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Purpose: Ductal carcinoma in situ (DCIS) is treated to prevent progression to invasive breast cancer. Yet, most lesions will never progress, implying that overtreatment exists. Therefore, we aimed to identify factors distinguishing harmless from potentially hazardous DCIS using a nested case–control study.Experimental Design: We conducted a case–control study nested in a population-based cohort of patients with DCIS treated with breast-conserving surgery (BCS) alone (N = 2,658) between 1989 and 2005. We compared clinical, pathologic, and IHC DCIS characteristics of 200 women who subsequently developed ipsilateral invasive breast cancer (iIBC; cases) and 474 women who did not (controls), in a matched setting. Median follow-up time was 12.0 years (interquartile range, 9.0–15.3). Conditional logistic regression models were used to assess associations of various factors with subsequent iIBC risk after primary DCIS.Results: High COX-2 protein expression showed the strongest association with subsequent iIBC [OR = 2.97; 95% confidence interval (95% CI), 1.72–5.10]. In addition, HER2 overexpression (OR = 1.56; 95% CI, 1.05–2.31) and presence of periductal fibrosis (OR = 1.44; 95% CI, 1.01–2.06) were associated with subsequent iIBC risk. Patients with HER2+/COX-2high DCIS had a 4-fold higher risk of subsequent iIBC (vs. HER2−/COX-2low DCIS), and an estimated 22.8% cumulative risk of developing subsequent iIBC at 15 years.Conclusions: With this unbiased study design and representative group of patients with DCIS treated by BCS alone, COX-2, HER2, and periductal fibrosis were revealed as promising markers predicting progression of DCIS into iIBC. Validation will be done in independent datasets. Ultimately, this will aid individual risk stratification of women with primary DCIS. Clin Cancer Res; 24(15); 3593–601. ©2018 AACR.

This file includes the legends for supplementary figure 1 and supplementary tables 1 and 2

This file shows the significant regions (highlighted in supplementary figure 1) for the comparison B1mutER+ versus SporER+. Subsequently the weighted average frequency in B1mutER+, in SporER+ and the difference between both groups is shown. The last two columns show respectively the p-value and the false discovery rate (q-bound).

Purpose:In breast cancer, pathologic complete response (pCR) to neoadjuvant systemic therapy (NST) is associated with favorable long-term outcome. Trastuzumab emtansine as additional adjuvant therapy improves recurrence-free survival of patients with HER2-positive breast cancer without pCR, but it is uncertain whether all patients without pCR need additional therapy. We evaluated the prognostic value of residual disease after trastuzumab-based NST in patients with HER2-positive breast cancer using Residual Cancer Burden (RCB), Neoadjuvant Response Index (NRI), and Neo-Bioscore.Experimental Design:We included patients with stage II or III HER2-positive breast cancer treated with trastuzumab-based NST and surgery at The Netherlands Cancer Institute between 2004 and 2016. RCB, NRI, and Neo-Bioscore were determined. Primary endpoint was 5-year recurrence-free interval (RFI). A 3% difference compared with the pCR group was considered acceptable as noninferiority margin on the 5-year RFI estimate, based on a proportional hazards model, and its lower 95% confidence boundary.Results:A total of 283 women were included. Median follow-up was 67 months (interquartile range 44–100). A total of 157 patients (56%) with pCR (breast and axilla) had a 5-year RFI of 92% (95% CI, 88–97); patients without pCR had a 5-year RFI of 80% (95% CI, 72–88). Patients with an RCB = 1 (N = 40, 15%), an NRI score between 0.75 and 0.99 (N = 30, 11%), or a Neo-Bioscore of 0 to 1 (without pCR; N = 28, 11%) have a 5-year RFI that falls within a predefined noninferiority margin of 3% compared with patients with pCR.Conclusions:The RCB, NRI, and Neo-Bioscore can identify patients with HER2-positive breast cancer with minimal residual disease (i.e., RCB = 1, NRI ≥ 0.75, or Neo-Bioscore = 0–1) after NST who have similar 5-year RFI compared with patients with pCR.

Purpose: Adequate preoperative staging of large sessile rectal tumors requires identifying adenomas that already contain an invasive focus, specifically those that are growing in or beyond the submucosa. We systematically compared chromosomal instability patterns in adenoma and carcinoma fractions of the same lesion to assess specific steps in rectal tumor progression.Experimental Design: We analyzed 36 formalin-fixed, paraffin-embedded tumors. Both the adenoma and carcinoma fractions were typed with single nucleotide polymorphism arrays and compared with 21 previously described pure adenomas. Eighteen cases were included in an intratumor heterogeneity analysis.Results: Five specific “malignant” events (gain of 8q, 13q, and 20q and loss of 17p and 18q) and aberrant staining for p53 and SMAD4 were all increased in the adenoma fractions of carcinoma cases compared with pure adenomas. Paired analysis revealed that 31% of the samples had an equal amount of malignant aberrations in their adenoma and carcinoma fractions, whereas 25% had one and 33% had two or more extra malignant events in the carcinoma fraction. Analysis of three core biopsies per patient showed a large degree of intratumor heterogeneity. However, the number of malignant aberrations in the biopsy with the most aberrations per tumor correlated with the corresponding adenoma or carcinoma fraction (r = 0.807; P < 0.001).Conclusion: Five specific chromosomal aberrations, combined with immunohistochemistry for p53 and SMAD4, can predict possible progression of sessile rectal adenomas to early rectal cancer and can, after validation studies, be added to preoperative staging. Preferably, three biopsies should be taken from each tumor to address intratumor heterogeneity.

Supplementary Tables

Supplementary Table from Progression and Tumor Heterogeneity Analysis in Early Rectal Cancer

Supplementary Materials and Methods

Supplementary data to Steenbruggen TG, van Seijen M, Janssen LM, van Ramshorst MS, van Werkhoven E, Vrancken Peeters MJTDF, et al. Prognostic value of residual disease after neoadjuvant therapy in HER2-positive breast cancer evaluated by Residual Cancer Burden, Neoadjuvant Response Index & Neo-Bioscore. Table S1, Table S2, Table S3, Table S4, Text and legend Figure S1

This figure shows pairwise comparisons between the BRCA2-mutated ER+ tumors and the sporadic ER+ tumors (upper panel) and the BRCA1-mutated ER+tumors and the sporadic ER+ tumors (lower

Figure S1 from Prognostic Value of Residual Disease after Neoadjuvant Therapy in HER2-Positive Breast Cancer Evaluated by Residual Cancer Burden, Neoadjuvant Response Index, and Neo-Bioscore

This file shows the significant regions (highlighted in supplementary figure 1) for the comparison B2mutER+ versus SporER+. Subsequently the weighted average frequency in B2mutER+, in SporER+ and the difference between both groups is shown. The last two columns show respectively the p-value and the false discovery rate (q-bound).

Figure S1: Keap1, SMARCB1, SMARCA4 and ARID1A regulate resistance to doxorubicin. Figure S2: Keap1 controls expression of NQO1 through Nrf2. Figure S3: C9orf82 regulates DNA double strand break repair. Figure S4: Expression of SWI/SNF complex subunits correlates to clinical outcome.

The topoisomerase II poisons doxorubicin and etoposide constitute longstanding cornerstones of chemotherapy. Despite their extensive clinical use, many patients do not respond to these drugs. Using a genome-wide gene knockout approach, we identified Keap1, the SWI/SNF complex, and C9orf82 (CAAP1) as independent factors capable of driving drug resistance through diverse molecular mechanisms, all converging on the DNA double-strand break (DSB) and repair pathway. Loss of Keap1 or the SWI/SNF complex inhibits generation of DSB by attenuating expression and activity of topoisomerase IIα, respectively, whereas deletion of C9orf82 augments subsequent DSB repair. Their corresponding genes, frequently mutated or deleted in human tumors, may impact drug sensitivity, as exemplified by triple-negative breast cancer patients with diminished SWI/SNF core member expression who exhibit reduced responsiveness to chemotherapy regimens containing doxorubicin. Collectively, our work identifies genes that may predict the response of cancer patients to the broadly used topoisomerase II poisons and defines alternative pathways that could be therapeutically exploited in treatment-resistant patients. Cancer Res; 75(19); 4176–87. ©2015 AACR.

Table S1: Overview of all screening hits.

Background Studies have shown that blood platelets contain tumour-specific mRNA profiles tumour-educated platelets (TEPs). Here, we aim to train a TEP-based breast cancer detection classifier. Methods Platelet mRNA was sequenced from 266 women with stage I–IV breast cancer and 212 female controls from 6 hospitals. A particle swarm optimised support vector machine (PSO-SVM) and an elastic net-based classifier (EN) were trained on 71% of the study population. Classifier performance was evaluated in the remainder (29%) of the population, followed by validation in an independent set (37 cases and 36 controls). Potential confounding was assessed in post hoc analyses. Results Both classifiers reached an area under the curve (AUC) of 0.85 upon internal validation. Reproducibility in the independent validation set was poor with an AUC of 0.55 and 0.54 for the PSO-SVM and EN classifier, respectively. Post hoc analyses indicated that 19% of the variance in gene expression was associated with hospital. Genes related to platelet activity were differentially expressed between hospitals. Conclusions We could not validate two TEP-based breast cancer classifiers in an independent validation cohort. The TEP protocol is sensitive to within-protocol variation and revision might be necessary before TEPs can be reconsidered for breast cancer detection.

PurposeInvasive breast cancer patients are increasingly being treated with neoadjuvant chemotherapy, however, only a fraction of the patients respond to it completely. To prevent over-treating patients with a toxic drug, there is an urgent need for biomarkers capable of predicting treatment response before administering the therapy. In this retrospective study, we developed interpretable, deep-learning based biomarkers to predict the pathological complete response (pCR, i.e. the absence of tumor cells in the surgical resection specimens) to neoadjuvant chemotherapy from digital pathology H&E images of pre-treatment breast biopsies.Experimental DesignOur approach consists of two steps: In the first step, using deep learning, mitoses are detected and the tissue segmented into several morphology compartments including tumor, lymphocytes and stroma. In the second step, computational biomarkers are derived from the segmentation and detection output to encode slide-level relationships between the morphological structures with focus on tumor infiltrating lymphocytes (TILs). We developed and evaluated our method on slides from N=721 patients from three European medical centers with triple-negative and Luminal B breast cancers.ResultsThe investigated biomarkers yield statistically significant prediction performance for pCR with areas under the receiver operating characteristic curve between 0.66 and 0.88 depending on the cancer subtype and center.ConclusionThe proposed computational biomarkers predict pathological complete response, but will require more evaluation and finetuning for clinical application. The results further corroborate the potential role of deep learning to automate TILs quantification, and their predictive value in breast cancer neoadjuvant treatment planning.

Background Radiotherapy (RT) following breast-conserving surgery (BCS) for ductal carcinoma in situ (DCIS) reduces ipsilateral breast event rates in clinical trials. This study assessed the impact of DCIS treatment on a 20-year risk of ipsilateral DCIS (iDCIS) and ipsilateral invasive breast cancer (iIBC) in a population-based cohort. Methods The cohort comprised all women diagnosed with DCIS in the Netherlands during 1989–2004 with follow-up until 2017. Cumulative incidence of iDCIS and iIBC following BCS and BCS + RT were assessed. Associations of DCIS treatment with iDCIS and iIBC risk were estimated in multivariable Cox models. Results The 20-year cumulative incidence of any ipsilateral breast event was 30.6% (95% confidence interval (CI): 28.9–32.6) after BCS compared to 18.2% (95% CI 16.3–20.3) following BCS + RT. Women treated with BCS compared to BCS + RT had higher risk of developing iDCIS and iIBC within 5 years after DCIS diagnosis (for iDCIS: hazard ratio (HR)age 3.2 (95% CI 1.6–6.6); HRage ≥ 50 3.6 (95% CI 2.6–4.8) and for iIBC: HRage 2.1 (95% CI 1.4–3.2); HRage ≥ 50 4.3 (95% CI 3.0–6.0)). After 10 years, the risk of iDCIS and iIBC no longer differed for BCS versus BCS + RT (for iDCIS: HRage 0.7 (95% CI 0.3–1.5); HRage ≥ 50 0.7 (95% CI 0.4–1.3) and for iIBC: HRage 0.6 (95% CI 0.4–0.9); HRage ≥ 50 1.2 (95% CI 0.9–1.6)). Conclusion RT is associated with lower iDCIS and iIBC risk up to 10 years after BCS, but this effect wanes thereafter.

Background: The addition of pertuzumab to neoadjuvant trastuzumab-containing chemotherapy significantly improves pathological complete response (pCR) rates in patients with HER2-positive breast cancer. However, effects on long-term survival outcomes have been modest. While prior studies suggest that only a subset of patients benefits most from the addition of pertuzumab, a reliable biomarker for response prediction has not been established. BluePrint is an 80-gene molecular subtyping test that classifies breast tumors as Basal-, Luminal- or HER2-subtype. Recent data shows that some tumors have more than one activated pathway and can be classified as dual subtype. Secondary analyses from the Aphinity trial (NCT01358877) suggest that pertuzumab benefit is largely restricted to patients with single-activated HER2-type tumors and less pronounced in patients with other single- or dual-activated subtypes. In this study, we evaluated BluePrint subtype as a biomarker for predicting response to trastuzumab-containing neoadjuvant chemotherapy with or without pertuzumab in a large nationwide cohort of patients. Methods: We collected data of all patients with stage II-III HER2-positive breast cancer who received neoadjuvant chemotherapy and trastuzumab between January 2013 and January 2016 from the Netherlands Cancer Registry. Pertuzumab was not reimbursed in the Netherlands during this period but was available to patients participating in the TRAIN2 clinical trial (NCT01996267). Pre-treatment biopsies were collected and tumor cell percentage was adequate in 404 patients who received pertuzumab and in 432 matched control patients who did not receive pertuzumab. Matching factors included treatment with anthracyclines (yes vs. no), age and hormone receptor status. BluePrint molecular subtypes were determined sucessfully through microarray in 680 patients. Pathological complete response (pCR) and overall survival (OS) were analyzed by treatment arm and BluePrint subtypes, using logistic and Cox regression analyses. Results: Microarray data was available for 334 patients who received pertuzumab and 346 patients who did not. Tumors were classified as single HER2 (77.6%), Luminal-HER2 (11.3%), single Luminal (8.4%), or other (2.6%). Overall, pCR rate was 65.0% in patients who received pertuzumab and 38.9% in patients who did not. In patients with single HER2-type tumors, pCR rate was significantly higher with pertuzumab (77.0% vs. 46.0%, multivariate adjusted odds ratio [aOR] 3.92, 95% confidence interval [CI] 2.60 - 5.98). In patients with other subtypes, pCR rate was lower overall and not significantly different with or without pertuzumab (23.5% vs. 13.5%, aOR 2.50, 95% CI 0.98 - 6.89). In addition, multivariate Cox regression analyses showed a significant OS benefit with pertuzumab in patients with single HER2-type tumors (adjusted hazard ratio [aHR] 0.33, 95% CI 0.16 - 0.67), but not in other subtypes (aHR 0.94, 95% CI 0.31 - 2.83). However, tests for interactions between pertuzumab and BluePrint subtypes were not significant. Conclusion: We confirmed the results of a prior secondary analysis of the Aphinity trial that the benefit of adding pertuzumab to (neo)adjuvant trastuzumab-based chemotherapy seems most pronounced in patients with a molecularly defined single-activated HER2-subtype. In other subtypes, pathological complete response rates and long-term outcomes are worse overall and no clear benefit of pertuzumab was seen, although tests for interaction between pertuzumab treatment and BluePrint subtype were not significant. Citation Format: Marte C Liefaard, Anna van der Voort, Joyce Sanders, Shiva Vonk, Hugo M Horlings, Sabine Siesling, Linda de Munck, Elise van Leeuwen-Stok, Miranda Kleijn, Lorenza Mittempergher, Midas M Kuilman, Esther H Lips, Gabe S Sonke. Effect of pertuzumab plus neoadjuvant trastuzumab-based chemotherapy in early-stage HER2-positive breast cancer according to BluePrint molecularly defined breast cancer subtypes [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 PD15-07.

In this study, we measured the kinase activity profiles of 32 pre-treatment tumour biopsies of HER2-positive breast cancer patients. The aim of this study was to assess the prognostic potential of kinase activity levels to identify potential mechanisms of resistance and to predict treatment success of HER2-targeted therapy combined with chemotherapy. Indeed, our system-wide kinase activity analysis, based on targeted mass spectrometry measurement of kinase activation loops, allowed us to link kinase activity to treatment response. Overall, high kinase activity in the HER2-pathway was associated with good treatment outcome. Furthermore, we found eleven kinases differentially regulated between treatment outcome groups. Amongst those, well-known players in therapy resistance were found, such as p38a, ERK and FAK, as well as a potential new player in drug resistance, namely MARK. Lastly, we defined an optimal signature of four kinases in a multiple logistic regression diagnostic test for prediction of treatment outcome (AUC=0.926). This kinase signature showed high sensitivity and specificity, indicating its potential as predictive biomarker for treatment success of HER2-targeted therapy.

Ductal carcinoma in situ (DCIS) is a non-invasive breast neoplasia that accounts for 25% of all screen-detected breast cancers diagnosed annually. Neoplastic cells in DCIS are confined to the ductal system of the breast, although they can escape and progress to invasive breast cancer in a subset of patients. A key concern of DCIS is overtreatment, as most patients screened for DCIS and in whom DCIS is diagnosed will not go on to exhibit symptoms or die of breast cancer, even if left untreated. However, differentiating low-risk, indolent DCIS from potentially progressive DCIS remains challenging. In this Review, we summarize our current knowledge of DCIS and explore open questions about the basic biology of DCIS, including those regarding how genomic events in neoplastic cells and the surrounding microenvironment contribute to the progression of DCIS to invasive breast cancer. Further, we discuss what information will be needed to prevent overtreatment of indolent DCIS lesions without compromising adequate treatment for high-risk patients.

Advances in tissue analysis methods, image analysis, high-throughput molecular profiling, and computational tools increasingly allow us to capture and quantify patient-to-patient variations that impact cancer risk, prognosis, and treatment response. Statistical models that integrate patient-specific information from multiple sources (e.g., family history, demographics, germline variants, imaging features) can provide individualized cancer risk predictions that can guide screening and prevention strategies. The precision, quality, and standardization of diagnostic imaging are improving through computer-aided solutions, and multigene prognostic and predictive tests improved predictions of prognosis and treatment response in various cancer types. A common theme across many of these advances is that individually moderately informative variables are combined into more accurate multivariable prediction models. Advances in machine learning and the availability of large data sets fuel rapid progress in this field. Molecular dissection of the cancer genome has become a reality in the clinic, and molecular target profiling is now routinely used to select patients for various targeted therapies. These technology-driven increasingly more precise and quantitative estimates of benefit versus risk from a given intervention empower patients and physicians to tailor treatment strategies that match patient values and expectations.

Ductal carcinoma in situ (DCIS) is a common precursor of invasive breast cancer (IBC), yet the genomic progression to recurrent disease remains poorly understood. A main contributor to this gap in knowledge arises from technical challenges with genomic profiling of formalin-fixed paraffin-embedded (FFPE) materials. To address this challenge, we developed Arc-well, the first high-throughput method that can perform single cell DNA sequencing of thousands of cells from FFPE materials and frozen tissues. Using Arc-well, we profiled genomic copy number in 27,851 single cells from 26 archival FFPE tissues that were stored for 3-31 years. Analysis of genomic evolution in 10 patients with matched DCIS and recurrent cancers (DCIS or IBC) separated by 2-16 years showed that many primary DCIS lesions had already undergone whole-genome-doubling and had extensive clonal diversity, similar to the paired recurrence. The data from most patients (8/10) suggest an evolutionary bottleneck model of progression, in which a single subclone persisted during the progression to the recurrent disease, revealing copy number aberrations associated with invasion and recurrence. Citation Format: Kaile Wang, Tapsi Kumar, Junke wang, Darlan Minussi, Emi Sei, Jianzhuo li, Tuan Tran, Aatish Thennavan, Min Hu, Anna Casasent, Zhenna Xiao, Shanshan Bai, Yuehui Zhao, Amado Zurita, Ana Aparicio, Brian Chapin, Jie ye, Jianjun Zhang, Don Gibbons, Andrew Futreal, Lorraine King, Jeffrey Marks, E. Shelley Hwang, Vandna Shah, Ellinor Sawyer, Petra Kristel, Jelle Wesseling, Esther H. Lips, Nicholas Navin. Archival single cell sequencing reveals persistent subclones over years to decades of DCIS progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 125.

The prognostic value of cytonuclear grade in ductal carcinoma in situ (DCIS) is debated, partly due to high interobserver variability and the use of multiple guidelines. The aim of this study was to evaluate interobserver agreement in grading DCIS between Dutch, British, and American pathologists. Haematoxylin and eosin-stained slides of 425 women with primary DCIS were independently reviewed by nine breast pathologists based in the Netherlands, the UK, and the USA. Chance-corrected kappa (kappa(ma)) for association between pathologists was calculated based on a generalised linear mixed model using the ordinal package in R. Overall kappa(ma) for grade of DCIS (low, intermediate, or high) was estimated to be 0.50 (95% confidence interval [CI] 0.44-0.56), indicating a moderate association between pathologists. When the model was adjusted for national guidelines, the association for grade did not change (kappa(ma) = 0.53; 95% CI 0.48-0.57); subgroup analysis for pathologists using the UK pathology guidelines only had significantly higher association (kappa(ma) = 0.58; 95% CI 0.56-0.61). To assess if concordance of grading relates to the expression of the oestrogen receptor (ER) and HER2, archived immunohistochemistry was analysed on a subgroup (n = 106). This showed that non-high grade according to the majority opinion was associated with ER positivity and HER2 negativity (100 and 89% of non-high grade cases, respectively). In conclusion, DCIS grade showed only moderate association using whole slide images scored by nine breast pathologists. As therapeutic decisions and inclusion in ongoing clinical trials are guided by DCIS grade, there is a pressing need to reduce interobserver variability in grading. ER and HER2 might be supportive to prevent the accidental and unwanted inclusion of high-grade DCIS in such trials.

Background We previously showed that BRCA-like profiles can be used to preselect individuals with the highest risk of carrying BRCA mutations but could also indicate which patients would benefit from double-strand break inducing chemotherapy. A simple, robust, and reliable assay for clinical use that utilizes limited amounts of formalin-fixed, paraffin-embedded tumor tissue to assess BRCAness status in both ER-positive and ER-negative breast cancer (BC) is currently lacking. Methods A digital multiplex ligation-dependent probe amplification (digitalMLPA) assay was designed to detect copy number alterations required for the classification of BRCA1-like and BRCA2-like BC. The BRCA1-like classifier was trained on 71 tumors, enriched for triple-negative BC; the BRCA2-like classifier was trained on 55 tumors, enriched for luminal-type BC. A shrunken centroid-based classifier was developed and applied on an independent validation cohort. A total of 114 cases of a randomized controlled trial were analyzed, and the association of the classifier result with intensified platinum-based chemotherapy response was assessed. Results The digitalMLPA BRCA1-like classifier correctly classified 91% of the BRCA1-like samples and 82% of the BRCA2-like samples. Patients with a BRCA-like tumor derived significant benefit of high-dose chemotherapy (adjusted hazard ratio (HR) 0.12, 95% CI 0.04–0.44) which was not observed in non-BRCA-like patients (HR 0.9, 95% CI 0.37–2.18) (p = 0.01). Analysis stratified for ER status showed borderline significance. Conclusions The digitalMLPA is a reliable method to detect a BRCA1- and BRCA2-like pattern on clinical samples and predicts platinum-based chemotherapy benefit in both triple-negative and luminal-type BC.

Intratumour heterogeneity fuels carcinogenesis and allows circumventing specific targeted therapies. HER2 gene amplification is associated with poor outcome in invasive breast cancer. Heterogeneous HER2 amplification has been described in 5–41% of breast cancers. Here, we investigated the genetic differences between HER2‐positive and HER2‐negative admixed breast cancer components. We performed an in‐depth analysis to explore the potential heterogeneity in the somatic mutational landscape of each individual tumour component. Formalin‐fixed, paraffin‐embedded breast cancer tissue of ten patients with at least one HER2‐negative and at least one HER2‐positive component was microdissected. Targeted next‐generation sequencing was performed using a customized 53‐gene panel. Somatic mutations and copy number variations were analysed. Overall, the tumours showed a heterogeneous distribution of 12 deletions, 9 insertions, 32 missense variants and 7 nonsense variants in 26 different genes, which are (likely) pathogenic. Three splice site alterations were identified. One patient had an EGFR copy number gain restricted to a HER2‐negative in situ component, resulting in EGFR protein overexpression. Two patients had FGFR1 copy number gains in at least one tumour component. Two patients had an 8q24 gain in at least one tumour component, resulting in a copy number increase in MYC and PVT1. One patient had a CCND1 copy number gain restricted to a HER2‐negative tumour component. No common alternative drivers were identified in the HER2‐negative tumour components. This series of 10 breast cancers with heterogeneous HER2 gene amplification illustrates that HER2 positivity is not an unconditional prerequisite for the maintenance of tumour growth. Many other molecular aberrations are likely to act as alternative or collaborative drivers. This study demonstrates that breast carcinogenesis is a dynamically evolving process characterized by a versatile somatic mutational profile, of which some genetic aberrations will be crucial for cancer progression, and others will be mere ‘passenger’ molecular anomalies., This in‐depth analysis of 10 breast cancers with heterogeneous HER2 gene amplification illustrates that HER2 positivity is not an unconditional prerequisite for the maintenance of tumour growth. This study demonstrates that breast carcinogenesis is a dynamically evolving process characterized by a versatile somatic mutational profile: some genetic aberrations will be crucial for cancer progression, and others will be mere ‘passenger’ molecular anomalies.

Purpose The majority of ‘low-risk’ (grade I/II) Ductal Carcinoma In Situ (DCIS) lesions do not progress to invasive breast cancer during a woman’s lifetime. Therefore, the safety of active surveillance versus standard surgical treatment for grade I/II DCIS is being evaluated in several clinical trials. If active surveillance is proven safe and implemented in clinical practice, a significant group of women with low-risk DCIS may forego surgery and radiotherapy. To further reduce their risks, there is an urgent need to identify potentially modifiable risk factors. Nonetheless, non-modifiable risk factors are also of great importance to gain clear understanding of the overall risk for developing a subsequent event (invasive breast cancer, in situ recurrences, distant metastases) following untreated DCIS. A systematic literature review was performed to evaluate the impact of established breast cancer risk factors on the risk of developing in situ or invasive disease after treatment of primary pure DCIS. Methods A systematic literature search was performed in PUBMED, EMBASE and Web of Science. PRISMA methodology was applied for the selection of studies. We only included studies that were published after the review by Shamliyan et al in 2010. Results Nine out of the 3,852 articles retrieved were included for final data extraction and evaluation. These nine studies included a total of 11,602 patients with primary pure DCIS. The sample size of these studies ranged from 50 to 4,131 patients. Across the studies, a total of 979 (range: 2-239) subsequent events, such as ipsilateral invasive, ipsilateral in situ events, and distant metastases, were reported. The median follow-up varied between four and nine years. There was limited information published and limited evidence in the selected studies especially on modifiable factors; and all studies except one, were in women of European-descent. Of the modifiable factors only a high BMI (≥25) in mainly postmenopausal women seemed to be associated with a lower risk of a subsequent event. There was some evidence for several non-modifiable predictors, i.e. younger age at diagnosis of DCIS, positive family history of breast cancer, premenopausal status, and high breast density. Age was associated with a two to threefold risk for experiencing a subsequent event. A positive family history had an almost two-fold increased risk, and pre-menopausal status increased the risk for a subsequent event between 46 to 89%. Furthermore, the highest quintile for breast density was associated with a 70% increase in risk for a subsequent event. See table 1 for effect sizes with 95% confidence intervals. Conclusion There is a limited number of studies published on the impact of risk factors on subsequent events after pure DCIS. Moreover, the available evidence is insufficient to identify potential targets for risk reduction strategies, due to the relatively small numbers and the lack of long term follow-up in a low-event disease such as DCIS. Traditional risk factors for primary invasive breast cancer showed, in general, also to be associated with the risk for a subsequent event after primary pure DCIS. The single study that reported on BMI showed a direction of association that was contrary to expectations. In conclusion large scale, well-designed, studies with specific attention to lifestyle factors are necessary to enable identification of DCIS who are at low and high risk for a subsequent event after primary pure DCIS. Table 1. Overview of published effect sizes of risk of a subsequent event after pure DCISIncluded articlesVariableHR/OR (95% CI)Moran et al. 2017 Age (≥50 vs < 50 years) HR(Multivariate) 0.54 (0.41-0.71)Alvarado et al. 2012Age (2 relatives with Breast Cancer)OR(Multivariate) 1.78 (1.02–3.10)De Lorenzi et al. 2018BMI (high ≥ 25 vs low)HR(Multivariate)0.48(0.26-0.90)De Lorenzi et al. 2018Menopausal state (pre vs post)HR(Multivariate)1.89 (0.37-0.77)Shurell et al. 2017Menopausal state (post vs pre)HR(Multivariate)0.54 (0.37-0.77)Habel et al. 2010Breast density (highest quintile)HR(Multivariate)1.70 (1.00–2.39) Citation Format: Sena Alaeikhanehshir, Ellen G Engelhardt, Frederieke H van Duijnhoven, Maartje van Seijen, Patrick A Bhairosing, Donna Pinto, Deborah Collyar, Jelle Wesseling, Esther H Lips, Marjanka K Schmidt. The impact of patient characteristics and lifestyle factors on the risk of an ipsilateral event after a primary DCIS: A systematic review [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 P5-08-15.

Background: The incidence of ductal carcinoma in situ (DCIS) has greatly increased since the introduction of mammographic screening. However, so far, it is still not possible to predict which patient with DCIS will further develop invasive breast cancer. Adipocyte hypertrophy and adipocyte inflammation has been associated with increased risk of developing breast cancer in postmenopausal women and with worse prognosis in breast cancer patients. In this study, we aimed 1) at evaluating the association between mammary adiposity, as assessed by digital pathology, with DCIS progression using a unique case-control series with long-term follow up and 2) study the interaction between DCIS cells and the surrounding adipocytes. Patients and methods: Mammary adipocyte size was measured in surgical specimens of 279 women with primary DCIS. These mammary adipocyte measurements were compared in 109 women with primary DCIS who subsequently developed ipsilateral invasive breast cancer (iIBC) (cases) and 170 women who did not (controls). Patients were matched for age and follow-up duration. Median follow-up time was 12.8 years. Post-menopausal patients were defined as >50 years at diagnosis and represented 220/279 (78,9%) of patients. All patients were treated with breast cancer-conserving surgery only. Intact adipocytes distant from the DCIS lesions were measured (largest diameter and area) using HALO™ image analysis software (Indica Labs, Corrales, NM) on H&E-stained whole slide images of primary DCIS. Given the specific interest in the larger adipocytes, we systematically considered the 75th percentile as unique value for each patient. An average of 4259 adipocytes (min:70, max:21107) was measured per patient. Tissue segmentation was used to measure the adipose area. Adipose triglyceride lipase (ATGL clone 2138, Cell Signaling) immunohistochemistry (IHC) was applied to study ATGL expression in DCIS cells. Her2, ER and COX-2 IHC and RNAseq of microdissected pure DCIS was already available for this series (LL. Visser et al. Clin Can Res 24 (15) 2018). Conditional logistic regression was applied to investigate differences in adipocyte hypertrophy between groups of patients (e.g., cases and controls). Results: Adipocytes were larger and the proportion of adipose tissue was higher in post-menopausal patients (both p Citation Format: Mathilde Matthea Machteld Almekinders, Michael Schaapveld, Bram Thijssen, Ingrid Hofland, Marjolijn Mertz, Dennis Peters, Annegien Broeks, Lodewijk Wessels, Wilbert Zwart, Jos Jonkers, Esther Lips, Christine Desmedt, Jelle Wesseling, on behalf of the PRECISION Team. Impact of increased mammary adiposity on DCIS progression [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 P6-15-07.

Purpose We aimed to study radiation-induced gene expression changes and to identify differences in gene expression between patients with and without response to radiation therapy (RT) for invasive breast cancer with the purpose of exploring whether a predictive signature could be developed. Such a signature could assist in optimizing individualized locoregional treatment. Methods and Materials RNA-seq using next-generation sequencing was performed on fresh frozen samples from pretreatment biopsies and post-RT surgery specimens from patients with low-risk breast cancer treated within the multicenter preoperative accelerated partial breast irradiation trial. Patients were treated with preoperative RT (10 × 4 Gy in 10 days or 5 × 6 Gy in 5 days) and a lumpectomy 6 weeks thereafter. The response of the tumor to RT was evaluated by pathologic assessment. To analyze the gene expression data, unsupervised and supervised clustering was performed. Gene expression profiles were compared between biopsies of responders and nonresponders and between samples before and after RT. Results Ninety-four samples from 77 patients were analyzed: 68 pretreatment biopsies and 26 post-RT surgery specimens. Six patients had a (near) complete pathologic response, 3 patients had a good response, 32 patients had a partial response, and 22 patients had no or very limited response. Comparing patients with and without response to RT, 25 genes were significantly differentially expressed and were not linked to a pathway. Comparison of samples before and after RT identified significant changes in gene expression. Genes involved in p53 signaling, TNFA1 signaling, apoptosis, epithelial mesenchymal transition, and inflammatory response were upregulated. Genes involved in mitotic spindle, G2M checkpoint, and E2F targets were downregulated. Conclusions Radiation-induced gene expression changes mainly involved p53 signaling, cell cycle regulation, DNA repair, and inflammatory response. No clinically significant differences could be identified in gene expression between patients with and without response to RT.

When locally advanced breast cancer is treated with neoadjuvant chemotherapy, the recurrence risk is significantly higher if no complete pathologic response is achieved. Identification of the underlying resistance mechanisms is essential to select treatments with maximal efficacy and minimal toxicity. Here we employed gene expression profiles derived from 317 HER2-negative treatment-naïve breast cancer biopsies of patients who underwent neoadjuvant chemotherapy, deep whole exome, and RNA-sequencing profiles of 22 matched pre- and post-treatment tumors, and treatment outcome data to identify biomarkers of response and resistance mechanisms. Molecular profiling of treatment-naïve breast cancer samples revealed that expression levels of proliferation, immune response, and extracellular matrix (ECM) organization combined predict response to chemotherapy. Triple negative patients with high proliferation, high immune response and low ECM expression had a significantly better treatment response and survival benefit (HR 0.29, 95% CI 0.10–0.85; p = 0.02), while in ER+ patients the opposite was seen (HR 4.73, 95% CI 1.51–14.8; p = 0.008). The characterization of paired pre-and post-treatment samples revealed that aberrations of known cancer genes were either only present in the pre-treatment sample (CDKN1B) or in the post-treatment sample (TP53, APC, CTNNB1). Proliferation-associated genes were frequently down-regulated in post-treatment ER+ tumors, but not in triple negative tumors. Genes involved in ECM were upregulated in the majority of post-chemotherapy samples. Genomic and transcriptomic differences between pre- and post-chemotherapy samples are common and may reveal potential mechanisms of therapy resistance. Our results show a wide range of distinct, but related mechanisms, with a prominent role for proliferation- and ECM-related genes.

Purpose: Previously, we developed breast cancer BRCA1-like and BRCA2-like copy-number profile shrunken centroid classifiers predictive for mutation status and response to therapy, targeting homologous recombination deficiency (HRD). Therefore, we investigated BRCA1- and BRCA2-like classification in ovarian cancer, aiming to acquire classifiers with similar properties as those in breast cancer. Experimental Design: We analyzed DNA copy-number profiles of germline BRCA1- and BRCA2-mutant ovarian cancers and control tumors and observed that existing breast cancer classifiers did not sufficiently predict mutation status. Hence, we trained new shrunken centroid classifiers on this set and validated them in the independent The Cancer Genome Atlas dataset. Subsequently, we assessed BRCA1/2-like classification and obtained germline and tumor mutation and methylation status of cancer predisposition genes, among them several involved in HR repair, of 300 ovarian cancer samples derived from the consecutive cohort trial AGO-TR1 (NCT02222883). Results: The detection rate of the BRCA1-like classifier for BRCA1 mutations and promoter hypermethylation was 95.6%. The BRCA2-like classifier performed less accurately, likely due to a smaller training set. Furthermore, three quarters of the BRCA1/2-like tumors could be explained by (epi)genetic alterations in BRCA1/2, germline RAD51C mutations and alterations in other genes involved in HR. Around half of the non–BRCA-mutated ovarian cancer cases displayed a BRCA-like phenotype. Conclusions: The newly trained classifiers detected most BRCA-mutated and methylated cancers and all tumors harboring a RAD51C germline mutations. Beyond that, we found an additional substantial proportion of ovarian cancers to be BRCA-like.

Ductal Carcinoma in Situ (DCIS) is a non-invasive non-obligate precursor of invasive breast cancer (IBC). DCIS is usually treated by surgery combined with radiotherapy, which can have a large impact on the life of patients. However, many of these DCIS lesions would never progress into IBC. To reduce the overtreatment of DCIS, but assure proper treatment for high risk DCIS, it is crucial to understand the biology underlying DCIS. To study the biology of DCIS we established Mouse INtraDuctal (MIND) patient-derived xenograft (PDX) models by intraductally injecting patient DCIS material into the mammary ducts of female immunocompromised mice. We engrafted 130 samples, which have been incubated in vivo for a period of 12 months. We obtain a take rate of 88% with 46% of our models showing invasive progression. Histology and molecular subtyping by PAM50 classification are well preserved in the MIND models compared to the primary counterpart, ensuring that our MIND models represent the patient disease well. For 102 primary samples we obtained RNAseq profiles as well as for 64 matched MIND-PDX models. In addition whole exome-/panel sequencing data is generated from the same primary DCIS samples together with 12 matched MIND-PDX WES profiles as well as 60 matched Copy Number Variation (CNV) MIND-PDX profiles. Together these data revealed multiple biomarkers related to invasive progression, including factors such as high grade, solid growth, a high copy number aberrations burden, HER2, PTK6 & MYC amplifications and a high Ki67. On top of this we used whole mount imaging of the injected mammary glands extracted from our MIND-PDX models, showing two distinct growth patterns correlated with invasion. And as this is all done in the context of the PRECISION consortium this allows us to confirm and validate our findings in larger sequencing and imaging efforts of human samples. We have also successfully passaged 42 MIND-PDX models which showed minimal changes in pheno- and genotype over time indicating invasive behavior is an intrinsic phenotype of DCIS with minimal evolution, supporting a multiclonal evolution model. Moreover, this provided a collection of 19 stable sequentially transplantable DCIS MIND models including Luminal A, Luminal B, ER+/HER2+ and ER-/HER2+ models. Ultimately these models can be used to validate the biomarkers found to be related to invasive progression, as an example we proved the direct role of HER2 overexpression in invasive progression by inhibiting the HER2 receptor or by overexpressing HER2. In conclusion all this data together enabled us to create a well-characterized biobank of DCIS models with the unique opportunity to follow the natural progression, sequentially transplant 42 models, find genomic and transcriptomic profiles related to high risk DCIS and manipulate gene expression to validate the role of genes in DCIS progression. Citation Format: Stefan J. Hutten, Roebi de Bruijn, Catrin Lutz, Madelon Badoux, Timo Eijkman, Xue Chao, Marta Ciwinska, Andrea Herencia-Ropero, Petra Kristel, Lennart Mulder, Joyce Sanders, Mathilde Almekinders, Alba Llop-Gueverra, Helen R. Davies, Fariba Behbod, Serena Nik-Zainal, Violeta Serra, Jacco van Rheenen, Esther H. Lips, Lodewyk F.A. Wessels, Jelle Wesseling, Colinda Scheele, Jos Jonkers. A living biobank of patient-derived ductal carcinoma in situ (DCIS) Mouse-INtraDuctal (MIND) xenografts identifies multiple risk factors of invasive progression [abstract]. In: Proceedings of the AACR Special Conference on Rethinking DCIS: An Opportunity for Prevention?; 2022 Sep 8-11; Philadelphia, PA. Philadelphia (PA): AACR; Can Prev Res 2022;15(12 Suppl_1): Abstract nr PR006.

Introduction: Ductal carcinoma in situ (DCIS) is considered a non-obligate precursor of invasive ductal carcinoma. With the aim of preventing a subsequent invasive cancer, all DCIS lesions are currently treated with surgical excision often supplemented with radiotherapy (RT). To prevent DCIS over- or undertreatment, a reliable marker of DCIS invasiveness risk is urgently needed. Methods: We studied two large DCIS cohorts: the Sloane cohort, a prospective breast screening cohort from the UK (median follow-up of 11 years), and a Dutch population-based cohort (NKI, median follow-up of 13 years). FFPE tissue specimens from patients with pure primary DCIS after breast-conserving surgery (BCS) +/- RT that did develop a subsequent ipsilateral event (DCIS or invasive) were considered as cases, whereas patients that did not develop any form of recurrence up to the last follow-up or death were considered as controls. We performed copy number analysis (CNA) and RNAseq analysis on 229 cases (80 DCIS only recurrences) and 344 controls. Results: DCIS was classified into the PAM50 subtypes using RNAseq data which revealed an enrichment of luminal A phenotype in DCIS that did not recur (P = 0.01, Fisher Exact test). No single copy number aberration was more common in cases compared to controls. RNAseq data did not reveal any genes significantly over/under-expressed in cases versus controls after FDR correction. However, by limiting the analysis to samples that had not had RT and excluding pure DCIS recurrences, we could develop a penalized Cox model from RNAseq data. The model was trained on weighted samples (to correct for the biased sampling of the case-control dataset) from the NKI series with double loop cross-validation. The genes were selected using the Elastic net framework of penalization. Using this predicted hazard ratio, the samples were split into high, medium, and low-risk quantiles, with a recurrence risk of 23%, 7% and 2%, respectively at 5 years (p = 10-10, Wald test). The NKI-trained predictor was independently validated in the Sloane No RT no DCIS recurrence cohort (p = 0.02, Wald test). GSEA analysis revealed proliferation hallmarks enriched in the recurrence predictor (FDR = 0.058). The RNAseq predictor was more predictive of recurrence than PAM50, clinical features (Grade, Her2 and ER) and the 12-gene Oncotype DCIS score (p < 0.001, permutation test using the Wald statistic) in both the NKI and Sloane series. Conclusion: Genomic profiling of two independent series of DCIS with outcome data did not reveal any clear associations with recurrence until analysis was limited to a set of samples who had not had radiotherapy and DCIS recurrences were excluded. We then identified an RNAseq-based classifier that could differentiate primary DCIS in low-, medium-, and high-risk groups, and validated it in an independent cohort. This classifier, if validated in other datasets, will allow us to identify women who do not need intensive treatment for their DCIS. Citation Format: Maria Roman Escorza, Michael Sheinman, Tycho Bismeijer, Ahmed A. Ahmed, Vandna Shah, Jeffrey R. Marks, Lorraine M. King, Anargyros Megalios, Lindy L. Visser, Marlous Hoogstrat, Helen R. Davies, Tapsi Kumar, Deborah Collyar, Hilary Stobart, Sarah Pinder, Nicholas N. Navin, Andrew Futreal, Serena Nik-Zainal, E. Shelley Hwang, Esther H. Lips, Alastair Thompson, Lodewyk F.A. Wessels, Jelle Wesseling, Elinor J. Sawyer. Genomic predictor can discriminate between high- and low-risk DCIS [abstract]. In: Proceedings of the AACR Special Conference on Rethinking DCIS: An Opportunity for Prevention?; 2022 Sep 8-11; Philadelphia, PA. Philadelphia (PA): AACR; Can Prev Res 2022;15(12 Suppl_1): Abstract nr PR002.

Ductal carcinoma in situ (DCIS) is the most common form of preinvasive breast cancer and, despite treatment, a small fraction (5-10%) of DCIS patients develop subsequent invasive disease. A fundamental biologic question is whether the invasive disease arises from tumor cells in the initial DCIS or represents new unrelated disease. To address this question, we performed genomic analyses on the initial DCIS lesion and paired invasive recurrent tumors in 95 patients together with single-cell DNA sequencing in a subset of cases. Our data show that in 75% of cases the invasive recurrence was clonally related to the initial DCIS, suggesting that tumor cells were not eliminated during the initial treatment. Surprisingly, however, 18% were clonally unrelated to the DCIS, representing new independent lineages and 7% of cases were ambiguous. This knowledge is essential for accurate risk evaluation of DCIS, treatment de-escalation strategies and the identification of predictive biomarkers.

Ductal carcinoma in situ (DCIS) is considered a potential precursor of invasive breast carcinoma (IBC). Studies aiming to find markers involved in DCIS progression generally have compared characteristics of IBC lesions with those of adjacent synchronous DCIS lesions. The question remains whether synchronous DCIS and IBC comparisons are a good surrogate for primary DCIS and subsequent IBC. In this study, we compared both primary DCIS and synchronous DCIS with the associated IBC lesion, on the basis of immunohistochemical marker expression. Immunohistochemical analysis of ER, PR, HER2, p53, and cyclo-oxygenase 2 (COX-2) was performed for 143 primary DCIS and subsequent IBC lesions, including 81 IBC lesions with synchronous DCIS. Agreement between DCIS and IBC was assessed using kappa, and symmetry tests were performed to assess the pattern in marker conversion. The primary DCIS and subsequent IBC more often showed discordant marker expression than synchronous DCIS and IBC. Strikingly, 18 of 49 (36%) women with HER2-positive primary DCIS developed an HER2-negative IBC. Such a difference in HER2 expression was not observed when comparing synchronous DCIS and IBC. The frequency of discordant marker expression did not increase with longer time between primary DCIS and IBC. In conclusion, comparison of primary DCIS and subsequent IBC yields different results than a comparison of synchronous DCIS and IBC, in particular with regard to HER2 status. To gain more insight into the progression of DCIS to IBC, it is essential to focus on the relationship between primary DCIS and subsequent IBC, rather than comparing IBC with synchronous DCIS.

Purpose: BRCA1-deficient breast cancers carry a specific DNA copy-number signature (“BRCA1-like”) and are hypersensitive to DNA double-strand break (DSB) inducing compounds. Here, we explored whether (i) EZH2 is overexpressed in human BRCA1-deficient breast tumors and might predict sensitivity to DSB-inducing drugs; (ii) EZH2 inhibition potentiates cisplatin efficacy in Brca1-deficient murine mammary tumors. Experimental Design: EZH2 expression was analyzed in 497 breast cancers using IHC or RNA sequencing. We classified 370 tumors by copy-number profiles as BRCA1-like or non-BRCA1–like and examined its association with EZH2 expression. Additionally, we assessed BRCA1 loss through mutation or promoter methylation status and investigated the predictive value of EZH2 expression in a study population of breast cancer patients treated with adjuvant high-dose platinum-based chemotherapy compared with standard anthracycline-based chemotherapy. To explore whether EZH2 inhibition by GSK126 enhances sensitivity to platinum drugs in EZH2-overexpressing breast cancers we used a Brca1-deficient mouse model. Results: The highest EZH2 expression was found in BRCA1-associated tumors harboring a BRCA1 mutation, BRCA1-promoter methylation or were classified as BRCA1 like. We observed a greater benefit from high-dose platinum-based chemotherapy in BRCA1-like and non-BRCA1–like patients with high EZH2 expression. Combined treatment with the EZH2 inhibitor GSK126 and cisplatin decreased cell proliferation and improved survival in Brca1-deficient mice in comparison with single agents. Conclusions: Our findings demonstrate that EZH2 is expressed at significantly higher levels in BRCA1-deficient breast cancers. EZH2 overexpression can identify patients with breast cancer who benefit significantly from intensified DSB-inducing platinum-based chemotherapy independent of BRCA1-like status. EZH2 inhibition improves the antitumor effect of platinum drugs in Brca1-deficient breast tumors in vivo.

Background Observational studies in metastatic breast cancer (MBC) show that long-term overall survival (OS) is associated with limited tumor burden, or oligo-MBC (OMBC). However, a uniform definition of OMBC is lacking. In this real-world nationwide cohort, we aimed to define the optimal OMBC threshold and factors associated with survival in patients with OMBC. Methods 3535 patients aged younger than 80 years at diagnosis of de novo MBC in the Netherlands between January 2000 and December 2007 were included. Detailed clinical, therapy, and outcome data were collected from medical records of a sample of the patients. Using inverse-sampling-probability weighting, the analysis cohort (n = 3447) was constructed. We assessed OS according to number of metastases at diagnosis to determine the optimal OMBC threshold. Next, we applied Cox regression models with inverse-sampling-probability weighting to study associations with OS and progression-free survival in OMBC. All statistical tests were 2-sided. Results Compared with more than 5 distant metastases, adjusted hazard ratios for OS (with 95% confidence interval [CI] based on robust standard errors) for 1, 2-3, and 4-5 metastases were 0.70 (95% CI = 0.52 to 0.96), 0.63 (95% CI = 0.45 to 0.89), and 0.91 (95% CI = 0.61 to 1.37), respectively. Ten-year OS estimates for patients with no more than 3 vs more than 3 metastases were 14.9% and 3.4% (P Conclusion OMBC defined as MBC limited to 1-3 metastases was associated with favorable OS. In OMBC, local therapy of metastases was associated with better OS, particularly if patients were premenopausal or perimenopausal without lung metastases.

Previously, we developed breast cancerThe detection rate of theThe newly trained classifiers detected most