Cases Of Ductal Carcinoma In Situ Research Articles

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 & 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 < 0.0001, Chi-square). In contrast, there was a statistically significant reduction in ER-receptor expression at a cut-off < 10% in LumA compared to the latter subtypes. The proportion of cases with PR-receptors with a cut-off of < 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 < 20% and HER2 (3+) in luminal subtypes (p < 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 < 1% to > 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 < 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 < 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 >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.

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.

Performance of artificial intelligence in 7533 consecutive prevalent screening mammograms from the BreastScreen Australia program.

To assess the performance of an artificial intelligence (AI) algorithm in the Australian mammography screening program which routinely uses two independent readers with arbitration of discordant results. A total of 7533 prevalent round mammograms from 2017 were available for analysis. The AI program classified mammograms into deciles on the basis of breast cancer (BC) risk. BC diagnoses, including invasive BC (IBC) and ductal carcinoma in situ (DCIS), included those from the prevalent round, interval cancers, and cancers identified in the subsequent screening round two years later. Performance was assessed by sensitivity, specificity, positive and negative predictive values, and the proportion of women recalled by the radiologists and identified as higher risk by AI. Radiologists identified 54 women with IBC and 13 with DCIS with a recall rate of 9.7%. In contrast, 51 of 54 of the IBCs and 12/13 cases of DCIS were within the higher AI score group (score 10), a recall equivalent of 10.6% (a difference of 0.9% (CI -0.03 to 1.89%, p= 0.06). When IBCs were identified in the 2017 round, interval cancers classified as false negatives or with minimal signs in 2017, and cancers from the 2019 round were combined, the radiologists identified 54/67 and 59/67 were in the highest risk AI category (sensitivity 80.6% and 88.06 % respectively, a difference that was not different statistically). As the performance of AI was comparable to that of expert radiologists, future AI roles in screening could include replacing one reader and supporting arbitration, reducing workload and false positive results. AI analysis of consecutive prevalent screening mammograms from the Australian BreastScreen program demonstrated the algorithm's ability to match the cancer detection of experienced radiologists, additionally identifying five interval cancers (false negatives), and the majority of the false positive recalls. • The AI program was almost as sensitive as the radiologists in terms of identifying prevalent lesions (51/54 for invasive breast cancer, 63/67 when including ductal carcinoma in situ). • If selected interval cancers and cancers identified in the subsequent screening round were included, the AI program identified more cancers than the radiologists (59/67 compared with 54/67, sensitivity 88.06 % and 80.6% respectively p = 0.24). • The high negative predictive value of a score of 1-9 would indicate a role for AI as a triage tool to reduce the recall rate (specifically false positives).

Misleading images of ductal carcinoma in situ: 4 lymph nodes with metastasis

Since sentinel lymph node examination became routine, findings of benign ectopic breast tissue in lymph nodes have increased. We report images of ductal carcinoma in situ (DCIS) in four lymph nodes in a 76-year-old woman with bilateral breast carcinoma. The right lumpectomy showed intermixed invasive lobular and ductal carcinoma, plus DCIS. 19 nodes were isolated in the axillary lymphadenectomy, 4 of which displayed solid and cribriform DCIS. Myosin and p63 immunohistochemical techniques were positive, suggesting an erroneous diagnosis of “metastatic DCIS”. A further three cases of DCIS in lymph nodes have been previously reported, all with a distinct layer of myoepithelial cells with actin, myosin or p63. Biologically, these images of DCIS in lymph nodes are not credible and three major hypotheses have been proposed to explain these findings: Iatrogenic Mechanical Transport, Revertant DCIS, and primary DCIS of lymph nodes. We consider the first one the most plausible explanation. Our case is unique as several, rare findings are simultaneously observed. More new cases, together with additional immunohistochemical techniques and molecular testing on previous cases, are needed to find a definitive explanation of this histologic finding.

DCIS and LCIS: Are the Risk Factors for Developing In Situ Breast Cancer Different?

Ductal carcinoma in situ (DCIS) is widely accepted as a precursor of invasive ductal carcinoma (IDC). Lobular carcinoma in situ (LCIS) is considered a risk factor for invasive lobular carcinoma (ILC), and it is unclear whether LCIS is also a precursor. Therefore, it would be expected that similar risk factors predispose to both DCIS and IDC, but not necessarily LCIS and ILC. This study examined associations with risk factors using data from 3075 DCIS cases, 338 LCIS cases, and 1584 controls aged 35-60, recruited from the UK-based GLACIER and ICICLE case-control studies between 2007 and 2012. Analysis showed that breastfeeding in parous women was protective against DCIS and LCIS, which is consistent with research on invasive breast cancer (IBC). Additionally, long-term use of HRT in post-menopausal women increased the risk of DCIS and LCIS, with a stronger association in LCIS, similar to the association with ILC. Contrary to findings with IBC, parity and the number of births were not protective against DCIS or LCIS, while oral contraceptives showed an unexpected protective effect. These findings suggest both similarities and differences in risk factors for DCIS and LCIS compared to IBC and that there may be justification for increased breast surveillance in post-menopausal women taking long-term HRT.

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.

Subtypes of high-grade breast ductal carcinoma in situ (DCIS): incidence and potential clinical impact

ObjectiveThe purpose of this study was to investigate and classify the molecular subtypes of high-grade ductal carcinoma in situ (DCIS) and identify possible high-risk subtypes. The heterogenicity of DCIS with variable clinical and histopathological presentations has been recognized. Nevertheless, only histopathological grading and diameter are currently implemented in clinical decision-making following the diagnosis of DCIS. The molecular subtypes of DCIS and their IHC surrogate markers have not been defined in conventional treatment guidelines and recommendations. We applied the definitions of molecular subtypes according to the IHC surrogate markers defined for IBC and subclassified high-grade DCIS, accordingly.MethodsHistopathological specimens were collected, revised, and regraded from 494 patients diagnosed with DCIS between 1996 and 2018. Other in situ and papillary lesions observed in breast biopsies were excluded from this study. 357 high-grade DCIS cases were submitted to IHC analysis. The markers investigated were ER, PR, HER2, and Ki67.Results45 cases were classified as grade 1, 19 as grade 2, and 430 as grade 3. Sixty patients with high-grade DCIS had an additional invasive component in the surgical specimen. Thirty-three patients were diagnosed with recurrent DCIS or invasive cancer (minimum one year after their primary DCIS diagnosis). The proportions of luminal A and luminal B HER2-negative subtypes varied depending on whether 2011 or 2013 St. Gallen Consensus Conference guidelines were adopted. Luminal A was the most prevalent subtype, according to both classifications. The luminal B HER2-positive subtype was found in 22.1% of cases, HER2-enriched subtype in 21.8%, and TPN subtype in 5.6%. There were strong indications that HER2-enriched subtype was significantly more frequent among DCIS with invasive component (p = 0.0169).ConclusionsHigh-grade DCIS exhibits all the molecular subtypes previously identified in IBC, but with a somewhat different distribution in our cohort. HER2-enriched subtype is substantially related to the presence of an invasive component in DCIS; consequently, it is regarded as a high-risk entity.

Preoperative diagnosis of ductal carcinoma in situ of the breast over a 24-year period.

The method of diagnosis of ductal carcinoma in situ (DCIS) has changed since the 1980s. The aim of this audit was to assess changes in the preoperative diagnosis of DCIS since the introduction of needle core biopsy, particularly the proportion with a preoperative biopsy diagnosis of DCIS. The preoperative diagnoses of patients with a final diagnosis of DCIS in the surgical specimen were reviewed (i) in 809 patients who presented through breast screening from 1997 to 2021, and (ii) in all patients in 5 individual years at 5-year intervals from 2000 to 2020 (254 in total). For screening-detected DCIS the proportion with a preoperative diagnosis of DCIS increased from 75% to 98% over the study period. In a detailed analysis of all cases of DCIS in 5 separate years the proportion with a preoperative diagnosis of DCIS increased from 68% in 2000 to 96% in 2020. For high-grade DCIS the proportion increased from 87% to 97%, and for low- or intermediate-grade DCIS from 48% to 93%. The proportion of women who had vacuum-assisted biopsy increased from 7% in 2000 to 58% in 2015. There was a small increase in the number of biopsies that had basal cytokeratin and oestrogen receptor immunohistochemistry to aid diagnosis. There has been an increase in the preoperative diagnosis of DCIS, particularly of low- or intermediate-grade, over the last two decades. The increasing use of vacuum-assisted biopsy is likely to be a major contributory factor to this increase.

Dendritic Cell Subpopulations Are Associated with Morphological Features of Breast Ductal Carcinoma In Situ.

Ductal carcinoma in situ (DCIS) is the preinvasive form of breast cancer (BC). It is disputed whether all cases of DCIS require extensive treatment as the overall risk of progression to BC is estimated at 40%. Therefore, the crucial objective for researchers is to identify DCIS with significant risk of transformation into BC. Dendritic cells (DC) are professional antigen presenting cells and as such play a pivotal role in the formation of immune cells that infiltrate in breast tumors. The aim of this study was to investigate the relationship between the density of DCs with different superficial antigens (CD1a, CD123, DC-LAMP, DC-SIGN) and various histopathological characteristics of DCIS. Our evaluation indicated that CD123+ and DC-LAMP+ cells were strongly associated with maximal tumor size, grading and neoductgenesis. Together with CD1a+ cells, they were negatively correlated with hormonal receptors expression. Furthermore, the number of DC-LAMP+ cells was higher in DCIS with comedo necrosis, ductal spread, lobular cancerization as well as comedo-type tumors, while CD1a+ cells were abundant in cases with Paget disease. We concluded that different subpopulations of DCs relate to various characteristics of DCIS. Of the superficial DCs markers, DC-LAMP seems particularly promising as a target for further research in this area.

Association of quantitative MRI features of DCIS with surgical upstaging and recurrence after treatment.

e12540 Background: Ductal carcinoma in situ (DCIS) is a common preinvasive breast cancer typically identified on screening mammography. Pure DCIS is nonlethal, but 20-25% of DCIS cases identified on core biopsy upstage to invasive cancer at surgery and about half progress to invasive disease if not treated. As a result, most DCIS patients undergo excision and radiation therapy. DCIS score is a multigene assay that can aid in radiation treatment decision-making and serve as a surrogate marker of ipsilateral breast recurrence (IBR) risk. Breast MRI can accurately depict DCIS span and has potential to assess biology and IBR risk. We sought to assess the associations of advanced quantitative MRI features with upstaging and DCIS score. Methods: In this IRB-approved single institution prospective clinical trial, patients recommended for biopsy of calcifications or those who had residual calcifications after a biopsy yielding pure DCIS consented to receive a multiparametric breast MRI (dynamic contrast-enhanced and diffusion-weighted) prior to surgery. The following quantitative MRI features were obtained: functional tumor volume (FTV), volume transfer constant (Ktrans), extracellular extravascular volume fraction (ve), background parenchymal enhancement (BPE), and apparent diffusion coefficient (ADC). Patients with biopsy-proven DCIS were followed through completion of surgery to determine upstaging. DCIS score was performed in those with sufficient excision-proven pure DCIS. Associations of MRI features with invasive upstaging and DCIS score and were evaluated by Spearman’s correlation and the Wilcoxon rank-sum test. Results: Of the 120 patients enrolled in the study, 57 (48%) were diagnosed with pure DCIS on core biopsy. Fifty-five patients underwent surgery, 13 of whom (24%) upstaged to invasive disease. Thirty-eight pure DCIS lesions had sufficient tissue to generate a DCIS Score. Several MRI features were associated with upstaging and DCIS Score: Ktrans (p &lt; 0.01) was significantly higher in lesions that upstaged to invasive disease. Higher DCIS scores were associated with lower Ktrans (p = 0.04) and ve (p = 0.05) values. No statistically significant associations were observed between FTV, BPE, or ADC and invasive upstaging or DCIS Score. Conclusions: This trial suggests advanced quantitative MRI features may help identify DCIS lesions at risk for upstaging to invasive disease and IBR. Specifically, an imaging feature of vascular permeability (Ktrans) showed an expected positive association with upstaging. Imaging features of vascular permeability (Ktrans and ve) showed counterintuitive inverse associations with DCIS Scores. Future research in larger cohorts is needed to validate these findings, better determine the biological reasons for these associations, and determine whether MRI features can be used clinically to optimize DCIS treatment. Clinical trial information: NCT03495011 .

Transcriptome analysis reveals differences in cell cycle, growth and migration related genes that distinguish fibroblasts derived from pre-invasive and invasive breast cancer.

As the most common form of pre-invasive breast cancer, ductal carcinoma in situ (DCIS) affects over 50,000 women in the US annually. Despite standardized treatment involving lumpectomy and radiation therapy, up to 25% of patients with DCIS experience disease recurrence often with invasive ductal carcinoma (IDC), indicating that a subset of patients may be under-treated. As most DCIS cases will not progress to invasion, many patients may experience over-treatment. By understanding the underlying processes associated with DCIS to IDC progression, we can identify new biomarkers to determine which DCIS cases may become invasive and improve treatment for patients. Accumulation of fibroblasts in IDC is associated with disease progression and reduced survival. While fibroblasts have been detected in DCIS, little is understood about their role in DCIS progression. We sought to determine 1) whether DCIS fibroblasts were similar or distinct from normal and IDC fibroblasts at the transcriptome level, and 2) the contributions of DCIS fibroblasts to breast cancer progression. Fibroblasts underwent transcriptome profiling and pathway analysis. Significant DCIS fibroblast-associated genes were further analyzed in existing breast cancer mRNA databases and through tissue array immunostaining. Using the sub-renal capsule graft model, fibroblasts from normal breast, DCIS and IDC tissues were co-transplanted with DCIS.com breast cancer cells. Through transcriptome profiling, we found that DCIS fibroblasts were characterized by unique alterations in cell cycle and motility related genes such as PKMYT1, TGF-α, SFRP1 and SFRP2, which predicted increased cell growth and invasion by Ingenuity Pathway Analysis. Immunostaining analysis revealed corresponding increases in expression of stromal derived PKMYT1, TGF-α and corresponding decreases in expression of SFRP1 and SFRP2 in DCIS and IDC tissues. Grafting studies in mice revealed that DCIS fibroblasts enhanced breast cancer growth and invasion associated with arginase-1+ cell recruitment. DCIS fibroblasts are phenotypically distinct from normal breast and IDC fibroblasts, and play an important role in breast cancer growth, invasion, and recruitment of myeloid cells. These studies provide novel insight into the role of DCIS fibroblasts in breast cancer progression and identify some key biomarkers associated with DCIS progression to IDC, with important clinical implications.

Abstract 5561: Molecular imaging of calcification and soft tissue defines prognosis of DCIS to invasive breast cancer

Abstract Introduction: Ductal carcinoma in situ (DCIS) is a non-obligatory precursor to invasive breast cancer (IBC). Currently there are no reliable markers to indicate if DCIS will progress IBC after an initial treatment, or if it will remain as DCIS. Because of this uncertainty, standard treatment for DCIS includes surgery with or without radiotherapy. Breast microcalcifications are a common feature in the mammographic detection of DCIS and occur in around 80% of DCIS and 50% of IBC. Despite this, their potential role in the development of DCIS and with progression to IBC remains largely unexplored. The main aim of the study is to identify novel biomarkers for DCIS prognosis, based on chemical and molecular compositional changes of calcifications and their surrounding soft tissue in DCIS. Methods: Consecutive tissue section from 422 patients were analyzed using mid-infrared and Raman hyperspectral imaging and X-ray diffraction (XRD) and included (i) ‘DCIS controls’ (DCIS without recurrence) (n=193), (ii) ‘DCIS cases’ (DCIS which subsequently recurred as an invasive disease) (n=123), (iii) ‘synchronous DCIS with invasive cancer’ (n=44) and ‘benign’ (n=62) groups. Both parametrized and data mining approaches including cluster analysis, unsupervised and supervised multivariate statistical analysis and biomodelling based on leave-one-sample-out cross-validation were used to classify the data and develop DCIS prediction models. Results: Prediction modelling of 314 calcification and soft tissue spectral images from 170 patients (‘DCIS controls’=118 versus ‘DCIS cases’=52) showed an AUROC mean value of 0.85 in distinguishing DCIS that did not recur from DCIS that recurred as IBC. Prediction modelling of XRD data from 124 patients (‘DCIS controls’=67 versus ‘DCIS cases’=57) showed an AUROC mean value of 0.80. The calcification spectral features showed pathology specific changes in phosphate to carbonate ratio as well as changes in magnesium whitlockite content. The epithelial features showed changes in protein secondary structure and content indicating structural remodeling between the two groups. XRD parametrized data analysis corroborated these observations in terms of difference in whitlockite, as well as crystallinity/unit axis of the measured calcifications. The prediction models are currently being tested independently on the remaining sample data. Conclusion: Complementary chemical and molecular analysis of breast calcifications and soft tissue using spectroscopy and XRD imaging predicts the likely progression of DCIS to IBC. Subject to the ongoing validation of biomarkers, these approaches appear as promising DCIS risk assessment tools that can inform diagnosis, prognosis and treatment options. Citation Format: Jayakrupakar Nallala, Doriana Calabrese, Sarah Gosling, Esther Lips, Rachel Factor, Allison Hall, Sarah Pinder, Ihssane Bouybayoune, Elinor Sawyer, Lorraine King, Jeffrey Marks, Thomas Lynch, Donna Pinto, Jelle Wesseling, Shelley Hwang, Keith Rogers, Nick Stone, Grand Challenge PRECISION consortium. Molecular imaging of calcification and soft tissue defines prognosis of DCIS to invasive breast cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5561.

High-Grade Ductal Carcinoma In Situ of the Breast With Regressive Changes: Radiological and Clinicopathological Findings.

Tumour regression is defined as continuity of changes leading to the elimination of a neoplastic population and is reflected as periductal fibrosis and intraductal tumour attenuation. The aim of this study was to describe the radiological and clinicopathological characteristics of high-grade breast ductal carcinoma in situ (DCIS) with regressive changes (RC). Thirty-two cases of high-grade DCIS with RC on biopsy specimens followed by excision were included. The mammographic, ultrasonographic (US), and magnetic resonance imaging (MRI) findings of cases were retrospectively reviewed according to the breast imaging reporting and data system (BI-RADS) lexicon. Clinical and histopathological findings [comedonecrosis, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) status and Ki-67 proliferation index] were recorded. The rate of upgrade to invasive cancer after surgical excision and lymph node involvement were evaluated. The most common mammographic finding was microcalcifications alone (68.8%). The most frequently seen findings on US were microcalcifications only (21.9%), followed by microcalcifications and hypoechoic area (18.7%). On MRI, most lesions presented as clumped non-mass enhancement with segmental distribution. ER/PR negativity (53.1%, 65.6%), HER2 positivity (56.3%) and high Ki-67 (62.5%), which are known to be associated with more aggressive behavior, were found to be proportionally higher. The rate of upgrade to invasive cancer was 21.8%. DCIS with RC lesions present most often as microcalcifications alone on both mammography and US. MRI features are not distinguishable from those of other DCIS lesions. DCIS with RC lesions show biomarker status reflecting more aggressive behavior and high upgrade rate to invasive cancer.

TGFβ-mediated MMP13 secretion drives myoepithelial cell dependent breast cancer progression

Ductal carcinoma in situ (DCIS) is a non-obligate precursor of invasive breast cancer. Virtually all women with DCIS are treated, despite evidence suggesting up to half would remain with stable, non-threatening, disease. Overtreatment thus presents a pressing issue in DCIS management. To understand the role of the normally tumour suppressive myoepithelial cell in disease progression we present a 3D in vitro model incorporating both luminal and myoepithelial cells in physiomimetic conditions. We demonstrate that DCIS-associated myoepithelial cells promote striking myoepithelial-led invasion of luminal cells, mediated by the collagenase MMP13 through a non-canonical TGFβ – EP300 pathway. In vivo, MMP13 expression is associated with stromal invasion in a murine model of DCIS progression and is elevated in myoepithelial cells of clinical high-grade DCIS cases. Our data identify a key role for myoepithelial-derived MMP13 in facilitating DCIS progression and point the way towards a robust marker for risk stratification in DCIS patients.