Abstract Ductal carcinoma in situ is a non-obligate precursor lesion of breast cancer. Often detected by mammography, most cases are managed through surgical and/or radiotherapy approaches. Today, it is not possible to predict which patients will progress to invasive disease. Here, we evaluate high-depth whole-genome sequenced ductal carcinoma in situ, enriched for high-grade clinical lesions, to understand whether deep WGS could reveal biological insights and/or personalized therapeutic vulnerabilities that may be targetable. We find genomic locations that are likely susceptible to producing the initiating lesion for structural variations prone to subsequent evolution, termed SHOREs. We additionally highlight individualized therapeutic potential that would otherwise not be appreciable without whole genome sequencing. We posit that holistic whole genome sequencing profiling could offer a more precise stratification approach, discerning higher-risk cases for prospective clinical studies on personalized therapies, from truly low-risk cases suitable for active monitoring.

Early prediction of the response to neoadjuvant chemotherapy (NAC) enables tailoring treatment strategies to the specific needs of individual breast cancer patients. Circulating tumor DNA (ctDNA) has shown to be a prognostic factor for response on NAC during treatment. However, at this point in time mostly tumor-informed ctDNA detection methods are used which are costly, have relatively long turnaround times and are subsequently potentially less feasible for widespread clinical application. In this study, we investigated four tumor-agnostic methods to determine their ability to accurately detect circulating tumor DNA (ctDNA) at baseline. These methods were the Oncomine Breast cell free DNA (cfDNA) NGS panel, the LINE-1 sequencing assay mFAST-SeqS, shallow whole genome sequencing and the genome-wide methylation profiling assay MeD-Seq. In total 40 patients with triple negative or luminal B breast cancer were included and cell free DNA (cfDNA) from plasma before the start of NAC was analyzed with the four assays. We detected ctDNA in 3/24 (12.5

BACKGROUND:Ductal Carcinoma In Situ (DCIS) can progress to ipsilateral invasive breast cancer (IBC) but over 75% of DCIS lesions do not progress if untreated. Currently, DCIS that might progress to IBC cannot reliably be identified. Therefore, most patients with DCIS undergo treatment resembling IBC. To facilitate identification of low-risk DCIS, we developed deep learning models using histology whole-slide images (WSIs) and clinico-pathological data. METHODS:We predicted invasive recurrence in patients with primary, pure DCIS treated with breast-conserving surgery using clinical Cox proportional hazards models and deep learning. Deep learning models were trained end-to-end with only WSIs or in combination with clinical data (integrative). We employed nested k-fold cross-validation (k = 5) on a Dutch multicentre dataset (n = 558). Models were also tested on the UK-based Sloane dataset (n = 94). FINDINGS:Evaluated over 20 years on the Dutch dataset, deep learning models using only WSIs effectively stratified patients into low-risk (no recurrence) and high-risk (invasive recurrence) groups (negative predictive value (NPV) = 0.79 (95% CI: 0.74-0.83); hazard ratio (HR) = 4.48 (95% CI: 3.41-5.88, p < 0.0001); area under the receiver operating characteristic curve (AUC) = 0.75 (95% CI: 0.70-0.79)). Integrative models achieved similar results with slightly enhanced hazard ratios compared to the image-only models (NPV = 0.77 (95% CI 0.73-0.82); HR = 4.85 (95% CI 3.65-6.45, p < 0.0001); AUC = 0.75 (95% CI 0.7-0.79)). In contrast, clinical models were borderline significant (NPV = 0.64 (95% CI 0.59-0.69); HR = 1.37 (95% CI 1.03-1.81, p = 0.041); AUC = 0.57 (95% CI 0.52-0.62)). Furthermore, external validation of the models was unsuccessful, limited by the small size and low number of cases (22/94) in our external dataset, WSI quality, as well as the lack of well-annotated datasets that allow robust validation. INTERPRETATION:Deep learning models using routinely processed WSIs hold promise for DCIS risk stratification, while the benefits of integrating clinical data merit further investigation. Obtaining a larger, high-quality external multicentre dataset would be highly valuable, as successful generalisation of these models could demonstrate their potential to reduce overtreatment in DCIS by enabling active surveillance for women at low risk. FUNDING:Cancer Research UK, the Dutch Cancer Society (KWF), and the Dutch Ministry of Health, Welfare and Sport.

The current clinical paradigm around Ductal Carcinoma in Situ (DCIS) is that it consists of malignant cells confined to the breast ducts, and therefore cannot metastasize. Nonetheless, several studies have reported DCIS with metastasis in the sentinel lymph node (SN+). For accurate risk communication and management, we aimed to assess to what extent registered “metastatic spread” in DCIS could be explained by limitations in registration or missed invasive breast cancer at time of diagnosis. Data from the nationwide cancer registry and national pathology database on women diagnosed with DCIS SN+ in the Netherlands, spanning from 2005 to 2020, was curated and reviewed, taking into account their histories of prior DCIS, invasive breast cancer, or other malignancies. Cases were excluded from further analysis if pathology data indicated registration errors, DCIS mixed with other lesion types, or diagnostic uncertainties. Next, hematoxylin and eosin-stained tissue slides of eligible DCIS SN+ cases were independently reviewed by two pathologists to assess the presence of invasive breast cancer and SN status. Additional immunohistochemical staining (CK 5/6 or CK 8/18) was performed when findings were unclear. Inter-observer agreement was evaluated using the linearly weighted Kappa statistic. A total of 30, 863 patients were identified with a DCIS diagnosis between 2005 and 2020, of which 16, 070 (52%) underwent SN biopsy according to cancer registry data. SN+ was registered in 454 (3 %) patients: 47 (10%) had macrometastases (>2 mm), 78 (17%) had micrometastases (>0.2 to <= 2 mm), and 329 (73%) were positive for isolated tumor cells (<= 0.2 mm). Out of the 454 registered DCIS SN+ cases, 273 (60%) were excluded from further investigation based on pathology data, including registration errors (n=44), DCIS mixed with other lesions (n=147), and diagnostic uncertainties (n=82). Tissue material of 46 (37%) out of 125 registered cases with macro- and micrometastases was reviewed. Observer variability in assessing the presence of invasive breast cancer was high (k= 0.14; 95% CI 0.08 - 0.34; p = 0.12) and additional CK 5/6 staining was requested for 38 cases. Two cases were classified as primary invasive breast cancer, 25 as pure DCIS, while 19 remained inconclusive, due to variation in tissue sections or suboptimal tissue quality. In six cases the SN was scored as negative by pathology revision, likely due to tissue section variability. Our study indicates that DCIS in itself has minimal to no metastatic potential. Ongoing clonality analysis of 9 cases with macrometastases aims to determine whether the SN metastases are clonally related to the DCIS lesions. Merle van Leeuwen, Sandra van den Belt- Dusebout, Petra Kristel, Lennart Mulder, Joyce Sanders, Carmen Vlahu, Esther H. Lips, Jelle Wesseling. Ductal carcinoma in situ: Potential to metastasize? A nationwide cancer registry-based study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2332.

Ductal carcinoma in situ (DCIS) is a non-obligate precursor to invasive breast cancer, but distinguishing patients with harmless from potentially hazardous DCIS remains a challenge. Consequently, a cornerstone of DCIS research is finding prognostic biomarkers. One such recent biomarker is adipocyte hypertrophy, which has been shown to be prognostic of ipsilateral invasive breast cancer (iIBC) in post-menopausal women with primary DCIS. However, little is known about the correlation between adipocyte size, clinical factors and mammographic density. Archival hematoxylin and eosin- stained breast biopsy and excision specimens were retrieved from 669 women diagnosed with primary DCIS between 2000 and 2020 treated at the Netherlands Cancer Institute. These slides were digitized whereafter a machine-learning algorithm using HALO®, was applied to retrieve adipocyte size. Radiology reports were obtained to extract mammographic BI-RADS density. Age at diagnosis, body mass index (BMI), menopausal status and information on comorbidities were extracted from electronic patient records. Associations between adipocyte size and clinical factors and mammographic density were investigated using univariable and multivariable linear regression models. Using a clinically relevant cutoff point for adipocyte size, multivariable logistic regression was performed. The median age at primary DCIS diagnosis was 55 years (interquartile range (IQR): 49.0 -63.0) and most DCIS lesions were grade 3 (40.9%). The median BMI was 24.1 (IQR: 21.9 - 27.1). The majority of women were post-menopausal (47.8%) and had dense breasts (53.8%). Significant positive correlations were found between adipocyte size and age, BMI and all metabolic comorbidities with the exception of smoking. Strong negative correlations were found between adipocyte size and mammographic density categories C and D. In univariable linear models with age and BMI, metabolic risk was able to further differentiate between patients with and without adipocyte hypertrophy beyond age and BMI. This was confirmed using multivariable logistic regression comparing models adjusted and unadjusted for metabolic risk. BMI is the strongest predictor of adipocyte hypertrophy in DCIS patients, however metabolic risk further differentiates between patients with and without adipocyte hypertrophy. Future work should be done on collecting long term follow-up and ascertaining subsequent breast cancer in these patients. If successful, BMI and metabolic risk can potentially be incorporated into risk prediction models for subsequent invasive breast cancer. Charlotta V. Mulder, Mathilde Almekinders, Renaud Tissier, Lennart Mulder, Petra Kristel, Esther Lips, Marjanka Schmidt, Jelle Wesseling. Metabolic risk is an important determinant of adipocyte hypertrophy beyond age, BMI and breast density in patients with ductal carcinoma in situ [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6215.

Upon the inception of population-based screening programs, the incidence of ductal carcinoma in situ (DCIS) increased 6-fold. DCIS is a non-obligate precursor lesion of invasive breast cancer (IBC), of which the majority does not progress. This implies that women with non-progressive DCIS are overtreated. Within the Cancer Grand Challenge PRECISION project, we identified prognostic markers, including immunohistochemical, morphological markers, and an RNA-seq classifier holding promise in distinguishing progressive from non-progressive DCIS. We now aim to validate these externally and build a clinical prediction model. We conducted a case-cohort study nested in a Dutch population-based cohort of 8987 patients with DCIS treated with breast-conserving surgery (BCS) between 2005 and 2015. Women who subsequently developed ipsilateral IBC were considered cases and controls were those who did not. Our study population consisted of a random sample of 10.7% of the full cohort as our subcohort, and all other additional cases, totaling 1237 women (cases n=308; controls n=929). Tissue blocks were requested for all women, of which 940 were received and eligible for immunohistochemical analysis of ER, HER2, COX-2, Ki67 and P16 1. A new H&E slide was cut for the measurement of adipocyte size 2, level of periductal fibrosis 3 and a ductal morphometric analysis 4. RNA-sequencing was carried out on a selection of BCS-only patients (cases n = 100; controls n = 100). To assess the association of each marker and iIBC risk, prentice-weighted Cox proportional hazards models, with age as the underlying time variable, were implemented. Multivariable models were also implemented and adjusted for treatment, margins status, DCIS grade and size. To account for multiple comparisons, a false discovery rate (FDR) < 0.05 was used to define statistical significance. The median follow-up time for the case-cohort was 7.2 years (interquartile range (IQR): 5.2-10.1). The age at primary DCIS diagnosis was similar for breast cancer cases and controls (58.0, IQR: 51.0-64.0 vs 58.0, IQR: 51.0-66.0), as was the size of the tumor (p = 0.06). After final surgery, 10.4% of cases and 6.7% of controls had involved margins of <2mm (p = 0.06). Cases were treated more frequently with radiotherapy than controls (76.0% vs 68.4%, p = 0.01) and were more often high grade DCIS (46.4% vs 38.0%, p = 0.04). In December, we will present the validation of immunohistochemical, morphological markers, and an RNA-seq classifier for predicting subsequent ipsilateral IBC after DCIS and the benefit of using these markers in a clinical prediction model. Ultimately, this will aid individual risk stratification of women with primary DCIS, and can be used to diminish the current overtreatment of harmless, low-risk DCIS. References 1. Visser, L. L. et al. Clinicopathological risk factors for an invasive breast cancer recurrence after ductal carcinoma in situ-a nested case-control study. Clinical Cancer Research 24, 3593–3601 (2018). 2. Almekinders, M. M. M. et al. Breast adipocyte size associates with ipsilateral invasive breast cancer risk after ductal carcinoma in situ. NPJ Breast Cancer 7, (2021). 3. Visser, L. L. et al. Predictors of an invasive breast cancer recurrence after DCIS: A Systematic Review and Meta-analyses. Cancer Epidemiology Biomarkers and Prevention vol. 28 835–845. 4. Sobral-Leite, M. et al. Articial intelligence-based morphometric signature to identify ductal carcinoma in situ with low risk of progression to invasive breast cancer. doi:10.21203/rs.3.rs-3639521/v1. Citation Format: Charlotta Mulder, Will Harley, Petra Kristel, Lennart Mulder, Sten Cornelissen, Renee Menezes, Michael Schaapveld, Marjanka K. Schmidt, Jelle Wesseling, Esther H. Lips. A clinical risk prediction model for subsequent invasive breast cancer after ductal carcinoma in situ [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P3-05-28.

Background: Benign breast disease (BBD) is commonly detected in women participating in breast cancer screening programs and comprises a diverse group of lesions. The clinical significance of BBD lies in its association with an increased risk of developing breast cancer, which depends on the histological subtype. Calcifications, frequently observed in mammographic screenings, are critical in the detection and diagnosis of both benign and malignant breast conditions. While many calcifications are benign, some patterns are indicative of ductal carcinoma in situ (DCIS) or invasive breast cancer (IBC). Better characterization of breast cancer risk among women with BBD, considering both clinical and molecular findings, can improve surveillance, early diagnosis, and survival. This study aims to identify mammographic and chemical characteristics of calcifications that are associated with subsequent development of DCIS or IBC. Methods: A matched case-control study was conducted of women diagnosed with BBD at the Netherlands Cancer Institute and Albert Schweitzer Hospital between 2004 and 2023. Cases (n=65) were women with BBD who developed ipsilateral DCIS or IBC ≥ 6 months after a first BBD diagnosis, whereas controls (n=244) were BBD patients who did not develop subsequent ipsilateral DCIS or IBC during the follow-up (FU) duration of their matching cases. Additionally, controls were matched based on the year and age at the time of BBD diagnosis. Patient characteristics (e.g. age at diagnosis, vital status) and characteristics of both the benign lesions and subsequent malignant lesions were extracted from pathology reports using text searches and Palga codes. Mammographic lesion types (e.g. calcifications, masses, architectural distortion, asymmetries) were extracted from radiology reports. Qualitative mammographic features including calcification morphology and distribution were extracted from mammograms by two researchers and a trained radiologist. Quantitative mammographic features including breast density score and calcification cluster size and number will be extracted using TRANSPARA 2.0, an radiology artificial intelligence decision support system. In a subset of cases (n = 29) and controls (n=59) chemical characteristics were measured using infrared and Raman spectroscopy. Results: The baseline comparison of mammographic qualitative features comprised 65 cases and 244 matched controls.Median age at BBD diagnosis was 51 years (range 35-80). Median FU from BBD diagnosis to DCIS or IBC was 6 years (range 1-17). Most cases and controls had non-proliferative BBD (89% and 94%) rather than proliferative BBD. Among cases, 19 developed DCIS while 46 developed IBC. While cases and controls showed comparable proportions of mammographic lesions, calcifications were more prevalent among cases (48.0% vs. 34.0%), approaching statistical significance (p = 0.06). Significant differences in calcification morphology were observed (p = 0.009), with cases more likely to display fine pleomorphic calcifications (23 % vs. 8.5%). The distribution of calcifications was similar between cases and controls (p = 0.49). Multivariate-adjusted conditional regression models showed an odds ratio (OR) of 1.7 (95% CI: 0.9-3.0) for the association between calcification presence and DCIS/IBC development, albeit with considerable uncertainty. Presence of suspicious calcification morphologies (amorphous, fine pleomorphic, linear) suggested an OR of 3.0 (95% CI: 0.9-10.0) compared to benign morphology. Conclusions: The trends observed in this study suggest potential prognostic value of calcification morphology in women with BBD. Additional results on quantitative mammographic features and chemical characteristics of cases and controls will be presented at the conference. Citation Format: Merle van Leeuwen, Sandra van den Belt-Dusebout, Jia Ning Zhuchen, Shannon Doyle, Petra Kristel, Lennart Mulder, Jayakrupakar Nallala, Pieter Westenend, Nick Stone, Esther Lips, Ritse Mann, Jelle Wesseling. Calcification characteristics in women with benign breast disease and the risk of subsequent breast cancer: a case-control study [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P3-03-29.

Background: The current clinical paradigm around Ductal Carcinoma in Situ (DCIS) is that it consists of malignant cells confined to the breast ducts, and therefore cannot metastasize. Nonetheless, several studies have reported DCIS with metastasis in the sentinel lymph node. For accurate risk communication and management, we aimed to assess to what extent registered “metastatic spread” in DCIS could be explained by limitations in registration or missed invasive breast cancer at time of diagnosis. Methods: Nationwide data on women diagnosed with DCIS and a positive sentinel node (DCIS SN+) between 2005 and 2021 were obtained from the Netherlands Cancer Registry (NCR) and the Dutch Nationwide Pathology Databank (Palga). The incidence of registered DCIS SN+ and the size of the metastasis was determined with data from NCR. Pathology data of the primary DCIS diagnosis was thoroughly reviewed, with all known corresponding history pathology reports of in-situ or invasive breast cancer or unknown primary. Cases were excluded from further analysis if PALGA data indicated registration errors, DCIS mixed with other types of lesions, or diagnostic uncertainties such as suspicion of microinvasion, uncertain sentinel node status, poor tissue quality or positive surgical margins. Next, hematoxylin and eosin stained tissue slides of eligible DCIS SN+ cases were independently reviewed by two expert breast pathologists to assess the presence of (micro)invasion and sentinel node status. Presence of invasion and sentinel node positivity was scored as yes, uncertain, no or not applicable. For cases scored as uncertain, additional immunohistochemical (IHC) stainings with cytokeratin 5/6 or 8/18 were used. Agreement between the two pathologists was assessed using the linearly weighted Kappa statistic. Results: A total of 30,863 patients were identified with a DCIS diagnosis between 2005 and 2020, of which 16,070 (52.1%) underwent sentinel lymph node biopsy according to NCR data. SN+ was identified in 454 (2.8 %) patients: 47 (10 %) had macrometastases (>2 mm), 78 (17%) had micrometastases (>0.2 - <= 2 mm), and 329 (73%) were positive for isolated tumor cells (ITCs) (<= 0.2 mm). Out of the 454 registered DCIS SN+ cases, 273 (60%) were excluded from further investigation based on pathology data from Palga, based on registration errors (n=44), due to the presence of other lesions (n=147), or diagnostic uncertainties (n=82). There was no significant difference in reasons for exclusion between micrometastases, macrometastases or ITCs (p = 0.36). Tissue material of 47 out of 181 DCIS cases with macro- and micrometastases were reviewed by the pathologists. Initial agreement on sentinel node status was weak with a kappa statistic of 0.33 (95% CI 0.15 – 0.52; p = 0.003. Agreement on the presence of an invasive component was minimal with a kappa statistic of 0.14 (95% CI 0.08 – 0.34 ; p = 0.12). The most frequent discrepancies were between ‘uncertain’ and ‘no’ scorings. After reaching consensus, three cases were scored as having an invasive component, and additional IHC staining was requested for 40 cases due to suspicion of the presence of (micro)invasion. Review results of these 40 cases will be presented at the conference. Conclusions: Our study offers a nuanced understanding of DCIS SN+ cases, suggesting that while these cases pose diagnostic challenges, the metastatic potential of pure DCIS remains low. Citation Format: Merle van Leeuwen, Sandra van den Belt-Dusebout, Petra Kristel, Lennart Mulder, Joyce Sanders, Carmen Vlahu, Esther Lips, Jelle Wesseling. Does Ductal Carcinoma in Situ have metastatic potential? A nationwide cancer registry-based study of Ductal Carcinoma in Situ with sentinel lymph node positivity [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P5-12-13.

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).

Supplementary Figure S1 and Supplementary Tables S1-S3.

Background Invasive breast cancer patients are increasingly being treated with neoadjuvant chemotherapy; however, only a fraction of the patients respond to it completely. To prevent overtreatment, there is an urgent need for biomarkers to predict treatment response before administering the therapy. Methods In this retrospective study, we developed hypothesis-driven interpretable biomarkers based on deep learning, to predict the pathological complete response (pCR, i.e., the absence of tumor cells in the surgical resection specimens) to neoadjuvant chemotherapy solely using digital pathology H&E images of pre-treatment breast biopsies. Our approach consists of two steps: First, we use deep learning to characterize aspects of the tumor micro-environment by detecting mitoses and segmenting tissue into several morphology compartments including tumor, lymphocytes and stroma. Second, we derive computational biomarkers from the segmentation and detection output to encode slide-level relationships of components of the tumor microenvironment, such as tumor and mitoses, stroma, and tumor infiltrating lymphocytes (TILs). Results 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 and performed external independent validation on n = 126 patients from a public dataset. We report the predictive value of the investigated biomarkers for predicting pCR with areas under the receiver operating characteristic curve between 0.66 and 0.88 across the tested cohorts. Conclusion The proposed computational biomarkers predict pCR, but will require more evaluation and finetuning for clinical application. Our results further corroborate the potential role of deep learning to automate TILs quantification, and their predictive value in breast cancer neoadjuvant treatment planning, along with automated mitoses quantification. We made our method publicly available to extract segmentation-based biomarkers for research purposes.

BackgroundStudies 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.MethodsPlatelet 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.ResultsBoth 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.ConclusionsWe 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.

Exploratory analyses of high-dose alkylating chemotherapy trials have suggested that BRCA1 or BRCA2-pathway altered (BRCA-altered) breast cancer might be particularly sensitive to this type of treatment. In this study, patients with BRCA-altered tumors who had received three initial courses of dose-dense doxorubicin and cyclophosphamide (ddAC), were randomized between a fourth ddAC course followed by high-dose carboplatin-thiotepa-cyclophosphamide or conventional chemotherapy (initially ddAC only or ddAC-capecitabine/decetaxel [CD] depending on MRI response, after amendment ddAC-carboplatin/paclitaxel [CP] for everyone). The primary endpoint was the neoadjuvant response index (NRI). Secondary endpoints included recurrence-free survival (RFS) and overall survival (OS). In total, 122 patients were randomized. No difference in NRI-score distribution ( p = 0.41) was found. A statistically non-significant RFS difference was found (HR 0.54; 95% CI 0.23–1.25; p = 0.15). Exploratory RFS analyses showed benefit in stage III ( n = 35; HR 0.16; 95% CI 0.03–0.75), but not stage II ( n = 86; HR 1.00; 95% CI 0.30–3.30) patients. For stage III, 4-year RFS was 46% (95% CI 24–87%), 71% (95% CI 48–100%) and 88% (95% CI 74–100%), for ddAC/ddAC-CD, ddAC-CP and high-dose chemotherapy, respectively. No significant differences were found between high-dose and conventional chemotherapy in stage II-III, triple-negative, BRCA-altered breast cancer patients. Further research is needed to establish if there are patients with stage III, triple negative BRCA-altered breast cancer for whom outcomes can be improved with high-dose alkylating chemotherapy or whether the current standard neoadjuvant therapy including carboplatin and an immune checkpoint inhibitor is sufficient. Trial Registration: NCT01057069.

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

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

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.

Background: The advent of breast screening has led to a 4-fold increase in the diagnosis of ductal carcinoma in situ (DCIS). Studies following the clinical outcomes of patients show variable progression free survival rates, with only up to 35% of patients progressing to invasive disease without treatment. This highlights the need to find a biomarker to accurately predict which DCIS lesions will recur as invasive tumors. Epigenetics changes are events which occur early in tumorigenesis, this makes DNA methylation a potential biomarker of DCIS progression. However, a lack of DCIS methylation profiling with long term follow-up data exists. This study investigates genome-wide methylation profiles in women with primary DCIS and associates the data with their overall recurrence free survival. Methods: DCIS was macrodissected from 89 formalin-fixed paraffin embedded (FFPE) to extract tumor enriched DNA from patients with DCIS and long term follow up. 39 women had developed an ipsilateral invasive recurrence (classified as cases) and 50 had no evidence of recurrent disease (classified as controls). Genome wide methylation was assessed using the human methylation EPIC BeadChip which, interrogates over 850,000 methylation sites. Data was assessed for quality both at the sample and probe level by the wateRmelon. Further analysis was performed in ChAMP to identify differentially methylated regions (DMR) and by DMRcate packages in R to identify variably methylated regions (VMR). Genes annotated in the most significant VMRs were analysed through Metascape, a web-based tool to perform functional gene set enrichment analysis (GSEA). A cox proportional hazards model was used to calculate the association between the methylation of the VMRs and recurrence free survival. This model was adjusted for DCIS receptor status and grade. Results: 59 samples passed data quality assessment (35 controls and 24 cases). 10 differentially methylated regions were identified. The most significant of which, was a hypomethylated region on chromosome 4, containing CDKL2 (p = 0.001), known to promote the epithelial-mesenchymal transition in breast cancer progression. 5813 VMRs were identified across the genome, (P-value range between 0 and 10-321). 82% of the VMRs aligned to the body of the gene or the surrounding regulatory features. GSEA revealed that VMRs were predominantly involved in pathways involved in cell adhesion (GO:0007156) Assessment of the significant VMRs by COX proportional hazards model showed that a VMR on chromosome 6p was associated with the development of invasive disease after adjusting for oestrogen receptor, human epidermal growth factor 2 status and grade (p=0.001). Conclusions: This preliminary study shows altered sites of methylation could be observed across the genome, in DCIS. The function of the VMRs is currently being investigated to understand how methylation in this region predisposes to invasive recurrence of DCIS. Correlation of methylation status and RNA expression data will be used to understand the biological relevance. Citation Format: Vandna Shah, Maria Roman-Escorza, Karen Clements Clements, Lennart Mulder, Esther H. Lips, Jelle Wesseling, Sarah Pinder, Alastair M. Thompson, Elinor J. Sawyer. Identification of methylated regions in ductal carcinoma in situ and association with disease 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 A016.

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.