Ductal Carcinoma In Situ of Breast: From Molecular Etiology to Therapeutic Management

MurSS: A Multi-Resolution Selective Segmentation Model for Breast Cancer

Accurately segmenting cancer lesions is essential for effective personalized treatment and enhanced patient outcomes. We propose a multi-resolution selective segmentation (MurSS) model to accurately segment breast cancer lesions from hematoxylin and eosin (H&E) stained whole-slide images (WSIs). We used The Cancer Genome Atlas breast invasive carcinoma (BRCA) public dataset for training and validation. We used the Korea University Medical Center, Guro Hospital, BRCA dataset for the final test evaluation. MurSS utilizes both low- and high-resolution patches to leverage multi-resolution features using adaptive instance normalization. This enhances segmentation performance while employing a selective segmentation method to automatically reject ambiguous tissue regions, ensuring stable training. MurSS rejects 5% of WSI regions and achieves a pixel-level accuracy of 96.88% (95% confidence interval (CI): 95.97-97.62%) and mean Intersection over Union of 0.7283 (95% CI: 0.6865-0.7640). In our study, MurSS exhibits superior performance over other deep learning models, showcasing its ability to reject ambiguous areas identified by expert annotations while using multi-resolution inputs.

Age and race/ethnicity differences in decisional conflict in women diagnosed with ductal carcinoma in situ

PURPOSE

Women diagnosed with ductal carcinoma in situ (DCIS) face confusion and uncertainty about treatment options. The objective of this study was to determine whether there are differences in decisional conflict about treatment by age and race/ethnicity.

METHODS

A cross-sectional survey was conducted of women (age ≥ 18) diagnosed with DCIS enrolled at Kaiser Permanente of Southern California. The Decisional Conflict Scale (DCS) measured personal perceptions of decision uncertainty, values clarity, and effective decision-making. We used a multivariable regression to study whether age, race, and ethnicity were associated with patient-reported DCS.

RESULTS

45% (N = 1395) of women who received the online survey, participated. The mean age was 56 (± 9.6) years, the majority were white. Compared to women younger than 50, women aged 60-69 reported lower overall DCS scores (-5.4; 95% CI -1.5 to -9.3). Women > 70 had lower values clarity scores (-9.0; 95% CI -2.8 to -15.2) about their treatment compared to women aged 50-59 and 60-69 (-7.1; 95% CI -2.9 to -11.3 and - 7.2; 95% CI -2.9 to -11.5) and likewise, lower effective decision-making scores (-5.4; 95% CI -1.7 to -9.2 and - 5.2; 95% CI -1.4 to -9.0) compared to women < 50. Compared to whites, blacks reported lower decision conflict (-4.4; 95% CI 0.04 to -8.8) and lower informed decision (-5.2; 95% CI -0.18 to -10.3) about DCIS treatment.

CONCLUSION

Younger women reported higher decisional conflict about DCIS treatment, compared to older women (> 70). Age based tailored discussions about treatment options, health education, and supportive decision-making interventions/tools may reduce decision conflict in future DCIS patients.

TRADE REGISTRATION

The IRB number is 10678.

KRT15 in early breast cancer screening and correlation with HER2 positivity, pathological grade and N stage

Objective: This study was designed to explore KRT15 dysregulation and its correlation with clinical characteristics among ductal carcinoma in situ (DCIS), DCIS with microinvasion (DCIS-MI) and invasive breast cancer (IBC) patients. Methods: KRT15 from lesion samples of 50 DCIS patients, 48 DCIS-MI patients and 50 IBC patients was detected by immunohistochemistry. Results: KRT15 discriminated IBC patients from DCIS patients (area under the curve [AUC] = 0.895; 95% CI = 0.836-0.954) and DCIS-MI patients (AUC = 0.707; 95% CI = 0.606-0.808). In DCIS patients, KRT15 was negatively correlated with pathological grade (p = 0.015). In DCIS-MI patients, KRT15 was positively related to estrogen receptor positivity but negatively associated with Ki-67 (both p < 0.05). In IBC patients, KRT15 was negatively linked to HER2 positivity, histological grade, N stage and tumor node metastasis stage (all p < 0.05). Conclusion: KRT15 assessment may help with early breast cancer screening.

Predicting Breast Cancer Events in Ductal Carcinoma In Situ (DCIS) Using Generative Adversarial Network Augmented Deep Learning Model

Standard clinicopathological parameters (age, growth pattern, tumor size, margin status, and grade) have been shown to have limited value in predicting recurrence in ductal carcinoma in situ (DCIS) patients. Early and accurate recurrence prediction would facilitate a more aggressive treatment policy for high-risk patients (mastectomy or adjuvant radiation therapy), and simultaneously reduce over-treatment of low-risk patients. Generative adversarial networks (GAN) are a class of DL models in which two adversarial neural networks, generator and discriminator, compete with each other to generate high quality images. In this work, we have developed a deep learning (DL) classification network that predicts breast cancer events (BCEs) in DCIS patients using hematoxylin and eosin (H & E) images. The DL classification model was trained on 67 patients using image patches from the actual DCIS cores and GAN generated image patches to predict breast cancer events (BCEs). The hold-out validation dataset (n = 66) had an AUC of 0.82. Bayesian analysis further confirmed the independence of the model from classical clinicopathological parameters. DL models of H & E images may be used as a risk stratification strategy for DCIS patients to personalize therapy.

Breast cancer vaccines: New insights into immunomodulatory and nano-therapeutic approaches

Breast cancer (BC) is known to be a highly heterogeneous disease that is clinically subdivided into four primary molecular subtypes, each having distinct morphology and clinical implications. These subtypes are principally defined by hormone receptors and other proteins involved (or not involved) in BC development. BC therapeutic vaccines [including peptide-based vaccines, protein-based vaccines, nucleic acid-based vaccines (DNA/RNA vaccines), bacterial/viral-based vaccines, and different immune cell-based vaccines] have emerged as an appealing class of cancer immunotherapeutics when used alone or combined with other immunotherapies. Employing the immune system to eliminate BC cells is a novel therapeutic modality. The benefit of active immunotherapies is that they develop protection against neoplastic tissue and readjust the immune system to an anti-tumor monitoring state. Such immunovaccines have not yet shown effectiveness for BC treatment in clinical trials. In recent years, nanomedicines have opened new windows to increase the effectiveness of vaccinations to treat BC. In this context, some nanoplatforms have been designed to efficiently deliver molecular, cellular, or subcellular vaccines to BC cells, increasing the efficacy and persistence of anti-tumor immunity while minimizing undesirable side effects. Immunostimulatory nano-adjuvants, liposomal-based vaccines, polymeric vaccines, virus-like particles, lipid/calcium/phosphate nanoparticles, chitosan-derived nanostructures, porous silicon microparticles, and selenium nanoparticles are among the newly designed nanostructures that have been used to facilitate antigen internalization and presentation by antigen-presenting cells, increase antigen stability, enhance vaccine antigenicity and remedial effectivity, promote antigen escape from the endosome, improve cytotoxic T lymphocyte responses, and produce humoral immune responses in BC cells. Here, we summarized the existing subtypes of BC and shed light on immunomodulatory and nano-therapeutic strategies for BC vaccination. Finally, we reviewed ongoing clinical trials on BC vaccination and highlighted near-term opportunities for moving forward.