cases at highest risk of recurrence. Our findings highlight opportunities for improved patient stratification and biomarker-driven clinical trials directed at the subset of breast cancer patients with persistent risk of recurrence. Citation Format: Curtis C, Rueda OM, Sammut S-J, Chin S-F, Caswell-Jin JL, Seoane JA, Callari M, Batra R, Pereira B, Bruna A, Ali HR, Provenzano E, Liu B, Parisien M, Gillett C, McKinney S, Green A, Murphy L, Purushotham A, Ellis I, Pharoah P, Rueda C, Aparicio S, Caldas C. Dynamics of breast cancer relapse reveal molecularly defined late recurring ER-positive subgroups: Results from the METABRIC study [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr GS3-06.

Background: Understanding to what extent a breast tumor's genetic composition may change over the course of a few months of neoadjuvant therapy has implications for optimal therapeutic approach. However, genomic changes observed across treatment may result from either treatment-induced clonal evolution or geographically disparate sampling of a heterogeneous tumor. We sought to characterize the geographic heterogeneity in primary breast tumors, and to incorporate this information into analysis of clonal evolution with neoadjuvant therapy. Methods: We assembled the largest cohort to date of multi-region (n=2-3) whole-exome sequenced (WES) or whole-genome sequenced untreated primary breast tumors with matched normal and adequate tumor purity for analysis: four tumors with data generated for this study and five tumors compiled from three previous studies. We also generated the first cohort of multi-region (n=2-6) WES breast tumors post-neoadjuvant HER2-targeted therapy and chemotherapy, sequencing one region from a pre-treatment diagnostic specimen, multiple regions from the post-treatment surgical specimen, and matched normal for five HER2+ breast tumors that did not achieve a pathologic complete response. We used an agent-based model of spatial tumor growth to investigate whether the mutational patterns we observed with treatment were consistent with pre-existing heterogeneity or treatment-induced selection. Results: In untreated primary breast tumors, on average 30% (range 1-70%) of apparently clonal mutations from a single region were absent or rare in a second, spatially disparate region (high-frequency regional, or HFR). Intra-tumor heterogeneity was similar post-treatment (HFR 28%, range 10-54%), and was higher in breast tumors than in previously analyzed colon, brain, lung, and esophageal tumors. Simulation studies confirmed that with high heterogeneity as observed in breast tumors, analysis of one pre-treatment and one post-treatment region could not distinguish treatment-induced clonal evolution from pre-existing heterogeneity; however, obtaining at least two post-treatment regions allowed for detection of clonal shifts with treatment. Analysis of multi-region data revealed that clonal replacement occurred with neoadjuvant therapy in two of the five tumors. Candidate causes of therapeutic resistance included amplifications in CCND1, ERBB4, and MYC in one subclone, and functional protein-altering mutations in ERCC2, SMO, and WT1 in another. Mathematical modeling suggested that these putative resistant subclones comprised 0.02-12.5% of the overall pre-treatment cell population, substantially larger than previous estimates of resistant tumor clone size. Conclusions: WES data from multiple regions of untreated and treated primary breast tumors revealed considerable heterogeneity that remained present throughout treatment with chemotherapy and HER2-targeted therapy, even while major clonal sweeps took place in a minority of tumors. Obtaining at least two samples for analysis from breast tumors post-neoadjuvant therapy may reveal the tumor's evolutionary path and, especially as increasing numbers of molecular and immune therapeutic targets are identified, inform new clinical strategies. Citation Format: Caswell-Jin JL, McNamara K, Reiter JG, Sun R, Hu Z, Ma Z, Suarez CJ, Tilk S, Raghavendra A, Forte V, Chin S-F, Bardwell H, Provenzano E, Caldas C, Lang J, West R, Tripathy D, Press MF, Curtis C. Clonal evolution and heterogeneity in breast tumors treated with neoadjuvant HER2-targeted therapy [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-06-01.

Purpose As genetic testing expands, patients are increasingly found to carry pathogenic variants in cancer susceptibility genes that are less familiar to most clinicians, specifically genes other than those causing hereditary breast ovarian cancer syndrome ( BRCA1 and BRCA2) and Lynch syndrome. Little is known about the subsequent behaviors of such patients in terms of managing cancer risks and informing relatives. Methods All adult patients who were counseled and tested at the Stanford Cancer Genetics Clinic from January 2013 to July 2015 and had a pathogenic variant in a non- BRCA1/2, non–Lynch syndrome gene were invited to participate in a telephone interview about adherence to risk-reducing recommendations, genetic testing by relatives, and new cancer incidence. Results Fifty-seven (40%) of 142 eligible patients were successfully contacted, and all 57 patients participated; median follow-up was 677 days (range, 247 to 1,401 days). Most patients (82%; 95% CI, 70% to 90%) recalled that a risk-reducing intervention (screening, medication, or surgery) was recommended, and most patients (85%; 95% CI, 72% to 93%) adhered to the recommendation. Nearly all patients (91%; 95% CI, 81% to 97%) shared results with relatives, and most patients (78%; 95% CI, 64% to 88%) reported that a relative was subsequently tested. During the follow-up period, 9% of patients (95% CI, 3% to 19%) developed second cancers, and in 14% of patients (95% CI, 7% to 26%), a first-degree relative developed cancer, some of which were detected by recommended screening. Conclusion Patients with a pathogenic variant in a less familiar cancer susceptibility gene report high adherence to risk-reducing interventions. Furthermore, in the 57 carriers and subsequently tested relatives with two years of follow-up, a total of three cancers (one in a proband and two in relatives) were detected through interventions recommended on the basis of the pathogenic variant. These results suggest a potential benefit of genetic counseling and testing for pathogenic variants in less familiar genes.

In cascade testing, genetic testing for an identified familial pathogenic variant extends to disease-free relatives to allow genetically targeted disease prevention. We evaluated the results of an online initiative in which carriers of 1 of 30 cancer-associated genes, or their first-degree relatives, could offer low-cost testing to at-risk first-degree relatives. In the first year, 1101 applicants invited 2280 first-degree relatives to undergo genetic testing. Of invited relatives, 47.5% (95% confidence interval [CI] = 45.5 to 49.6%) underwent genetic testing, and 12.0% (95% CI = 9.2 to 14.8%) who tested positive continued the cascade by inviting additional relatives to test. Of tested relatives, 4.9% (95% CI = 3.8 to 6.1%) had a pathogenic variant in a different gene from the known familial one, and 16.8% (95% CI = 14.7 to 18.8%) had a variant of uncertain significance. These results suggest that an online, low-cost program is an effective approach to implementing cascade testing, and that up to 5% of the general population may carry a pathogenic variant in 1 of 30 cancer-associated genes.

Models of estrogen receptor–positive breast cancer are used to explore the rationale for androgen receptor agonists as treatments for breast cancer.

This study uses Women’s Health Initiative data to compare the prevalence of pathogenic variants (PVs) in breast cancer susceptibility genes in postmenopausal women with vs without breast cancer to guide decisions about who should undergo PV testing.

PURPOSE Electronic medical records (EMRs) and population-based cancer registries contain information on cancer outcomes and treatment, yet rarely capture information on the timing of metastatic cancer recurrence, which is essential to understand cancer survival outcomes. We developed a natural language processing (NLP) system to identify patient-specific timelines of metastatic breast cancer recurrence. PATIENTS AND METHODS We used the OncoSHARE database, which includes merged data from the California Cancer Registry and EMRs of 8,956 women diagnosed with breast cancer in 2000 to 2018. We curated a comprehensive vocabulary by interviewing expert clinicians and processing radiology and pathology reports and progress notes. We developed and evaluated the following two distinct NLP approaches to analyze free-text notes: a traditional rule-based model, using rules for metastatic detection from the literature and curated by domain experts; and a contemporary neural network model. For each 3-month period (quarter) from 2000 to 2018, we applied both models to infer recurrence status for that quarter. We trained the NLP models using 894 randomly selected patient records that were manually reviewed by clinical experts and evaluated model performance using 179 hold-out patients (20%) as a test set. RESULTS The median follow-up time was 19 quarters (5 years) for the training set and 15 quarters (4 years) for the test set. The neural network model predicted the timing of distant metastatic recurrence with a sensitivity of 0.83 and specificity of 0.73, outperforming the rule-based model, which had a specificity of 0.35 and sensitivity of 0.88 ( P < .001). CONCLUSION We developed an NLP method that enables identification of the occurrence and timing of metastatic breast cancer recurrence from EMRs. This approach may be adaptable to other cancer sites and could help to unlock the potential of EMRs for research on real-world cancer outcomes.

10531 Background: Genomic assays, such as RS, are used to determine chemotherapy benefit in early-stage, estrogen receptor (ER)- and/or progesterone receptor (PR)-positive, HER2 negative BC patients (pts). Currently, guidelines to use pts’ germline genetic testing results to guide adjuvant therapy are lacking. Several reports have indicated worse outcomes for BC pts with g CHEK2 pathogenic variants (PV). We investigated whether PV in CHEK2 were associated with increased RS. Methods: Patient-level clinical data and RS were derived from electronic medical records of seven medical centers between years 2013-17. Confirmation of RS using the Genomic Health provider portal was performed. 38 pts with germline PV in CHEK2 (15 pts/39.5% with c.1100delC mutation) and RS score (cases) were matched with BC pts whose genetic testing did not identify PV (controls) using a 1:2 matching schema. Pts were matched based on age at diagnosis and lymph node (LN) status. LN negative pts were further matched based on T-stage. A multivariate random intercept linear mixed model of CHEK2 mutation status on RS was performed, adjusting for PR. A secondary ordinal univariate analysis was conducted that categorized RS into low, intermediate and high risk ( < 18, 18-30, and > 30, respectively). P-values were reported based on a null hypothesis of no effect against a two-sided alternative. Results: The median RS for cases was 19.5 (interquartile range [IQR]: 15 to 25) and the median RS for controls was 18 (IQR: 12 to 22). A greater proportion of cases were categorized as high risk (10.5%) compared to controls (5.6%), and a smaller proportion of cases were categorized as low risk (36.8%) compared to controls (49.3%). Cases had higher grade and increased proportion of PR-negative BC as compared with controls (grade 1: 12.1% of cases versus 32.4% of controls; PR-negative: 7.9% of cases versus 5.6% of controls). The variables used to match cases and controls (age, lymph node status, and T-stage) had similar summary statistics. The RS was 1.97-point higher in pts with g CHEK2 PV compared to controls, after adjusting for PR (95% confidence interval [CI]: 1.02-point lower to 4.96-point higher; p = 0.194). The secondary analysis of CHEK2 mutation status on an ordinal RS risk group yielded comparable results; on average, the odds of being high risk compared to the combined intermediate/low risk groups was 1.72 times higher in cases compared to controls (95% CI: 0.77 to 3.80; p = 0.181), but these differences were not significant. Conclusions: Our case-control study did not show a statistically higher RS for BC that develops in pts with g CHEK2 PV. Further studies are warranted to evaluate the association between type of CHEK2 PV (frameshift versus missense) and other modifying genetic variables and RS.

181 Background: In “window of opportunity” (WOO) clinical trials, people with newly diagnosed early-stage cancer are exposed to an experimental drug during the period of time between diagnosis and definitive anti-cancer treatment. These trials allow investigators to study drug efficacy in untreated disease, which can expedite drug development. However, for trial participants, the WOO approach requires them to decide about an altruistic clinical trial during an intense time immediately after cancer diagnosis. This qualitative study aimed to understand patient perspectives on WOO clinical trials. Methods: We recruited adults newly diagnosed with early-stage breast cancer who were awaiting definitive therapy at a single academic medical center. We developed an interview guide grounded in the theoretical framework, the Theory of Planned Behavior (TPB). TBP is a well-validated decision-making model with three domains that guide behavior: (1) attitudes (2) normative factors and (3) perceived difficulty of a behavior. We conducted one-on-one semi-structured interviews that were audio-recorded and transcribed. Transcripts were analyzed to ensure interrater reliability and content analysis was performed to assess themes that emerged. Results: We interviewed 15 women (age 32-72) with early-stage breast cancer, and the majority were White (n = 12, 80%) and at least college educated (n = 12, 80%). Key themes that emerged included favorable attitudes towards participating in a WOO trial that were altruistic, including the desire to contribute to science (n = 10, 67%) and to help future breast cancer patients (n = 5, 33%). Several individuals also identified a potential benefit to themselves (n = 10, 67%), including access to a targeted drug (n = 4, 27%) and adding meaning to their diagnosis (n = 3, 20%). However, most interviewees reported concerns about drug side effects (n = 12, 80%) and whether side effects would impact other planned treatments (n = 10, 67%). Interviewees also expressed family would be an important normative factor in decision-making (n = 8, 53%). A key theme that emerged as a difficulty was the potential delay in standard treatment (n = 14, 93%). Despite this concern, at the end of the interviews, most interviewees stated they would participate in a WOO trial if offered (n = 10, 67%). Conclusions: WOO trials are becoming increasingly common in oncology research. In this qualitative study, interviewees weighed altruism against the possibility delaying or impacting other treatments. Our results may inform trial design and communication approaches in future WOO efforts.

1503 Background: The TC model, a breast cancer (BC) risk assessment tool based on family cancer history, reproductive and lifestyle factors is used to guide BC screening and prevention. TC was developed and validated largely in non-Hispanic White (NHW) women. We evaluated the calibration and discrimination of TC version 7.02 among racially/ethnically diverse post-menopausal women enrolled in the Women’s Health Initiative (WHI) clinical trials or observational study. Methods: WHI enrolled post-menopausal women from 1993-1998 and followed them prospectively for BC incidence. We included women aged ≤80 years at enrollment with no prior BC or mastectomy and with data required for TC, including weight, height, ages at menarche, first birth and menopause, menopausal hormone therapy use and family history of breast or ovarian cancer in first or second-degree relatives. Calibration was assessed by the ratio of observed BC cases to the number expected by TC (O/E), with expected cases calculated as the sum of cumulative hazards. We tested for differential discrimination by race/ethnicity (NHW, African American, Hispanic, Asian/Pacific Islander, Native American, other) using Cox regression. Time to BC was modeled using age, race/ethnicity, TC estimate (transformed by log of relative lifetime risk), and a term for interaction between race/ethnicity and TC estimate. Results: During the follow-up period (median 18.9 years, maximum 23.4 years), 6,836 new BC cases were diagnosed among 91,893 women. TC was well-calibrated overall (O/E 0.95) in NHW and African Americans, but over-estimated risk for Hispanics (O/E 0.75, Table). Results suggested good calibration for Asian/Pacific Islanders and Native Americans, but sample sizes were small. Discrimination did not differ significantly by race/ethnicity (two-sided p-value for interaction = 0.33). Conclusions: TC provided similar risk discrimination among post-menopausal women of different racial/ethnic groups over nearly 20 years of follow-up; however, it overestimated risk for Hispanics. Future studies in diverse populations are warranted, with need for a more accurate breast cancer risk assessment tool for Hispanics. [Table: see text]

7063 Background: Treatment and monitoring options for metastatic breast cancer (MBC) are increasing, but little is known about patterns or predictors of their use. Insurance claims are potentially informative, but tracking patient care timelines using episodic claims data has been cumbersome. Methods: We used an advanced cohort engine, implementing a temporal query language, to link IBM Marketscan claims over time for > 125 million US individuals from 2007-2014. To select the most common MBC subtype (ER+, HER2-), our criteria were: ≥2 MBC ICD codes, ≥1 year of follow-up, and ≥1 CPT or medication code for endocrine therapy and 0 for HER2-targeted therapy. We defined aggressive MBC as ≥1 ICD code for visceral or central nervous system metastasis < 1 year after the first MBC code. Geographic area was defined by 9 Census-Bureau designated regions. End-of-record was used as a surrogate for death in a Cox regression. We used multivariate logistic regression to determine correlation of factors, including disease aggressiveness and geography, with treatment, monitoring, and end-of-life events. Results: 7,335 women met criteria for ER+, HER2- MBC, with median age 59. Nearly half (46%) had aggressive disease, which correlated with shorter survival (hazard ratio (HR) 1.5 [1.4-1.6], P < 0.001). Treatment: first-line was endocrine therapy for 64% versus chemotherapy for 36%. Monitoring: 79% were imaged mostly by CT versus 21% by PET-CT, with median between-scan interval of 81 days; 63% received CA 15-3 serum tumor markers. End-of-life: 10% had a hospice code, of whom 19% had an ICU stay and 34% chemotherapy in the prior 3 months. Correlates of care: Disease aggressiveness correlated with first-line chemotherapy (odds ratio (OR) 2.0 [1.8-2.2], P < 0.001), PET/CT (OR 1.6 [1.4-1.8], P < 0.001), more frequent scans (OR 2.3 [2.1-2.6], P < 0.001), and chemotherapy < 3 months pre-hospice (OR 1.2 [1.1-1.4], P < 0.001), but not with CA 15-3 monitoring or ICU stay. Disease aggressiveness did not vary by region (χ2 P = 0.8), but region was significantly associated with treatment, monitoring, and end-of-life care (P < 0.001). Conclusions: Approximately two-thirds of ER+, HER2- MBC patients receive first-line endocrine therapy and are monitored with CA 15-3 serum tumor markers; 10% had evidence of hospice use, a likely underestimate due to differential follow-up. MBC care patterns vary by geography while disease aggressiveness does not, suggesting that care is not optimally tailored to individuals. These insights from claims data can inform quality improvement for MBC care.

Background Metastatic breast cancer (MBC) treatment has changed substantially over time, but we do not know whether survival post-metastasis has improved at the population level. Methods We searched for studies of MBC patients that reported survival after metastasis in at least two time periods between 1970 and the present. We used meta-regression models to test for survival improvement over time in four disease groups: recurrent, recurrent estrogen (ER)-positive, recurrent ER-negative, and de novo stage IV. We performed sensitivity analyses based on bias in some studies that could lead earlier cohorts to include more aggressive cancers. Results There were 15 studies of recurrent MBC (N = 18 678 patients; 3073 ER-positive and 1239 ER-negative); meta-regression showed no survival improvement among patients recurring between 1980 and 1990, but median survival increased from 21 (95% confidence interval [CI] = 18 to 25) months to 38 (95% CI = 31 to 47) months from 1990 to 2010. For ER-positive MBC patients, median survival increased during 1990–2010 from 32 (95% CI = 23 to 43) to 57 (95% CI = 37 to 87) months, and for ER-negative MBC patients from 14 (95% CI = 11 to 19) to 33 (95% CI = 21 to 51) months. Among eight studies (N = 35 831) of de novo stage IV MBC, median survival increased during 1990–2010 from 20 (95% CI = 16 to 24) to 31 (95% CI = 24 to 39) months. Results did not change in sensitivity analyses. Conclusion By bridging studies over time, we demonstrated improvements in survival for recurrent and de novo stage IV MBC overall and across ER-defined subtypes since 1990. These results can inform patient-doctor discussions about MBC prognosis and therapy.

Anthracyclines are a highly effective component of curative breast cancer chemotherapy but are associated with substantial morbidity1,2. Because anthracyclines work in part by inhibiting topoisomerase-II (TOP2) on accessible DNA3,4, we hypothesized that chromatin regulatory genes (CRGs) that mediate DNA accessibility might predict anthracycline response. We studied the role of CRGs in anthracycline sensitivity in breast cancer through integrative analysis of patient and cell line data. We identified a consensus set of 38 CRGs associated with anthracycline response across ten cell line datasets. By evaluating the interaction between expression and treatment in predicting survival in a metacohort of 1006 patients with early-stage breast cancer, we identified 54 CRGs whose expression levels dictate anthracycline benefit across the clinical subgroups; of these CRGs, 12 overlapped with those identified in vitro. CRGs that promote DNA accessibility, including Trithorax complex members, were associated with anthracycline sensitivity when highly expressed, whereas CRGs that reduce accessibility, such as Polycomb complex proteins, were associated with decreased anthracycline sensitivity. We show that KDM4B modulates TOP2 accessibility to chromatin, elucidating a mechanism of TOP2 inhibitor sensitivity. These findings indicate that CRGs mediate anthracycline benefit by altering DNA accessibility, with implications for the stratification of patients with breast cancer and treatment decision making.

The aim of this study is to determine the effect of chromatin regulators on anthracycline response in breast cancer. Anthracyclines are a highly effective component of curative breast cancer chemotherapy, but can be associated with substantial side effects. Because anthracyclines work in part via inhibition of topoisomerase II (TOP2) on accessible DNA, we hypothesized that expression levels of chromatin regulatory proteins that mediate DNA accessibility might predict anthracycline response. We used RNA-sequencing data from the TCGA breast cancer cohort to infer a breast cancer chromatin regulatory network using the ARACNE algorithm. A panel of 45 breast cancer cell lines with accompanying gene expression and growth inhibitory (GI50) data was used to build a genome-wide signature of anthracycline response. Based on the GI50, cell lines were classified as sensitive or resistant. The VIPER algorithm was then used to identify chromatin regulatory genes (CRGs) from the ARACNE network whose targets were significantly enriched in the anthracycline resistance signature. We further compiled a metacohort of 1006 clinically annotated early-stage breast cancer patients with gene expression data, allowing us to identify CRGs whose expression levels were associated with outcome after adjuvant treatment with anthracyclines. 24 CRGs associated (P Citation Format: Jose A. Seoane, Jacob G. Kirkland, Jennifer L. Caswell-Jin, Gerald R. Crabtree, Christina Curtis. Chromatin state as a mechanism of anthracycline response in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2108.

1513 Background: Next-generation sequencing enables rapid analysis of many inherited cancer susceptibility genes. Little is known about the prevalence and penetrance of pathogenic variants (PVs) in such genes among post-menopausal women with breast cancer, who comprise the majority of all breast cancer patients. Methods: The Women’s Health Initiative enrolled post-menopausal women from 1993-1998. We conducted a nested case-control study using banked DNA samples of 2,195 women who subsequently developed invasive breast cancer (cases) and 2,322 cancer-free controls. Sequenced genes were APC, ATM, BARD1, BMPR1A, BRCA1, BRCA2, BRIP1, CDH1, CDK4, CDKN2A (p16INK4a and p14ARF) , CHEK2, EPCAM, GREM1, MLH1, MSH2, MSH6, MUTYH, NBN, PALB2, PMS2, POLD1, POLE, PTEN, RAD51C, RAD51D, SMAD4, STK11, and TP53. PV were defined using American College of Medical Genetics criteria. PV prevalence is reported as proportions and penetrance as the odds ratio (OR) and 95% confidence interval (CI) of PV versus none among cases versus controls. Results: Among cases, the median age at diagnosis was 73 years; 66% were White, 18% Black, 6% Hispanic, 6% Asian and 4% other. The prevalence of PVs in any gene was significantly higher in cases (6.61%, 95% CI 5.57-7.65%) versus controls (4.09%, 95% CI 3.29-4.90%). The prevalence of BRCA1/2 PVs was 1.2% in cases and 0.22% in controls. Among cases, the prevalence of PVs in other breast cancer-risk genes was 2.3% ( ATM, CDH1, BARD1, BRIP1, CHEK2, NBN, and PALB2 collectively), two-fold higher than PVs in BRCA1/2. Prevalence of BRCA1/2 PVs decreased with age among cases, while prevalence of ATM, CHEK2 and PALB2 PVs did not. Statistically significant ORs for breast cancer penetrance were observed for BRCA1 (5.43, 95% CI 1.19-51.52), BRCA2 (4.71, 95% CI 1.84-15.08), BARD1 (9.78, 95% CI 1.04-1295.87) and PALB2 (6.30, 95% CI 1.93-31.94). Conclusions: Approximately 7% of women diagnosed with post-menopausal breast cancer carry a PV in a cancer susceptibility gene. In contrast to studies of younger breast cancer patients, PVs in other breast cancer-related genes were two times more common than in BRCA1/2. Results may guide genetic testing of women with post-menopausal breast cancer.

PurposeWe examined racial/ethnic differences in the usage and results of germ-line multiple-gene sequencing (MGS) panels to evaluate hereditary cancer risk.MethodsWe collected genetic testing results and clinical information from 1,483 patients who underwent MGS at Stanford University between 1 January 2013 and 31 December 2015.ResultsAsians and Hispanics presented for MGS at younger ages than whites (48 and 47 vs. 55; P = 5E-16 and 5E-14). Across all panels, the rate of pathogenic variants (15%) did not differ significantly between racial groups. Rates by gene did differ: in particular, a higher percentage of whites than nonwhites carried pathogenic CHEK2 variants (3.8% vs. 1.0%; P = 0.002). The rate of a variant of uncertain significance (VUS) result was higher in nonwhites than whites (36% vs. 27%; P = 2E-4). The probability of a VUS increased with increasing number of genes tested; this effect was more pronounced for nonwhites than for whites (1.1% absolute difference in VUS rates testing BRCA1/2 vs. 8% testing 13 genes vs. 14% testing 28 genes), worsening the disparity.ConclusionIn this diverse cohort undergoing MGS testing, pathogenic variant rates were similar between racial/ethnic groups. By contrast, VUS results were more frequent among nonwhites, with potential significance for the impact of MGS testing by race/ethnicity.

The original article was published on 22 July 2015: https://doi.org/10.1038/ncomms8539. The article is also available at: http://hdl.handle.net/1822/62280, [Excerpt] In this Article, members of the UCSF cohort who had been alive for longer than two years were inadvertently included in the data presented in Table 3. USCF/old treatments should have 109 patients with a hazard ratio of 3.35 and a P value of 0.00028 instead of the 124 patients with a hazard ratio of 3.37 and a P value of 0.00026. The USCF/new patients should have 187 patients with a hazard ratio of 3.57 and a P value of 0.0007 instead of the 208 patients with a hazard ratio of 3.62 and a P value of 0.0006. [...]

A corrigendum to this article was published on 09 December 2015: https://doi.org/10.1038/ncomms10203. The corrigendum is also available at: http://hdl.handle.net/1822/62281 This work was supported by the Steve and Nancy Grand Multiple Myeloma Translational Initiative and a grant from Expression Analysis and a grant from the National Cancer Institute (R21CA191896). E.Z. is supported in part by a mid-career award in patient research from the National Cancer Institute (K24169004). Collection of blood samples from Polish patients and controls from Łodz area, and DNA extraction was supported by a grant from Polish Ministry of Science and Higher Education (No. NN402178334).DNA extraction from Danish healthy controls was supported by The Research Fund at Region Sjælland, Denmark. The Utah study was supported by LLS 6067–09, CA152336and CA134674, with data collection made possible, in part, by the Utah Population Database (UPDB) and the Utah Cancer Registry (UCR). Partial support for all data sets within the UPDB is provided by the Huntsman Cancer Institute (HCI) and the HCICancer Center Support grant, P30 CA42014. The UCR is supported in part by NIH contract HHSN261201000026C from the NCI SEER Program with additional support from the Utah State Department of Health and the University of Utah. In Utah, we thank Jathine Wong for support in formatting files and advice on R programming. Several authors (E.Z., N.C., F.C., C.V.) are members of the International Multiple Myeloma Consortium. We are grateful to the leadership and other members of the International Multiple Myeloma Consortium for facilitating this collaborative study.