Cases Of Ductal Carcinoma In Situ Research Articles

Pathogenic variants in cancer susceptibility genes predispose to Ductal Carcinoma In situ of the breast.

To determine the relationship between germline pathogenic variants (PVs) in cancer predisposition genes and the risk of ductal carcinoma in situ (DCIS). Germline PV frequencies in breast cancer predisposition genes (ATM, BARD1, BRCA1, BRCA2, CDH1, CHEK2, PALB2, RAD51C, and RAD51D) were compared between DCIS cases and unaffected controls, and between DCIS and infiltrating ductal carcinoma (IDC) cases from a clinical-testing cohort (n=9,887), a population-based cohort (n=3,876) and the UK Biobank (n=2421). The risk of contralateral breast cancer for DCIS cases with PVs was estimated in the population-based cohort. Germline PVs were observed in 6.5% and 4.6% of women with DCIS in the clinical-testing and population-based cohorts, respectively. BRCA1, BRCA2, and PALB2 PVs frequencies were significantly lower among women with DCIS than IDC (clinical cohort: 2.8% vs. 5.7%; population-based cohort: 1.7% vs. 3.7%), whereas the PV frequencies for ATM and CHEK2 were similar. ATM, BRCA1, BRCA2, CHEK2, and PALB2 PVs were significantly associated with an increased risk of DCIS (OR>2.0), but only BRCA2 PVs were associated with high-risk (OR>4) in both cohorts. The cumulative incidence of contralateral breast cancer among carriers of PVs in high penetrance genes with DCIS was 23% over 15 years. The enrichment of PVs in ATM, BRCA1, BRCA2, CHEK2 and PALB2 among women with DCIS suggests that multigene panel testing may be appropriate for women with DCIS. Elevated risks of contralateral breast cancer in carriers of PVs in high penetrance genes with DCIS confirmed the utility of testing for surgical decision-making.

P63 Expression in Ductal Carcinoma In Situ (DCIS) of the Breast and Its Correlation with Histopathological Grading and Morphological Variants

Background: Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer with varying potential for progression to invasive carcinoma. Myoepithelial cells (MECs) play a role in preventing this progression, and their absence is a hallmark of invasive disease. The p63 protein is a myoepithelial marker that can be assessed by immunohistochemistry (IHC). This study aimed to evaluate the relationship between p63 expression in MECs, the grade of DCIS, and the morphological subtype of DCIS. Methods: A cross-sectional study was conducted on 35 cases of DCIS diagnosed at the Anatomical Pathology Laboratory of Dr. M. Djamil General Hospital Padang. Paraffin blocks were collected, and Hematoxylin and Eosin (H&E) slides were reviewed to confirm the diagnosis and determine the histopathological grading (low, intermediate, and high) and morphological variants (comedo and non-comedo) of DCIS. Paraffin blocks were re-cut for p63 immunohistochemical staining. The extent of p63 expression was classified as complete or incomplete. Results: The majority of DCIS cases were high grade (54.3%) and of the non-comedo subtype (68.4%). All cases with complete p63 expression were of low histologic grade, while all cases with incomplete p63 expression were of high histologic grade. The results of the Chi-square test showed a statistically significant relationship between p63 expression and histopathological grading (p<0.001). There was no statistically significant relationship between p63 expression and morphological variant. Conclusion: The absence of p63 expression in DCIS is associated with high histologic grade. This finding suggests that p63 IHC may be a useful adjunct in evaluating DCIS.

9218 Highly Multiplexed Mapping of the Ductal Carcinoma In Situ Ecosystem to Predict Disease Outcome

Abstract Disclosure: G.M. Wilson: None. T.B. Doan: None. B.J. Guild: None. N. Pathmanathan: None. J.D. Graham: None. Mammographic screening programs have resulted in remarkable improvements in breast cancer survival due to early-stage detection. However, this has resulted in a dramatic increase in the detection of non-invasive ductal carcinoma in situ (DCIS), a non-obligate precursor of invasive ductal carcinoma (IDC), which is confined to the ducts and surrounded by a normal myoepithelial cell layer. Many DCIS cases will not progress to IDC within the lifetime of the patient. However, there are limited tools to accurately predict progression, thus virtually all DCIS is surgically removed, and most patients receive adjuvant radiotherapy as it halves recurrence rates. This likely represents overtreatment for a majority of patients. Emerging evidence has revealed that the adjacent microenvironment becomes progressively altered in DCIS and IDC compared to normal tissue, and this is correlated with disease outcome. The objective of this study was to investigate changes to the DCIS microenvironment during disease progression and identify disease states that predict patient outcome. We used immunohistochemistry and imaging mass cytometry with a well-annotated cohort of DCIS, with or without adjacent IDC, to measure a panel of 40 phenotypic and functional markers that allowed us to distinguish the diverse cell populations present in the tissues. We observed striking changes to almost all cell types within the DCIS tissue when comparing pure DCIS cases to DCIS adjacent to IDC. In particular, there were considerable changes to tissue-resident immune populations, with differences observed in the type, location and population size of a variety of immune cell subsets, in IDC-associated DCIS compared with DCIS-only cases. Computational mapping of cell type communities in pure and IDC-adjacent DCIS tissues revealed specific differences that could be used to predict the emergence of invasive disease, informing clinical management of this good prognosis malignancy and potentially avoiding over-treatment. Presentation: 6/3/2024

9218 Highly Multiplexed Mapping Of The Ductal Carcinoma In Situ Ecosystem To Predict Disease Outcome

Abstract Disclosure: G.M. Wilson: None. T.B. Doan: None. B.J. Guild: None. N. Pathmanathan: None. J.D. Graham: None. Mammographic screening programs have resulted in remarkable improvements in breast cancer survival due to early-stage detection. However, this has resulted in a dramatic increase in the detection of non-invasive ductal carcinoma in situ (DCIS), a non-obligate precursor of invasive ductal carcinoma (IDC), which is confined to the ducts and surrounded by a normal myoepithelial cell layer. Many DCIS cases will not progress to IDC within the lifetime of the patient. However, there are limited tools to accurately predict progression, thus virtually all DCIS is surgically removed, and most patients receive adjuvant radiotherapy as it halves recurrence rates. This likely represents overtreatment for a majority of patients. Emerging evidence has revealed that the adjacent microenvironment becomes progressively altered in DCIS and IDC compared to normal tissue, and this is correlated with disease outcome. The objective of this study was to investigate changes to the DCIS microenvironment during disease progression and identify disease states that predict patient outcome. We used immunohistochemistry and imaging mass cytometry with a well-annotated cohort of DCIS, with or without adjacent IDC, to measure a panel of 40 phenotypic and functional markers that allowed us to distinguish the diverse cell populations present in the tissues. We observed striking changes to almost all cell types within the DCIS tissue when comparing pure DCIS cases to DCIS adjacent to IDC. In particular, there were considerable changes to tissue-resident immune populations, with differences observed in the type, location and population size of a variety of immune cell subsets, in IDC-associated DCIS compared with DCIS-only cases. Computational mapping of cell type communities in pure and IDC-adjacent DCIS tissues revealed specific differences that could be used to predict the emergence of invasive disease, informing clinical management of this good prognosis malignancy and potentially avoiding over-treatment. Presentation: 6/3/2024

8770 Highly Multiplexed Mapping of the Ductal Carcinoma In Situ Ecosystem to Predict Disease Outcome

Abstract Disclosure: G.M. Wilson: None. T.B. Doan: None. B.J. Guild: None. N. Pathmanathan: None. J.D. Graham: None. Mammographic screening programs have resulted in remarkable improvements in breast cancer survival due to early-stage detection. However, this has resulted in a dramatic increase in the detection of non-invasive ductal carcinoma in situ (DCIS), a non-obligate precursor of invasive ductal carcinoma (IDC), which is confined to the ducts and surrounded by a normal myoepithelial cell layer. Many DCIS cases will not progress to IDC within the lifetime of the patient. However, there are limited tools to accurately predict progression, thus virtually all DCIS is surgically removed, and most patients receive adjuvant radiotherapy as it halves recurrence rates. This likely represents overtreatment for a majority of patients. Emerging evidence has revealed that the adjacent microenvironment becomes progressively altered in DCIS and IDC compared to normal tissue, and this is correlated with disease outcome. The objective of this study was to investigate changes to the DCIS microenvironment during disease progression and identify disease states that predict patient outcome. We used immunohistochemistry and imaging mass cytometry with a well-annotated cohort of DCIS, with or without adjacent IDC, to measure a panel of 40 phenotypic and functional markers that allowed us to distinguish the diverse cell populations present in the tissues. We observed striking changes to almost all cell types within the DCIS tissue when comparing pure DCIS cases to DCIS adjacent to IDC. In particular, there were considerable changes to tissue-resident immune populations, with differences observed in the type, location and population size of a variety of immune cell subsets, in IDC-associated DCIS compared with DCIS-only cases. Computational mapping of cell type communities in pure and IDC-adjacent DCIS tissues revealed specific differences that could be used to predict the emergence of invasive disease, informing clinical management of this good prognosis malignancy and potentially avoiding over-treatment. Presentation: 6/3/2024

8770 Highly Multiplexed Mapping Of The Ductal Carcinoma In Situ Ecosystem To Predict Disease Outcome

Abstract Disclosure: G.M. Wilson: None. T.B. Doan: None. B.J. Guild: None. N. Pathmanathan: None. J.D. Graham: None. Mammographic screening programs have resulted in remarkable improvements in breast cancer survival due to early-stage detection. However, this has resulted in a dramatic increase in the detection of non-invasive ductal carcinoma in situ (DCIS), a non-obligate precursor of invasive ductal carcinoma (IDC), which is confined to the ducts and surrounded by a normal myoepithelial cell layer. Many DCIS cases will not progress to IDC within the lifetime of the patient. However, there are limited tools to accurately predict progression, thus virtually all DCIS is surgically removed, and most patients receive adjuvant radiotherapy as it halves recurrence rates. This likely represents overtreatment for a majority of patients. Emerging evidence has revealed that the adjacent microenvironment becomes progressively altered in DCIS and IDC compared to normal tissue, and this is correlated with disease outcome. The objective of this study was to investigate changes to the DCIS microenvironment during disease progression and identify disease states that predict patient outcome. We used immunohistochemistry and imaging mass cytometry with a well-annotated cohort of DCIS, with or without adjacent IDC, to measure a panel of 40 phenotypic and functional markers that allowed us to distinguish the diverse cell populations present in the tissues. We observed striking changes to almost all cell types within the DCIS tissue when comparing pure DCIS cases to DCIS adjacent to IDC. In particular, there were considerable changes to tissue-resident immune populations, with differences observed in the type, location and population size of a variety of immune cell subsets, in IDC-associated DCIS compared with DCIS-only cases. Computational mapping of cell type communities in pure and IDC-adjacent DCIS tissues revealed specific differences that could be used to predict the emergence of invasive disease, informing clinical management of this good prognosis malignancy and potentially avoiding over-treatment. Presentation: 6/3/2024

Advancements in Managing Pure Ductal Carcinoma In Situ: An 18-Year Artificial Intelligence-Aided Analysis of 998 Patients

IntroductionThe integration of artificial intelligence (AI) in oncological diseases has opened new avenues for early cancer detection, accurate risk assessments, and personalized treatment protocols. The objective of our study was to describe the shift in the treatment indications and prognosis of ductal carcinoma in situ (DCIS) in a prospective cohort of 998 patients managed over 18 years at a single University Hospital in France. MethodsThis analysis included all patients managed for DCIS at the University Hospitals of Strasbourg between January 14, 2002, and December 18, 2019, from the prospective SENOMETRY cohort, which initially included 9599 patients with both in situ and invasive breast cancer. Data were analyzed using Onconum, an AI-based natural language processing tool, to extract structured information from medical records. ResultsThe incidence of DCIS remained stable at 65 new cases per year. The mean age at diagnosis increased from 55 years in 2002 to 61 years in 2019. There was a significant rise in high-grade DCIS (DIN3) cases from 25% to 35%. The re-excision rate decreased from 57% in 2002 to 18% in 2019. Most DCIS cases were managed with breast-conserving surgeries (682), while total mastectomies accounted for 385 cases. Sentinel lymph node biopsy was performed in 39% of cases, primarily in high-grade and multifocal DCIS. Specific mortality was 0%, with a recurrence rate of 2.2%, predominantly invasive and occurring earlier in high-grade DCIS. Discussion: Over 18 years, there has been a notable shift in the clinicopathological characteristics of DCIS, with an increase in patient age at diagnosis and higher histopathological grades. Therapeutic management evolved significantly, with reduced surgical margins and fewer adjuvant treatments, while maintaining low and stable recurrence rates. AI significantly enhanced data extraction and analysis efficiency, contributing to better clinical decision-making. ConclusionThe study confirms the possibility of therapeutic de-escalation in DCIS, supported by AI-driven data analysis, allowing individualized treatment approaches and leading to optimized patient outcomes.

Expression of T cell-related proteins in breast ductal carcinoma in situ.

This study aims to explore the expression of T cell subtype markers within the immune cells constituting the tumor microenvironment of ductal carcinoma in situ (DCIS) and to assess its implications. A tissue microarray comprising 191 cases of breast DCIS was created, and immunohistochemistry staining for T cell subtype markers (STAT3, STAT4, STAT-6, and FOXP3) was conducted. The DCIS cases were categorized into luminal, HER-2, and TNBC (Triple-negative breast cancer) types based on ER, PR, HER-2, and Ki-67 results. Additionally, they were classified as low-TIL (tumor-infiltrating lymphocytes) (<10%) or high-TIL (≥10%) types according to stromal TIL. Results revealed that 54.6% were luminal, 39.5% HER-2, and 5.9% TNBC. STAT3 exhibited a high positivity rate in luminal-type tumor cells, while STAT3, STAT4, STAT6, and FOXP3 showed elevated positivity rates in TNBC immune cells (p<0.05). Furthermore, a higher positivity rate was observed in high-TIL immune cells compared with low-TIL (p<0.001). The strongest agreement between T cell subtype markers in immune cells was found between STAT3 and STAT4 (OA=83.7%, κ=0.658), whereas the lowest was between STAT4 and FOXP3 (OA=71.7%, κ=0.370). In immune cells, STAT3 and STAT4 positivity correlated with necrosis (p<0.001), and the absence of positivity in all immune cell-related proteins in DCIS with necrosis was associated with poor prognosis (p=0.013). In conclusion, the immune cells in DCIS exhibit positivity for diverse T cell subtype markers, with TNBC and high-TIL DCIS displaying heightened positivity.

Presurgical Upgrade Prediction of DCIS to Invasive Ductal Carcinoma Using Time-dependent Deep Learning Models with DCE MRI.

Purpose To determine whether time-dependent deep learning models can outperform single time point models in predicting preoperative upgrade of ductal carcinoma in situ (DCIS) to invasive malignancy at dynamic contrast-enhanced (DCE) breast MRI without a lesion segmentation prerequisite. Materials and Methods In this exploratory study, 154 cases of biopsy-proven DCIS (25 upgraded at surgery and 129 not upgraded) were selected consecutively from a retrospective cohort of preoperative DCE MRI in women with a mean age of 59 years at time of diagnosis from 2012 to 2022. Binary classification was implemented with convolutional neural network (CNN)-long short-term memory (LSTM) architectures benchmarked against traditional CNNs without manual segmentation of the lesions. Combinatorial performance analysis of ResNet50 versus VGG16-based models was performed with each contrast phase. Binary classification area under the receiver operating characteristic curve (AUC) was reported. Results VGG16-based models consistently provided better holdout test AUCs than did ResNet50 in CNN and CNN-LSTM studies (multiphase test AUC, 0.67 vs 0.59, respectively, for CNN models [P = .04] and 0.73 vs 0.62 for CNN-LSTM models [P = .008]). The time-dependent model (CNN-LSTM) provided a better multiphase test AUC over single time point (CNN) models (0.73 vs 0.67; P = .04). Conclusion Compared with single time point architectures, sequential deep learning algorithms using preoperative DCE MRI improved prediction of DCIS lesions upgraded to invasive malignancy without the need for lesion segmentation. Keywords: MRI, Dynamic Contrast-enhanced, Breast, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2024.

Cancer-specific alterations in nuclear matrix proteins determined by multi-omics analyses of ductal carcinoma in situ.

Breast cancer (BC) is the most common cancer affecting women in the United States. Ductal carcinoma in situ (DCIS) is the earliest identifiable pre-invasive BC lesion. Estimates show that 14 to 50% of DCIS cases progress to invasive BC. Our objective was to identify nuclear matrix proteins (NMP) with specifically altered expression in DCIS and later stages of BC compared to non-diseased breast reduction mammoplasty and a contralateral breast explant culture using mass spectrometry and RNA sequencing to accurately identify aggressive DCIS. Sixty NMPs were significantly differentially expressed between the DCIS and non-diseased breast epithelium in an isogenic contralateral pair of patient-derived extended explants. Ten of the sixty showed significant mRNA expression level differences that matched the protein expression. These 10 proteins were similarly expressed in non-diseased breast reduction cells. Three NMPs (RPL7A, RPL11, RPL31) were significantly upregulated in DCIS and all other BC stages compared to the matching contralateral breast culture and an unrelated non-diseased breast reduction culture. RNA sequencing analyses showed that these three genes were increasingly upregulated with BC progression. Finally, we identified three NMPs (AHNAK, CDC37 and DNAJB1) that were significantly downregulated in DCIS and all other BC stages compared to the isogenically matched contralateral culture and the non-diseased breast reduction culture using both proteomics and RNA sequencing techniques. These genes should form the basis of, or contribute to, a molecular diagnostic panel that could identify DCIS lesions likely to be indolent and therefore not requiring aggressive treatment.

Low progesterone receptor levels in high-grade DCIS correlate with HER2 upregulation and the presence of invasive components.

In this study, we investigated pivotal molecular markers in human high-grade breast ductal carcinoma in situ (DCIS). Expression status of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth receptor 2 (HER2) was measured among various subtypes (Luminal (Lum) A, LumB HER2-, LumB HER2+, HER2-enriched and triple-negative). In total, 357 DCIS cases were classified into respective subtypes, according to the 2013 St. Gallen guidelines. Each subtype was categorized into three subcategories: "Pure" (those without an invasive component), "W/invasive" (those with an invasive component), and "All" (the entire group of the given subtype). ER and PR expression were registered as intervals. Equivocal HER2 immunohistochemistry (IHC) cases (2+) were further investigated using dual-color in situ hybridization. The majority of patients (71%) were over the age of 50. We discovered no significant differences in the proportion of age between the "Pure" and "W/invasive" groups. There was no significant difference in ER/PR expression between "Pure" luminal subtypes of DCIS and "W/invasive" cases. We compared the HER2 IHC scores of "0", "1+", and "2+" among LumA and LumB HER2 subtypes and identified no statistically significant differences between "Pure" and "W/invasive" (p = 0.603). ER and PR expression ≥ 50% cutoff value was present in > 90% of all LumA cases. The incidences of cases with ER expression at cutoff values of < 10% and ≥ 50% in LumA were significantly different compared to other luminal subtypes (p < 0.0001). The proportion of cases with PR expression < 20% showed significant differences in the various luminal subtypes. In luminal B subtypes, low PR expression (< 20%) was significantly associated with both strong HER2 expression (3+) and the presence of an invasive component (p = 0.0001 and p = 0.0365, respectively). ER and PR expression at ≥ 50% cutoff values were found in more than 90% of LumA cases. Samples with ER < 10% and ≥ 50% in LumA were significantly different compared to other luminal subtypes (p < 0.0001). Low PR expression in high-grade DCIS was strongly associated with HER2 overexpression (3+) and an invasive component (p = 0.0001 and p = 0.0365, respectively).

Abstract PO1-24-11: Distinct patterns of MHC class I, PD-L1 expression, and T-cell infiltration in different subtypes of ductal carcinoma in situ of the breast

Abstract Background: Ductal carcinoma in situ (DCIS) of the breast encompasses various subtypes with distinct behaviors. Immune cell infiltration within the tumor microenvironment is thought to influence DCIS progression. Tumor-infiltrating lymphocytes (TILs) serve as a surrogate marker for the adaptive immune response. The interaction between programmed death 1 (PD-1) and its ligand PD-L1 suppresses effector T-cell function. Major histocompatibility complex (MHC) class I molecules play a critical role in presenting tumor antigens to cytotoxic T cells. Despite the significance of PD-L1 and MHC class I in immune evasion, limited data exist on their correlation with T-cell infiltration in DCIS subtypes. Methods: Using immunohistochemistry, we examined MHC class I and PD-L1 expression, along with CD3+ and CD8+ T lymphocytes, in 131 DCIS cases through tissue microarrays. DCIS subtypes included hormone receptor-positive (HR+)/human epidermal growth factor 2-negative (HER2-), HR+/HER2+, HR-/HER2+, and triple-negative (TN) subtypes. Results: Among the 128 interpretable cases, the distribution was as follows: HR+/HER2- (50.8%), HR+/HER2+ (14.1%), HR-/HER2+ (26.5%), and TN (8.6%). HER2+ subtypes (HR+/HER2+ and HR-/HER2+) exhibited higher stromal TILs, CD3+ T cells, and CD8+ T cells compared to HR+/HER2- subtype. MHC class I loss was observed in 16.4% (21/128) of cases, with the highest frequency in the HR+/HER2- subtype (29.2%), followed by HR-/HER2+ (5.9%), HR+/HER2+ (0%), and TN (0%) subtypes. DCIS cases lacking MHC class I expression displayed lower stromal CD8+ T cell infiltration than those with intact MHC class I. PD-L1 expression in immune cells (≥1%) was detected in 18.8% (24/128) of cases and correlated with increased stromal TILs, CD3+ T cells, and CD8+ T cells. PD-L1 expression was more prevalent in HER2+ subtypes than in HR+/HER2- subtype. PD-L1-positive DCIS cases mostly retained MHC class I expression (95.8%), except for one case (4.2%) in the HR-/HER2+ subtype. The highest numbers of stromal CD3+ and CD8+ T cells were observed in DCIS cases with intact MHC class I and positive PD-L1 expression. The correlation between MHC class I and PD-L1 expression and infiltration of CD3+ and CD8+ T cells was maintained in HR+/HER2- and HR-/HER2+ subtypes. Notably, tumoral CD3+ and CD8+ T cells showed no significant association with MHC class I or PD-L1 expression. Furthermore, MHC class I and PD-L1 expression did not predict tumor recurrence. Conclusion: We observed distinct patterns of MHC class I and PD-L1 expression and T-cell infiltration among DCIS subtypes. Enhancing our understanding of MHC class I, PD-L1, and immune subsets, including CD8+ T cells, may contribute to the development of immune modulating therapies for DCIS, particularly in the HER2+ subtype. Citation Format: Nah Ihm Kim, Min Ho Park, Ji Shin Lee. Distinct patterns of MHC class I, PD-L1 expression, and T-cell infiltration in different subtypes of ductal carcinoma in situ of the breast [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-24-11.

Abstract PO5-13-11: Quantitative Assessment of HER2 Expression in Intraductal Neoplasms of the Breast

Abstract HER2 (ERBB2) is an established prognostic and predictive marker for patients with invasive breast cancer and used in HER2-targeted therapy. The clinical and biological significance of expression of HER2 protein in patients with intraductal neoplasms of the breast is not well characterized. In this study, we used our HS-HER2 (High Sensitivity-HER2) which is a CAP/CLIA certified laboratory derived test (LDT) using quantitative immunofluorescence (QIF). We evaluated HER2 expression in DCIS (ductal carcinoma in situ), LCIS (lobular carcinoma in situ) and benign lesions to correlate with their clinicopathologic characteristics. Thirty- eight cases of DCIS, and fourteen cases of LCIS and thirty benign cases were selected from the Yale Pathology department archives and 252 ROIs (regions of interest) were annotated by a board-certified pathologist. DCIS is classified according to the three-tier nuclear grading system: low, intermediate, and high-nuclear grade. LCIS is classified as PLCIS (pleomorphic variant) and CLCIS (classic LCIS) according to the WHO classification of breast tumors. Serial sections of FFPE tumor specimens were used to accurately quantify the HER2 expression by the HS-HER2 assay and the acquisition by QuPath v.04 with Qymia extension. According to the LOD (limit of detection,3 amol/mm2), LOQ (limit of quantification, 9 amol/mm2) and LOL (limit of linearity, 23 amol/mm2) of the optimized HS-HER2 assay as performed in our CLIA lab shows a broad range of HER2 expression in both DCIS and LCIS lesions, ranging from 0.7 amol/mm2 to 111.4 amol/mm2 in DCIS and 3.6 amol/mm2 to 49.9 amol/mm2 in LCIS. High grade DCIS lesions express higher average HER2 levels (111.4 + 0.7 amol/mm2) than combined low and intermediate grade DCIS (77.9 +1.9 amol/mm2) with no significant difference between low/intermediate and high-grade DCIS. CLCIS lesions express higher average HER2 level (49.9 + 3.6 amol/mm2) than PLCIS (35.1 + 5.4amol/mm2) but again this is not statistically significant. HER protein expression is relatively low in benign lesions ranging from 0.8 to 8.4 amol/mm2. Using the HS-HER2 assay, our results show quantitative level HER2 levels in DCIS and LCIS breast lesions. These findings may be important as HER2 targeted therapies that work in gene-unamplified cancer (HER2 conjugated ADCs) gain broader usage in the clinic. Citation Format: Nay Nwe Nyein Chan, Haiying Zhan, Revekka Khaimova, Thazin Aung, Charles Robbins, Patricia Gaule, David Rimm. Quantitative Assessment of HER2 Expression in Intraductal Neoplasms of the Breast [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-13-11.

Abstract PO4-25-10: Towards personalized medicine for DCIS - the role of hormone receptors, HER2, and Ki67 status in high-grade DCIS

Abstract Objective: This study aimed to investigate the molecular details in breast ductal carcinoma in situ (DCIS). Expression status of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki67 were investigated among luminal A (LumA), luminal B HER2-negative (LumB HER2-), luminal B HER2-positive (LumB HER2+), HER2-enriched, and triple-negative (TPN), subtypes of DCIS. Materials &amp; Methods: The study comprised formalin-fixed paraffin-embedded (FFPE) specimens of 357 DCIS grade 3 cases diagnosed between 1996 – 2018. Routine diagnostic immunohistochemical (IHC) staining were performed. DCIS cases were classified as LumA, LumB HER2-, LumB HER2+, HER2-enriched or TPN, according to the 2013 St. Gallen guidelines, which is used for molecular subtyping of invasive breast carcinoma (IBC). Each subtype was sorted into three subcategories: “pure” meaning those without an invasive component; “w/invasive” meaning those with an invasive component; and “all” meaning the entire group of the given subtype. Furthermore, ER and PR-receptor expression was registered for LumA, LumB HER2- and LumB HER2+ cases as intervals. The distribution of Ki67 was analyzed within each subtype. For some analyses, we combined the LumA and LumB HER2- subtypes into one entire group. The inter-observer variability of Ki67 was calculated by setting a cut-off value of 20% (2013 St. Gallen). A cohort (n = 47) of DCIS cases with a median Ki67 value of 18% was selected. Ki67 was calculated by analyzing 200 DCIS cells in two separate hotspots. Cohen's Kappa coefficient was calculated based on these data. HER2 (IHC) was scored based on ASCO/CAP guidelines established for routine diagnostic work-up for IBC. 16 equivocal cases (2+) were further investigated using dual SISH. Results: 98% of “all” cases of the LumA subtype showed an ER ≥ 50%. PR expression ≥ 50% was found in 91% of cases in this subtype. The incidence of ER-receptor at a cut-off ≥ 50% in the LumA subtype was significantly higher than that in the LumB HER2- and LumB HER2+ subtypes (p &amp;lt; 0.0001, Chi-square). In contrast, there was a statistically significant reduction in ER-receptor expression at a cut-off &amp;lt; 10% in LumA compared to the latter subtypes. The proportion of cases with PR-receptors with a cut-off of &amp;lt; 20% showed significant differences between LumA, LumB HER2- and LumB HER2+ subtypes (1.6%; 47% and 37%, respectively). There was also a significantly higher proportion of PR-receptor ≥ 50% cases among the LumA subtype. We found a significant association between PR &amp;lt; 20% and HER2 (3+) in luminal subtypes (p &amp;lt; 0.0004, Fisher's exact). There was no significant difference in ER/PR expression in “pure” cases of luminal subtypes of DCIS versus those with an invasive component (p = 0.1831, Chi-square). The Ki67 in the entire population (n = 357) varied from &amp;lt; 1% to &amp;gt; 80%. The mean and median were around 20% in those subtypes whose classifications were not depended on Ki67. There was a significant difference in the distribution of Ki67 when cases of LumA “all” and those of LumB HER2- “all” were combined as one entire group and were compared to LumB HER2+ “all” and HER2-enriched “all” (p-adjusted &amp;lt; 0.0001, Kruskal Wallis). The assessment of Ki67 among different observers showed a Cohen's kappa coefficient of 0.29 – 0.31 (fair agreement). We compared the HER2 (IHC) 0, 1+ and 2+ score among LumA and LumB HER2- subtypes and did not find a statically significant difference, when the “pure” and “w/invasive” were compared (p = 0.603, Chi-square). Conclusions: Ki67 was highly variable in DCIS grade 3. Inter-observer agreement was (as expected) suboptimal, and the cut-off at 20% defined by the 2013 St Gallen guidelines for IBC is not reliable for the distinction of LumA and LumB HER2 subtypes of DCIS. The LumB subtype of DCIS is heterogeneous with considerable variability among the four IHC markers used in the present study. A low PR is strongly associated with HER2 (3+), in luminal subtypes (p &amp;lt; 0.0004, Fisher's exact). Citation Format: Hossein Schandiz, Lorant Farkas, Daehoon Park, Solveig Norheim Andersen, Jürgen Geisler, Torill Sauer. Towards personalized medicine for DCIS - the role of hormone receptors, HER2, and Ki67 status in high-grade DCIS [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-25-10.

Abstract PR05: Spatial transcriptomics of ductal carcinoma in situ reveal subtype specific differences in tumoral and stromal cell compartments

Abstract Up to 50% of patients diagnosed with ductal carcinoma in situ (DCIS) never experience progression to invasive disease, even if left untreated. Current knowledge of what makes DCIS become invasive is limited and we lack diagnostic tools to predict which patients can be spared treatment. Escape of tumor cells from the breast ducts is influenced by characteristics of the tumor cells, the microenvironment surrounding the ducts, and the interplay between the two. Intraductal tumor cells in DCIS are not physically in contact with extraductal stromal cells. Until now, analyses of DCIS tissue have mainly been performed on bulk tissue, however, this approach does not take into consideration the unique morphology of DCIS. We used the Nanostring GeoMX® digital spatial profiling platform to determine the transcriptome of DCIS tumor cells and the surrounding stromal cells separately. From a large cohort of &amp;gt;500 DCIS cases from Akershus and Oslo University Hospitals, we selected 23 pure DCIS cases, grade 3, of different molecular subtypes: triple negative (TN), Luminal A (LumA) and HER2-enriched. At least four tumor-stroma pairs were selected from each case. Bioinformatic analyses were used to explore both tumor and stroma expression data and the interplay between the two cellular compartments. There were distinct gene expression differences between DCIS of different subtypes. Intertumoral heterogeneity was larger than intratumoral heterogeneity in all subtypes. This was also apparent in stromal cell compartments, although less pronounced. Gene ontology analyses of DCIS tumor cells from each of the three subtypes showed several overlapping biological processes, suggesting common mechanisms for regulating tumor cell growth in DCIS. We also found subtype specific differences: Tumor cells of the TN subtype showed upregulation of protein synthesis. Processes associated with RNA splicing were upregulated in tumor cells of the LumA subtype, whereas the HER2-enriched subtype showed increased DNA repair activity. The immune microenvironment differed between the subtypes: LumA DCIS were characterized by lower immune cell infiltration than TN and HER2-enriched, however, there was variation between and within the cases. Using in silico cell deconvolution, we found that B-cells were more abundant in HER2-enriched tumors, while T-cells were more common in TN and LumA. T-regulatory cells were found in all subtypes. The GeoMX® digital spatial profiling platform is well suited for exploring the transcriptome of DCIS samples. We found biologically relevant differences between tumor cells of the different molecular subtypes already at the DCIS stage in breast cancer progression. The immune cell composition surrounding DCIS lesions also differed between the subtypes. Further studies of the tumor-stroma interplay are required to understand the processes involved in progression of DCIS to invasive disease. Citation Format: Helga Bergholtz, Jens Henrik Norum, Tonje G Lien, Hossein Schandiz, Torill Sauer, Jürgen Geisler, Therese Sørlie. Spatial transcriptomics of ductal carcinoma in situ reveal subtype specific differences in tumoral and stromal cell compartments [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr PR05.

Deep-learning model to improve histological grading and predict upstaging of atypical ductal hyperplasia / ductal carcinoma in situ on breast biopsy.

Risk stratification of atypical ductal hyperplasia (ADH) and ductal carcinoma in situ (DCIS), diagnosed using breast biopsy, has great clinical significance. Clinical trials are currently exploring the possibility of active surveillance for low-risk lesions, whereas axillary lymph node staging may be considered during surgical planning for high-risk lesions. We aimed to develop a machine-learning algorithm based on whole-slide images of breast biopsy specimens and clinical information to predict the risk of upstaging to invasive breast cancer after wide excision. Patients diagnosed with ADH/DCIS on breast biopsy were included in this study, comprising 592 (740 slides) and 141 (198 slides) patients in the development and independent testing cohorts, respectively. Histological grading of the lesions was independently evaluated by two pathologists. Clinical information, including biopsy method, lesion size, and Breast Imaging Reporting and Data System (BI-RADS) classification of ultrasound and mammograms, were collected. Deep DCIS consisted of three deep neural networks to evaluate nuclear grade, necrosis, and stromal reactivity. Deep DCIS output comprised five parameters: total patches, lesion extent, Deep Grade, Deep Necrosis, and Deep Stroma. Deep DCIS highly correlated with the pathologists' evaluations of both slide- and patient-level labels. All five parameters of Deep DCIS were significantly associated with upstaging to invasive carcinoma in subsequent wide excisional specimens. Using multivariate logistic regression, Deep DCIS predicted upstaging to invasive carcinoma with an area under the curve (AUC) of 0.81, outperforming pathologists' evaluation (AUC, 0.71 and 0.69). After including clinical and hormone receptor status information, performance further improved (AUC, 0.87). This combined model retained its predictive power in two subgroup analyses: the first subgroup included unequivocal DCIS (excluding cases of ADH and DCIS suspicious for microinvasion) (AUC, 0.83), while the second excluded cases of high-grade DCIS (AUC, 0.81). The model was validated in an independent testing cohort (AUC, 0.81). This study demonstrated that deep-learning models can refine histological evaluation of ADH and DCIS on breast biopsies, which may help guide future treatment planning.

Sonography-based multimodal information platform for identifying the surgical pathology of ductal carcinoma in situ

BackgroundThe risk of ductal carcinoma in situ (DCIS) identified by biopsy often increases during surgery. Therefore, confirming the DCIS grade preoperatively is necessary for clinical decision-making. PurposeTo train a three-classification deep learning (DL) model based on ultrasound (US), combining clinical data, mammography (MG), US, and core needle biopsy (CNB) pathology to predict low-grade DCIS, intermediate-to-high-grade DCIS, and upstaged DCIS. Materials and MethodsData of 733 patients with 754 DCIS cases confirmed by biopsy were retrospectively collected from May 2013 to June 2022 (N1), and other data (N2) were confirmed by biopsy as low-grade DCIS. The lesions were randomly divided into training (n=471), validation (n=142), and test (n = 141) sets to establish the DCIS-Net. Information on the DCIS-Net, clinical (age and sign), US (size, calcifications, type, breast imaging reporting and data system [BI-RADS]), MG (microcalcifications, BI-RADS), and CNB pathology (nuclear grade, architectural features, and immunohistochemistry) were collected. Logistic regression and random forest analyses were conducted to develop Multimodal DCIS-Net to calculate the specificity, sensitivity, accuracy, receiver operating characteristic curve, and area under the curve (AUC). ResultsIn the test set of N1, the accuracy and AUC of the multimodal DCIS-Net were 0.752–0.766 and 0.859–0.907 in the three-classification task, respectively. The accuracy and AUC for discriminating DCIS from upstaged DCIS were 0.751–0.780 and 0.829–0.861, respectively. In the test set of N2, the accuracy and AUC of discriminating low-grade DCIS from upstaged low-grade DCIS were 0.769–0.987 and 0.818–0.939, respectively. DL was ranked from one to five in the importance of features in the multimodal-DCIS-Net. ConclusionBy developing the DCIS-Net and integrating it with multimodal information, diagnosing low-grade DCIS, intermediate-to high-grade DCIS, and upstaged DCIS is possible. It can also be used to distinguish DCIS from upstaged DCIS and low-grade DCIS from upstaged low-grade DCIS, which could pave the way for the DCIS clinical workflow.

Temporal changes in the incidence of ductal carcinoma in situ in Goiânia, Brazil: a population-based study

Introduction: Mammography screening has resulted in a considerable increase in the diagnosis of early-stage tumors in various countries. However, most available data refer to high-income countries, hospital-based studies, or studies with limited follow-up. Therefore the aim of this study was to determine the incidence of ductal carcinoma in situ (DCIS) in Goiânia, Brazil. Methods: Ecological study among residents of the city of Goiânia, Brazil. We included all the DCIS cases registered at the Goiânia population-based cancer registry between 1994 and 2010. Crude incidence and age-standardized incidence rates (using the world standard population) were calculated. Poisson regression was used to analyze temporal changes, with the average annual percent change (AAPC) in the crude and age-standardized incidence rates being calculated. Results: There were 261 cases of DCIS, with an annual incidence rate that ranged from 0.58 to 4.21 per 1,000 women (crude and standardized) over the period. The number of cases of DCIS in the 40–49 and 60–69-years age groups increased significantly (p&lt;0.01). The AAPC of the crude incidence rate for the period was 11.88% per year (95%CI 9–15; p&lt;0.01) and the standardized rate was 11.89% per year (95%CI 9–15; p&lt;0.01). Conclusions: The incidence of DCIS in Goiânia increased between 1994 and 2010, possibly due to improved mammography screening. The present study, which was conducted in a consolidated population-based cancer registry (PBCR) and involved an extensive follow-up time, could contribute towards increasing epidemiological knowledge on DCIS and its variations around the world.

Cytomorphological Assessment in Aspirates of Ductal Carcinoma in situ: Correlations with Histopathologic Grade, Architectural Pattern, and Invasion

Introduction: Fine-needle aspiration biopsy (FNAB) of the breast is an effective and widely adopted diagnostic technique. Histopathologic grading of ductal carcinoma in situ (DCIS) has prognostic significance. In this current study, FNAB of DCIS was reviewed to identify parameters that predict grading, histopathologic architecture, and presence of invasion in DCIS. Methods: Aspirates from histopathology-proven cases of DCIS were retrieved and reviewed for cytomorphologic parameters including cellularity, composition, epithelial fragment architecture cellular/nuclear features. Results: In total 104 aspirates were reviewed. Cytopathologic cellular features – large nuclear size (p = 0.005), prominent nucleoli (p = 0.011), increased nuclear membrane irregularity (p = 0.043), high variation in nuclear size (p = 0.025), and presence of apoptotic figures in epithelial structures (p < 0.001); and background debris (p = 0.033) correlated with a high-grade diagnosis. Cytoplasmic vacuolation (p = 0.034) was seen exclusively in non-high-grade aspirates. Epithelial fragment architecture did not correlate with grading. A predominance (≥50%) of solid aggregates and papillary fragments on FNAB correlated with histopathologically solid (p = 0.039, p = 0.005) and papillary (p = 0.029, < p = 0.001) patterns. No parameter showed correlation with invasion. Conclusion: FNAB is effective in predicting DCIS grading. Epithelial fragment architecture assessment is limited to papillary or solid types, and FNAB cannot predict focal invasion in DCIS.

A case of ductal carcinoma in situ (DCIS) with markedly elevated CA15-3 levels requiring 2 years of diagnosis

BackgroundCA15-3 is often elevated in breast cancer recurrence and rarely in ductal carcinoma in situ (DCIS). We report a case of DCIS with elevated CA15-3 levels, which was diagnosed after over 2 years of follow-up.Case presentationA 74-year-old woman presented with a left-sided breast mass and pain. Redness, swelling, and induration were observed in the left breast. Ultrasonography revealed a non-mass lesion in the left 3 o'clock position, skin thickening, and axillary lymphadenopathy. Serum CA15-3 levels were markedly high at 640 U/mL, suggesting inflammatory breast cancer. However, biopsies showed no malignancy. We diagnosed chronic mastitis with elevated CA15-3 levels and followed up with magnetic resonance imaging and a biopsy, as needed. Finally, DCIS was diagnosed 27 months after the first visit. She underwent a left mastectomy and a sentinel lymph node biopsy; DCIS had spread 6.5 cm and was immunohistochemically positive for CA15-3. No metastasis was found in the lymph nodes, but incidental Hodgkin lymphoma was observed. Postoperative normalization of CA15-3 levels indicated that she had DCIS with elevated CA15-3 levels. The patient underwent chemotherapy for Hodgkin lymphoma postoperatively, and there was no evidence of recurrence 1 year after surgery.ConclusionHigh CA15-3 levels can also be observed in DCIS, indicating that CA15-3 should not be used solely in breast cancer staging.