The clinical significance of oestrogen receptor expression in breast ductal carcinoma in situ

Accurate and efficient integrative reference-informed spatial domain detection for spatial transcriptomics

Spatially resolved transcriptomics (SRT) studies are becoming increasingly common and large, offering unprecedented opportunities in mapping complex tissue structures and functions. Here we present integrative and reference-informed tissue segmentation (IRIS), a computational method designed to characterize tissue spatial organization in SRT studies through accurately and efficiently detecting spatial domains. IRIS uniquely leverages single-cell RNA sequencing data for reference-informed detection of biologically interpretable spatial domains, integrating multiple SRT slices while explicitly considering correlations both within and across slices. We demonstrate the advantages of IRIS through in-depth analysis of six SRT datasets encompassing diverse technologies, tissues, species and resolutions. In these applications, IRIS achieves substantial accuracy gains (39-1,083%) and speed improvements (4.6-666.0) in moderate-sized datasets, while representing the only method applicable for large datasets including Stereo-seq and 10x Xenium. As a result, IRIS reveals intricate brain structures, uncovers tumor microenvironment heterogeneity and detects structural changes in diabetes-affected testis, all with exceptional speed and accuracy.

Progression from ductal carcinoma in situ to invasive breast cancer: molecular features and clinical significance

Ductal carcinoma in situ (DCIS) represents pre-invasive breast carcinoma. In untreated cases, 25-60% DCIS progress to invasive ductal carcinoma (IDC). The challenge lies in distinguishing between non-progressive and progressive DCIS, often resulting in over- or under-treatment in many cases. With increasing screen-detected DCIS in these years, the nature of DCIS has aroused worldwide attention. A deeper understanding of the biological nature of DCIS and the molecular journey of the DCIS-IDC transition is crucial for more effective clinical management. Here, we reviewed the key signaling pathways in breast cancer that may contribute to DCIS initiation and progression. We also explored the molecular features of DCIS and IDC, shedding light on the progression of DCIS through both inherent changes within tumor cells and alterations in the tumor microenvironment. In addition, valuable research tools utilized in studying DCIS including preclinical models and newer advanced technologies such as single-cell sequencing, spatial transcriptomics and artificial intelligence, have been systematically summarized. Further, we thoroughly discussed the clinical advancements in DCIS and IDC, including prognostic biomarkers and clinical managements, with the aim of facilitating more personalized treatment strategies in the future. Research on DCIS has already yielded significant insights into breast carcinogenesis and will continue to pave the way for practical clinical applications.

Is There a Special Role for Ovarian Hormones in the Pathogenesis of Lobular Carcinoma?

Lobular carcinoma represent the most common special histological subtype of breast cancer, with the majority classed as hormone receptor positive. Rates of invasive lobular carcinoma in postmenopausal women have been seen to increase globally, while other hormone receptor-positive breast cancers proportionally have not followed the same trend. This has been linked to exposure to exogenous ovarian hormones such as hormone replacement therapy. Reproductive factors resulting in increased lifetime exposure to endogenous ovarian hormones have also been linked to an increased risk of lobular breast cancer, and taken together, these data make a case for the role of ovarian hormones in the genesis and progression of the disease. In this review, we summarize current understanding of the epidemiological associations between ovarian hormones and lobular breast cancer and highlight mechanistic links that may underpin the etiology and biology.

Ductal Carcinoma in Situ: State-of-the-Art Review

Ductal carcinoma in situ (DCIS) is a nonobligate precursor of invasive cancer, and its detection, diagnosis, and management are controversial. DCIS incidence grew with the expansion of screening mammography programs in the 1980s and 1990s, and DCIS is viewed as a major driver of overdiagnosis and overtreatment. For pathologists, the diagnosis and classification of DCIS is challenging due to undersampling and interobserver variability. Understanding the progression from normal breast tissue to DCIS and, ultimately, to invasive cancer is limited by a paucity of natural history data with multiple proposed evolutionary models of DCIS initiation and progression. Although radiologists are familiar with the classic presentation of DCIS as asymptomatic calcifications at mammography, the expanded pool of modalities, advanced imaging techniques, and image analytics have identified multiple potential biomarkers of histopathologic characteristics and prognosis. Finally, there is growing interest in the nonsurgical management of DCIS, including active surveillance, to reduce overtreatment and provide patients with more personalized management options. However, current biomarkers are not adept at enabling identification of occult invasive disease at biopsy or accurately predicting the risk of progression to invasive disease. Several active surveillance trials are ongoing and are expected to better identify women with low-risk DCIS who may avoid surgery.

Improving ductal carcinoma in situ classification by convolutional neural network with exponential linear unit and rank-based weighted pooling

Ductal carcinoma in situ (DCIS) is a pre-cancerous lesion in the ducts of the breast, and early diagnosis is crucial for optimal therapeutic intervention. Thermography imaging is a non-invasive imaging tool that can be utilized for detection of DCIS and although it has high accuracy (~ 88%), it is sensitivity can still be improved. Hence, we aimed to develop an automated artificial intelligence-based system for improved detection of DCIS in thermographs. This study proposed a novel artificial intelligence based system based on convolutional neural network (CNN) termed CNN-BDER on a multisource dataset containing 240 DCIS images and 240 healthy breast images. Based on CNN, batch normalization, dropout, exponential linear unit and rank-based weighted pooling were integrated, along with L-way data augmentation. Ten runs of tenfold cross validation were chosen to report the unbiased performances. Our proposed method achieved a sensitivity of 94.08 ± 1.22%, a specificity of 93.58 ± 1.49 and an accuracy of 93.83 ± 0.96. The proposed method gives superior performance than eight state-of-the-art approaches and manual diagnosis. The trained model could serve as a visual question answering system and improve diagnostic accuracy.

Risk of hematologic malignancies after breast ductal carcinoma in situ treatment with ionizing radiation

The increased incidence of secondary hematologic malignancies (SHM) is a well-known, potentially fatal, complication after cancer treatment. It is unknown if patients with ductal carcinoma in situ (DCIS) of the breast treated with external beam radiotherapy (RT) and who survive long-term have increased risks of secondary hematologic malignancies (SHM), especially for low/intermediate-risk subsets with limited benefits from RT. DCIS patients in Surveillance, Epidemiology, and End Results (SEER) registries (1975-2016) were identified. Relative risks (RR), hazard ratio (HR), and standardized incidence ratios (SIR) were calculated to assess the SHM risk and subsequent survival times. SHM development, defined as a nonsynchronous SHM occurring ≥1 year after DCIS diagnosis, was our primary endpoint. Of 184,363 eligible patients with DCIS, 77,927 (42.3%) in the RT group, and 106,436 (57.7%) in the non-RT group, 1289 developed SHMs a median of 6.4 years (interquartile range, 3.5 to 10.3 years) after their DCIS diagnosis. Compared with DCIS patients in the non-RT group, RT was associated with increased early risk of developing acute lymphoblastic leukemia (ALL; hazard ratio, 3.15; 95% CI, 1.21 to 8.17; P = 0.02), and a delayed risk of non-Hodgkin lymphoma (NHL; hazard ratio, 1.33; 95% CI, 1.09 to 1.62; P < 0.001). This increased risk of ALL and NHL after RT was also observed in subgroup analyses restricted to low/intermediate-risk DCIS. In summary, our data suggest that RT after breast conserving surgery for DCIS patients should be cautiously tailored, especially for low and intermediate-risk patients. Long-term SHM surveillance after DCIS diagnosis is warranted.