Predicting treatment outcome using kinome activity profiling in HER2+ breast cancer biopsies.

In this study, we measured the kinase activity profiles of 32 pre-treatment tumor 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 allowed us to link kinase activity to treatment response. Overall, high kinase activity in the HER2-pathway was associated with good treatment outcome. We found eleven kinases differentially regulated between treatment outcome groups, including well-known players in therapy resistance, such as p38a, ERK, and FAK, and an unreported one, namely MARK1. 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 develops within clonal fields of mutant cells in morphologically normal ducts.

Mutations are abundantly present in tissues of healthy individuals, including the breast epithelium. Yet it remains unknown whether mutant cells directly induce lesion formation or first spread, leading to a field of mutant cells that is predisposed towards lesion formation. To study the clonal and spatial relationships between morphologically normal breast epithelium adjacent to pre-cancerous lesions, we developed a three-dimensional (3D) imaging pipeline combined with spatially resolved genomics on archival, formalin-fixed breast tissue with the non-obligate breast cancer precursor ductal carcinoma in situ (DCIS). Using this 3D image-guided characterization method, we built high-resolution spatial maps of DNA copy number aberration (CNA) profiles within the DCIS lesion and the surrounding normal mammary ducts. We show that the local heterogeneity within a DCIS lesion is limited. However, by mapping the CNA profiles back onto the 3D reconstructed ductal subtree, we find that in eight out of 16 cases the healthy epithelium adjacent to the DCIS lesions has overlapping structural variations with the CNA profile of the DCIS. Together, our study indicates that pre-malignant breast transformations frequently develop within mutant clonal fields of morphologically normal-looking ducts. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Application of deep learning on mammographies to discriminate between low and high-risk DCIS for patient participation in active surveillance trials.

BACKGROUND: Ductal Carcinoma In Situ (DCIS) can progress to invasive breast cancer, but most DCIS lesions never will. Therefore, four clinical trials (COMET, LORIS, LORETTA, AND LORD) test whether active surveillance for women with low-risk Ductal carcinoma In Situ is safe (E. S. Hwang et al., BMJ Open, 9: e026797, 2019, A. Francis et al., Eur J Cancer. 51: 2296-2303, 2015, Chizuko Kanbayashi et al. The international collaboration of active surveillance trials for low-risk DCIS (LORIS, LORD, COMET, LORETTA),  L. E. Elshof et al., Eur J Cancer, 51, 1497-510, 2015). Low-risk is defined as grade I or II DCIS. Because DCIS grade is a major eligibility criteria in these trials, it would be very helpful to assess DCIS grade on mammography, informed by grade assessed on DCIS histopathology in pre-surgery biopsies, since surgery will not be performed on a significant number of patients participating in these trials. OBJECTIVE: To assess the performance and clinical utility of a convolutional neural network (CNN) in discriminating high-risk (grade III) DCIS and/or Invasive Breast Cancer (IBC) from low-risk (grade I/II) DCIS based on mammographic features. We explored whether the CNN could be used as a decision support tool, from excluding high-risk patients for active surveillance. METHODS: In this single centre retrospective study, 464 patients diagnosed with DCIS based on pre-surgery biopsy between 2000 and 2014 were included. The collection of mammography images was partitioned on a patient-level into two subsets, one for training containing 80% of cases (371 cases, 681 images) and 20% (93 cases, 173 images) for testing. A deep learning model based on the U-Net CNN was trained and validated on 681 two-dimensional mammograms. Classification performance was assessed with the Area Under the Curve (AUC) receiver operating characteristic and predictive values on the test set for predicting high risk DCIS-and high-risk DCIS and/ or IBC from low-risk DCIS. RESULTS: When classifying DCIS as high-risk, the deep learning network achieved a Positive Predictive Value (PPV) of 0.40, Negative Predictive Value (NPV) of 0.91 and an AUC of 0.72 on the test dataset. For distinguishing high-risk and/or upstaged DCIS (occult invasive breast cancer) from low-risk DCIS a PPV of 0.80, a NPV of 0.84 and an AUC of 0.76 were achieved. CONCLUSION: For both scenarios (DCIS grade I/II vs. III, DCIS grade I/II vs. III and/or IBC) AUCs were high, 0.72 and 0.76, respectively, concluding that our convolutional neural network can discriminate low-grade from high-grade DCIS.

Locoregional Breast Cancer Recurrence in the European Organisation for Research and Treatment of Cancer 10041/BIG 03-04 MINDACT Trial: Analysis of Risk Factors Including the 70-Gene Signature.

PURPOSE: A number of studies are currently investigating de-escalation of radiation therapy in patients with a low risk of in-breast relapses on the basis of clinicopathologic factors and molecular tests. We evaluated whether 70-gene risk score is associated with risk of locoregional recurrence (LRR) and estimated 8-year cumulative incidences for LRR in patients with early-stage breast cancer treated with breast conservation. METHODS: In this exploratory substudy of European Organisation for Research and Treatment of Cancer 10041/BIG 03-04 MINDACT trial, we evaluated women with a known clinical and genomic 70-gene risk score test result and who had breast-conserving surgery (BCS). The primary end point was LRR at 8 years, estimated by cumulative incidences. Distant metastasis and death were considered competing risks. RESULTS: Among 6,693 enrolled patients, 5,470 (81.7%) underwent BCS, of whom 98% received radiotherapy. At 8-year follow-up, 189 patients experienced a LRR, resulting in an 8-year cumulative incidence of 3.2% (95% CI, 2.7 to 3.7). In patients with a low-risk 70-gene signature, the 8-year LRR incidence was 2.7% (95% CI, 2.1 to 3.3). In univariable analysis, adjusted for chemotherapy, five of 12 variables were associated with LRR, including the 70-gene signature. In multivariable modeling, adjuvant endocrine therapy and to a lesser extent tumor size and grade remained significantly associated with LRR. CONCLUSION: This exploratory analysis of the MINDACT trial estimated an 8-year low LRR rate of 3.2% after BCS. The 70-gene signature was not independently predictive of LRR perhaps because of the low number of events observed and currently cannot be used in clinical decision making regarding LRR. The overall low number of events does provide an opportunity to design trials toward de-escalation of local therapy.

The effects of contemporary treatment of DCIS on the risk of developing an ipsilateral invasive Breast cancer (iIBC) in the Dutch population.

PURPOSE: To assess the effects of contemporary treatment of ductal carcinoma in situ (DCIS) on the risk of developing an ipsilateral invasive breast cancer (iIBC) in the Dutch female population. METHODS: Clinical data was obtained from the Netherlands Cancer Registry (NCR), a nationwide registry of all primary malignancies in the Netherlands integrated with the data from PALGA, the Dutch nationwide network and registry of histo- and cytopathology in the Netherlands, on all women in the Netherlands treated for primary DCIS from 2005 to 2015, resulting in a population-based cohort of 14.419 women. Cumulative iIBC incidence was assessed and associations of DCIS treatment type with subsequent iIBC risk were evaluated by multivariable Cox regression analyses. RESULTS: Ten years after DCIS diagnosis, the cumulative incidence of iIBC was 3.1% (95% CI: 2.6-3.5%) in patients treated by breast conserving surgery (BCS) plus radiotherapy (RT), 7.1% (95% CI: 5.5-9.1) in patients treated by BCS alone, and 1.6% (95% CI: 1.3-2.1) in patients treated by mastectomy. BCS was associated with a significantly higher risk for iIBC compared to BCS + RT during the first 5 years after treatment (HR 2.80, 95% CI: 1.91-4.10%). After 5 years of follow-up, the iIBC risk declined in the BCS alone group but remained higher than the iIBC risk in the BCS + RT group (HR 1.73, 95% CI: 1.15-2.61). CONCLUSIONS: Although absolute risks of iIBC were low in patients treated for DCIS with either BCS or BCS + RT, risks remained higher in the BCS alone group compared to patients treated with BCS + RT for at least 10 years after DCIS diagnosis.

PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning.

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.

Deep (phospho)proteomics profiling of pre- treatment needle biopsies identifies signatures of treatment resistance in HER2+ breast cancer.

Patients with early-stage HER2-overexpressing breast cancer struggle with treatment resistance in 20%-40% of cases. More information is needed to predict HER2 therapy response and resistance in vivo. In this study, we perform (phospho)proteomics analysis of pre-treatment HER2+ needle biopsies of early-stage invasive breast cancer to identify molecular signatures predictive of treatment response to trastuzumab, pertuzumab, and chemotherapy. Our data show that accurate quantification of the estrogen receptor (ER) and HER2 biomarkers, combined with the assessment of associated biological features, has the potential to enable better treatment outcome prediction. In addition, we identify cellular mechanisms that potentially precondition tumors to resist therapy. We find proteins with expression changes that correlate with resistance and constitute to a strong predictive signature for treatment success in our patient cohort. Our results highlight the multifactorial nature of drug resistance in vivo and demonstrate the necessity of deep tumor profiling.

Association of DCIS size and margin status with risk of developing breast cancer post-treatment: multinational, pooled cohort study.

OBJECTIVE: To examine the association between size and margin status of ductal carcinoma in situ (DCIS) and risk of developing ipsilateral invasive breast cancer and ipsilateral DCIS after treatment, and stage and subtype of ipsilateral invasive breast cancer. DESIGN: Multinational, pooled cohort study. SETTING: Four large international cohorts. PARTICIPANTS: Patient level data on 47 695 women with a diagnosis of pure, primary DCIS between 1999 and 2017 in the Netherlands, UK, and US who underwent surgery, either breast conserving or mastectomy, often followed by radiotherapy or endocrine treatment, or both. MAIN OUTCOME MEASURES: The main outcomes were 10 year cumulative incidence of ipsilateral invasive breast cancer and ipsilateral DCIS estimated in relation to DCIS size and margin status, and adjusted hazard ratios and 95% confidence intervals, estimated using multivariable Cox proportional hazards analyses with multiple imputed data RESULTS: The 10 year cumulative incidence of ipsilateral invasive breast cancer was 3.2%. In women who underwent breast conserving surgery with or without radiotherapy, only adjusted risks for ipsilateral DCIS were significantly increased for larger DCIS (20-49 mm) compared with DCIS <20 mm (hazard ratio 1.38, 95% confidence interval 1.11 to 1.72). Risks for both ipsilateral invasive breast cancer and ipsilateral DCIS were significantly higher with involved compared with clear margins (invasive breast cancer 1.40, 1.07 to 1.83; DCIS 1.39, 1.04 to 1.87). Use of adjuvant endocrine treatment was not significantly associated with a lower risk of ipsilateral invasive breast cancer compared to treatment with breast conserving surgery only (0.86, 0.62 to 1.21). In women who received breast conserving treatment with or without radiotherapy, higher DCIS grade was not significantly associated with ipsilateral invasive breast cancer, only with a higher risk of ipsilateral DCIS (grade 1: 1.42, 1.08 to 1.87; grade 3: 2.17, 1.66 to 2.83). Higher age at diagnosis was associated with lower risk (per year) of ipsilateral DCIS (0.98, 0.97 to 0.99) but not ipsilateral invasive breast cancer (1.00, 0.99 to 1.00). Women with large DCIS (≥50 mm) more often developed stage III and IV ipsilateral invasive breast cancer compared to women with DCIS <20 mm. No such association was found between involved margins and higher stage of ipsilateral invasive breast cancer. Associations between larger DCIS and hormone receptor negative and human epidermal growth factor receptor 2 positive ipsilateral invasive breast cancer and involved margins and hormone receptor negative ipsilateral invasive breast cancer were found. CONCLUSIONS: The association of DCIS size and margin status with ipsilateral invasive breast cancer and ipsilateral DCIS was small. When these two factors were added to other known risk factors in multivariable models, clinicopathological risk factors alone were found to be limited in discriminating between low and high risk DCIS.

Characterization of the Tumor Microenvironment of De Novo Oligometastatic Breast Cancer in a Nationwide Cohort.

PURPOSE: Oligometastatic breast cancer (OMBC) has a more favorable outcome than widespread metastatic breast cancer. Some patients with OMBC achieve long-term remission if treated with multimodality therapy, including systemic and locally ablative therapies. However, not all patients with OMBC benefit from such treatment, while all experience toxicity. To explore biomarkers identifying patients with OMBC and potential long-term survival, we compared tumor-immune characteristics of patients with OMBC and long-term versus shorter-term survival. MATERIALS AND METHODS: We collected tumor tissue of 97 patients with de novo OMBC (≤5 metastases) via the Dutch nationwide cancer and pathology registries using a case-control design. Long-term survivors (LTS) were defined as patients alive ≥10 years since OMBC diagnosis. Fifty-five LTS and 42 shorter-term survivors (STS) were included. Median follow-up was 15 years (IQR, 14-16). Tumor characteristics and infiltrating immune cells were assessed by immunohistochemistry and next-generation RNA-sequencing. Association of the resulting 52 biomarkers with long-term survival was assessed using logistic regression. Associations with survival within LTS were assessed using Cox-proportional hazards modeling. P values were adjusted for multiple hypothesis testing. RESULTS: Most patients had estrogen receptor (ER)-positive OMBC (n = 86; 89%) and 23 (24%) had human epidermal growth factor receptor 2-positive disease. ER positivity in primary tumors distinguished LTS from STS. In addition, extracellular matrix (ECM)2-low and ECM4-high distinguished between long-term and shorter-term survival. Immune levels in the primary tumor did not associate with LTS. However, within the LTS subset, higher immune levels associated with improved progression-free survival. CONCLUSION: We identified tumor and ECM features in the primary tumor of patients with de novo OMBC that were associated with long-term survival. Our data should be validated in other patients with OMBC before they can be used in clinical practice.

High-dose alkylating chemotherapy in BRCA-altered triple-negative breast cancer: the randomized phase III NeoTN trial.

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.

Archival single-cell genomics reveals persistent subclones during DCIS progression.

Ductal carcinoma in situ (DCIS) is a common precursor of invasive breast cancer. Our understanding of its genomic progression to recurrent disease remains poor, partly due to challenges associated with the genomic profiling of formalin-fixed paraffin-embedded (FFPE) materials. Here, we developed Arc-well, a high-throughput single-cell DNA-sequencing method that is compatible with FFPE materials. We validated our method by profiling 40,330 single cells from cell lines, a frozen tissue, and 27 FFPE samples from breast, lung, and prostate tumors stored for 3-31 years. Analysis of 10 patients with matched DCIS and cancers that recurred 2-16 years later show that many primary DCIS had already undergone whole-genome doubling and clonal diversification and that they shared genomic lineages with persistent subclones in the recurrences. Evolutionary analysis suggests that most DCIS cases in our cohort underwent an evolutionary bottleneck, and further identified chromosome aberrations in the persistent subclones that were associated with recurrence.

Microcalcification crystallography as a potential marker of DCIS recurrence.

Ductal carcinoma in-situ (DCIS) accounts for 20-25% of all new breast cancer diagnoses. DCIS has an uncertain risk of progression to invasive breast cancer and a lack of predictive biomarkers may result in relatively high levels (~ 75%) of overtreatment. To identify unique prognostic biomarkers of invasive progression, crystallographic and chemical features of DCIS microcalcifications have been explored. Samples from patients with at least 5-years of follow up and no known recurrence (174 calcifications in 67 patients) or ipsilateral invasive breast cancer recurrence (179 microcalcifications in 57 patients) were studied. Significant differences were noted between the two groups including whitlockite relative mass, hydroxyapatite and whitlockite crystal maturity and, elementally, sodium to calcium ion ratio. A preliminary predictive model for DCIS to invasive cancer progression was developed from these parameters with an AUC of 0.797. These results provide insights into the differing DCIS tissue microenvironments, and how these impact microcalcification formation.

Immune landscape of breast tumors with low and intermediate estrogen receptor expression.

Immune checkpoint blockade (ICB) is currently approved for patients with triple-negative breast cancer (TNBC), whereas responses to ICB are also observed in a small subgroup of Estrogen Receptor (ER)-positive breast cancer. The cut-off for ER-positivity (≥1%) is based on likelihood of endocrine treatment response, but ER-positive breast cancer represents a very heterogeneous group. This raises the question whether selection based on ER-negativity should be revisited to select patients for ICB treatment in the context of clinical trials. Stromal tumor-infiltrating lymphocytes (sTILs) and other immune parameters are higher in TNBC compared to ER-positive breast cancer, but it is unknown whether lower ER levels are associated with more inflamed tumor microenvironments (TME). We collected a consecutive series of primary tumors from 173 HER2-negative breast cancer patients, enriched for tumors with ER expression between 1 and 99% and found levels of stromal TILs, CD8 + T cells, and PD-L1 positivity in breast tumors with ER 1-9% and ER 10-50% to be comparable to tumors with ER 0%. Expression of immune-related gene signatures in tumors with ER 1-9% and ER 10-50% was comparable to ER 0%, and higher than in tumors with ER 51-99% and ER 100%. Our results suggest that the immune landscape of ER low tumors (1-9%) and ER intermediate tumors (10-50%) mimic that of primary TNBC.

A living biobank of patient-derived ductal carcinoma in situ mouse-intraductal xenografts identifies risk factors for invasive progression.

Ductal carcinoma in situ (DCIS) is a non-obligate precursor of invasive breast cancer (IBC). Due to a lack of biomarkers able to distinguish high- from low-risk cases, DCIS is treated similar to early IBC even though the minority of untreated cases eventually become invasive. Here, we characterized 115 patient-derived mouse-intraductal (MIND) DCIS models reflecting the full spectrum of DCIS observed in patients. Utilizing the possibility to follow the natural progression of DCIS combined with omics and imaging data, we reveal multiple prognostic factors for high-risk DCIS including high grade, HER2 amplification, expansive 3D growth, and high burden of copy number aberrations. In addition, sequential transplantation of xenografts showed minimal phenotypic and genotypic changes over time, indicating that invasive behavior is an intrinsic phenotype of DCIS and supporting a multiclonal evolution model. Moreover, this study provides a collection of 19 distributable DCIS-MIND models spanning all molecular subtypes.

Tumour-educated platelets for breast cancer detection: biological and technical insights.

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.

Artificial intelligence-based morphometric signature to identify ductal carcinoma in situ with low risk of progression to invasive breast cancer.

Ductal carcinoma in situ (DCIS) may progress to ipsilateral invasive breast cancer (iIBC), but often never will. Because DCIS is treated as early breast cancer, many women with harmless DCIS face overtreatment. To identify these women that may forego treatment, we hypothesized that DCIS morphometric features relate to the risk of subsequent iIBC. We developed an artificial intelligence-based DCIS morphometric analysis pipeline (AIDmap) to detect DCIS as a pathologist and measure morphological structures in hematoxylin-eosin-stained (H&E) tissue sections. These were from a case-control study of patients diagnosed with primary DCIS, treated by breast-conserving surgery without radiotherapy. We analyzed 689 WSIs of DCIS of which 226 were diagnosed with subsequent iIBC (cases) and 463 were not (controls). The distribution of 15 duct morphological measurements in each H&E was summarized in 55 morphometric variables. A ridge regression classifier with cross validation predicted 5-years-free of iIBC with an area-under the curve of 0.65 (95% CI 0.55-0.76). A morphometric signature based on the 30 variables most associated with outcome, identified lesions containing small-sized ducts, low number of cells and low DCIS/stroma area ratio. This signature was associated with lower iIBC risk in a multivariate regression model including grade, ER, HER2 and COX-2 expression (HR = 0.56; 95% CI 0.28-0.78). AIDmap has potential to identify harmless DCIS that may not need treatment.

Learning to distinguish progressive and non-progressive ductal carcinoma in situ.

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.