Molecular Classification of Breast Cancer: Relevance and Challenges

MOB-CBAM: A dual-channel attention-based deep learning generalizable model for breast cancer molecular subtypes prediction using mammograms

BACKGROUND AND OBJECTIVE

Deep Learning models have emerged as a significant tool in generating efficient solutions for complex problems including cancer detection, as they can analyze large amounts of data with high efficiency and performance. Recent medical studies highlight the significance of molecular subtype detection in breast cancer, aiding the development of personalized treatment plans as different subtypes of cancer respond better to different therapies.

METHODS

In this work, we propose a novel lightweight dual-channel attention-based deep learning model MOB-CBAM that utilizes the backbone of MobileNet-V3 architecture with a Convolutional Block Attention Module to make highly accurate and precise predictions about breast cancer. We used the CMMD mammogram dataset to evaluate the proposed model in our study. Nine distinct data subsets were created from the original dataset to perform coarse and fine-grained predictions, enabling it to identify masses, calcifications, benign, malignant tumors and molecular subtypes of cancer, including Luminal A, Luminal B, HER-2 Positive, and Triple Negative. The pipeline incorporates several image pre-processing techniques, including filtering, enhancement, and normalization, for enhancing the model's generalization ability.

RESULTS

While identifying benign versus malignant tumors, i.e., coarse-grained classification, the MOB-CBAM model produced exceptional results with 99 % accuracy, precision, recall, and F1-score values of 0.99 and MCC of 0.98. In terms of fine-grained classification, the MOB-CBAM model has proven to be highly efficient in accurately identifying mass with (benign/malignant) and calcification with (benign/malignant) classification tasks with an impressive accuracy rate of 98 %. We have also cross-validated the efficiency of the proposed MOB-CBAM deep learning architecture on two datasets: MIAS and CBIS-DDSM. On the MIAS dataset, an accuracy of 97 % was reported for the task of classifying benign, malignant, and normal images, while on the CBIS-DDSM dataset, an accuracy of 98 % was achieved for the classification of mass with either benign or malignant, and calcification with benign and malignant tumors.

CONCLUSION

This study presents lightweight MOB-CBAM, a novel deep learning framework, to address breast cancer diagnosis and subtype prediction. The model's innovative incorporation of the CBAM enhances precise predictions. The extensive evaluation of the CMMD dataset and cross-validation on other datasets affirm the model's efficacy.

Real-world overall survival and characteristics of patients with ER-zero and ER-low HER2-negative breast cancer treated as triple-negative breast cancer: a Swedish population-based cohort study

Background

Estrogen receptor-low (ER-low) HER2-negative breast cancer has similar pathological and molecular characteristics as triple-negative breast cancer (TNBC), and it is questionable whether it should be considered a separate entity. When the international guidelines lowered the cutoff for ER positivity to ≥1% in 2010, the ≥10% threshold was kept in Sweden. ER-low breast cancer (ER 1-9%) is thus in Sweden treated as TNBC. We aimed to describe patient and tumor characteristics, treatment patterns and overall survival in a Swedish population-based cohort of patients with ER-zero and ER-low HER2-negative breast cancer treated as TNBC.

Methods

All TNBC cases diagnosed in Sweden 2008-2020 were included in a population-based cohort study. Patient, tumor and treatment characteristics were analyzed by ER-status (ER 0% vs 1-9%), and associations between subgroups compared using χ2 test. Survival endpoint was overall survival (OS), and Kaplan-Meier curves were estimated. Cox proportional hazards models were used to estimate adjusted hazard ratios comparing ER-low to ER-zero.

Findings

Of the 5655 tumors, 90.1% had an ER expression of 0%, while 9.9% were ER-low. ER-low tumors were grade III in 69.4% (80.8% in ER-zero tumors, p-value = 0.001), with a median Ki67 of 60% (63% in ER-zero tumors, p-value = 0.005). There were no significant differences in given chemotherapy (p = 0.546). A pathological complete response (pCR) was achieved in 28.1% of ER-low tumors (25.1% in ER-zero tumors). In the unadjusted analysis of OS, women with ER-low disease had a borderline but not significantly better OS than those with ER-zero disease (HR 0.84 (95% CI 0.71-1.00), p = 0.052). ER-status 1-9% vs 0% was not associated with OS in the multivariable analysis (HR 1.11 (0.90-1.36)). Distant disease-free survival did not differ by ER-status 0% vs 1-9% (HR 0.97 for ER-zero vs ER-low (0.62-1.53), p = 0.905). After preoperative treatment, the impact of pCR for OS did not significantly differ between ER-zero or ER-low disease.

Interpretation

ER-low HER2-negative breast cancer has characteristics and prognosis similar to TNBC, when treated in the same way. Therefore, it seems reasonable to use a ≥10% threshold for ER positivity. This would provide patients with ER-low tumors the same treatment opportunities as patients with TNBC, within studies and within clinical routine.

Funding

This work was financially supported by Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA, in accordance with terms and conditions of a Master Collaboration Agreement between the company and Karolinska Institutet.

Aptamer as a targeted approach towards treatment of breast cancer

Aptamers, a novel type of targeted ligand used in drug delivery, have quickly gained popularity due to their high target specificity and affinity. Different aptamer-mediated drug delivery systems, such as aptamer-drug conjugate (ApDC), aptamer-siRNA, and aptamer-functionalised nanoparticle systems, are currently being developed for the successful treatment of cancer based on the excellent properties of aptamers. These systems can decrease potential toxicity and enhance therapeutic efficacy by targeting the drug moiety. In this review, we provide an overview of recent developments in aptamer-mediated delivery systems for cancer therapy, specifically for breast cancer, and talk about the potential applications and current issues of novel aptamer-based techniques. This study in aptamer technology for breast cancer therapy highlights key aptamers targeting well-established biomarkers such as HER2, oestrogen receptor, and progesterone receptor. Additionally, we explore the potential of aptamers in overcoming various challenges such as drug resistance and improving the delivery of therapeutic agents. This review aims to provide a deeper understanding of the present aptamer-based targeted delivery applications through in-depth analysis to increase efficacy and create new therapeutic approaches that may ultimately lead to better treatment outcomes for cancer patients.

Molecular Classifications in Gastric Cancer: A Call for Interdisciplinary Collaboration

Gastric cancer (GC) is a heterogeneous disease, often diagnosed at advanced stages, with a 5-year survival rate of approximately 20%. Despite notable technological advancements in cancer research over the past decades, their impact on GC management and outcomes has been limited. Numerous molecular alterations have been identified in GC, leading to various molecular classifications, such as those developed by The Cancer Genome Atlas (TCGA) and the Asian Cancer Research Group (ACRG). Other authors have proposed alternative perspectives, including immune, proteomic, or epigenetic-based classifications. However, molecular stratification has not yet transitioned into clinical practice for GC, and little attention has been paid to alternative molecular classifications. In this review, we explore diverse molecular classifications in GC from a practical point of view, emphasizing their relationships with clinicopathological factors, prognosis, and therapeutic approaches. We have focused on classifications beyond those of TCGA and the ACRG, which have been less extensively reviewed previously. Additionally, we discuss the challenges that must be overcome to ensure their impact on patient treatment and prognosis. This review aims to serve as a practical framework to understand the molecular landscape of GC, facilitate the development of consensus molecular categories, and guide the design of innovative molecular studies in the field.

Breaking Barriers: The Promise and Challenges of Immune Checkpoint Inhibitors in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is a highly aggressive malignancy with pronounced immunogenicity, exhibiting rapid proliferation and immune cell infiltration into the tumor microenvironment. TNBC's heterogeneity poses challenges to immunological treatments, inducing resistance mechanisms in the tumor microenvironment. Therapeutic modalities, including immune checkpoint inhibitors (ICIs) targeting PD-1, PD-L1, and CTLA-4, are explored in preclinical and clinical trials. Promising results emerge from combining ICIs with anti-TGF-β and VISTA, hindering TNBC tumor growth. TNBC cells employ complex evasion strategies involving interactions with stromal and immune cells, suppressing immune recognition through various cytokines, chemokines, and metabolites. The recent focus on unraveling humoral and cellular components aims to disrupt cancer crosstalk within the tumor microenvironment. This review identifies TNBC's latest resistance mechanisms, exploring potential targets for clinical trials to overcome immune checkpoint resistance and enhance patient survival rates.

The crucial role of fascin-1 in the pathogenesis, metastasis, and chemotherapeutic resistance of breast cancer

Breast cancer (BC) is the most common type of cancer in women to be diagnosed, and it is also the second leading cause of cancer death in women globally. It is the disease that causes the most life years adjusted for disability lost among women, making it a serious worldwide health issue. Understanding and interpreting carcinogenesis and metastatic pathways is critical for curing malignancy. Fascin-1 was recognized as an actin-bundling protein with parallel, rigid bundles as a result of the cross-linking of F-actin microfilaments. Increasing levels of fascin-1 have been associated with bad prognostic profiles, aggressiveness of clinical courses, and poor survival outcomes in a variety of human malignancies. Cancer cells that overexpress fascin-1 have higher capabilities for proliferation, invasion, migration, and metastasis. Fascin-1 is being considered as a potential target for therapy as well as a potential biomarker for diagnostics in a variety of cancer types. This review aims to provide an overview of the FSCN1 gene and its protein structure, elucidate its physiological and pathological roles, and throw light on its involvement in the initiation, development, and chemotherapeutic resistance of BC.

Predictive, preventive, and personalized medicine in breast cancer: targeting the PI3K pathway

Breast cancer (BC) is a multifaceted disease characterized by distinct molecular subtypes and varying responses to treatment. In BC, the phosphatidylinositol 3-kinase (PI3K) pathway has emerged as a crucial contributor to the development, advancement, and resistance to treatment. This review article explores the implications of the PI3K pathway in predictive, preventive, and personalized medicine for BC. It emphasizes the identification of predictive biomarkers, such as PIK3CA mutations, and the utility of molecular profiling in guiding treatment decisions. The review also discusses the potential of targeting the PI3K pathway for preventive strategies and the customization of therapy based on tumor stage, molecular subtypes, and genetic alterations. Overcoming resistance to PI3K inhibitors and exploring combination therapies are addressed as important considerations. While this field holds promise in improving patient outcomes, further research and clinical trials are needed to validate these approaches and translate them into clinical practice.

Unraveling the Role of Adiponectin Receptors in Obesity-Related Breast Cancer

Obesity has a noteworthy role in breast tumor initiation and progression. Among the mechanisms proposed, the most validated is the development of chronic low-grade inflammation, supported by immune cell infiltration along with dysfunction in adipose tissue biology, characterized by an imbalance in adipocytokines secretion and alteration of their receptors within the tumor microenvironment. Many of these receptors belong to the seven-transmembrane receptor family, which are involved in physiological features, such as immune responses and metabolism, as well as in the development and progression of several malignancies, including breast cancer. These receptors are classified as canonical (G protein-coupled receptors, GPCRs) and atypical receptors, which fail to interact and activate G proteins. Among the atypical receptors, adiponectin receptors (AdipoRs) mediate the effect of adiponectin, the most abundant adipocytes-derived hormone, on breast cancer cell proliferation, whose serum levels are reduced in obesity. The adiponectin/AdipoRs axis is becoming increasingly important regarding its role in breast tumorigenesis and as a therapeutic target for breast cancer treatment. The objectives of this review are as follows: to point out the structural and functional differences between GPCRs and AdipoRs, and to focus on the effect of AdipoRs activation in the development and progression of obesity-dependent breast cancer.

Cross-attention multi-branch CNN using DCE-MRI to classify breast cancer molecular subtypes

Purpose

The aim of this study is to improve the accuracy of classifying luminal or non-luminal subtypes of breast cancer by using computer algorithms based on DCE-MRI, and to validate the diagnostic efficacy of the model by considering the patient's age of menarche and nodule size.

Methods

DCE-MRI images of patients with non-specific invasive breast cancer admitted to the Second Affiliated Hospital of Dalian Medical University were collected. There were 160 cases in total, with 84 cases of luminal type (luminal A and luminal B and 76 cases of non-luminal type (HER 2 overexpressing and triple negative). Patients were grouped according to thresholds of nodule sizes of 20 mm and age at menarche of 14 years. A cross-attention multi-branch net CAMBNET) was proposed based on the dataset to predict the molecular subtypes of breast cancer. Diagnostic performance was assessed by accuracy, sensitivity, specificity, F1 and area under the ROC curve (AUC). And the model is visualized with Grad-CAM.

Results

Several classical deep learning models were included for diagnostic performance comparison. Using 5-fold cross-validation on the test dataset, all the results of CAMBNET are significantly higher than the compared deep learning models. The average prediction recall, accuracy, precision, and AUC for luminal and non-luminal types of the dataset were 89.11%, 88.44%, 88.52%, and 96.10%, respectively. For patients with tumor size <20 mm, the CAMBNET had AUC of 83.45% and ACC of 90.29% for detecting triple-negative breast cancer. When classifying luminal from non-luminal subtypes for patients with age at menarche years, our CAMBNET model achieved an ACC of 92.37%, precision of 92.42%, recall of 93.33%, F1of 92.33%, and AUC of 99.95%.

Conclusions

The CAMBNET can be applied in molecular subtype classification of breasts. For patients with menarche at 14 years old, our model can yield more accurate results when classifying luminal and non-luminal subtypes. For patients with tumor sizes ≤20 mm, our model can yield more accurate result in detecting triple-negative breast cancer to improve patient prognosis and survival.