Understanding breast cancer heterogeneity through non-genetic heterogeneity

Prediction models of breast cancer molecular subtypes based on multimodal ultrasound and clinical features

BACKGROUND AND AIMS

Breast cancer classify into four molecular subtypes: Luminal A, Luminal B, HER2-overexpressing (HER2), and triple-negative (TNBC) based on immunohistochemical assessments. The multimodal ultrasound features correlate with biological biomarkers and molecular subtypes, facilitating personalized, precision-guided treatment strategies for patients. In this study, we aimed to explore the differences of multimodal ultrasound features generated from conventional ultrasound (CUS), shear wave elastography (SWE) and contrast-enhanced ultrasound (CEUS) between molecular subtypes of breast cancer, investigate the value of prediction model of breast cancer molecular subtypes based on multimodal ultrasound and clinical features.

METHODS

Breast cancer patients who visited our hospital from January 2023 to June 2024 and underwent CUS, SWE and CEUS were selected, according to inclusion criteria. Based on the selected effective feature subset, binary prediction models of features of CUS, features of SWE, features of CEUS and full parameters were constructed separately for the four breast cancer subtypes Luminal A, Luminal B, HER2, and TNBC, respectively.

RESULTS

There were ten parameters that showed significant differences between molecular subtypes of breast cancer, including BI-RADS, palpable mass, aspect ratio, maximum diameter, calcification, heterogeneous echogenicity, irregular shape, standard deviation elastic modulus value of lesion, time of appearance, peak intensity. Full parameter models had highest area under the curve (AUC) values in every test set. In aggregate, judging from the values of accuracy, precision, recall, F1 score and AUC, models used features selected from full parameters showed better prediction results than those used features selected from CUS, SWE and CEUS alone (AUC: Luminal A, 0.81; Luminal B, 0.74; HER2, 0.89; TNBC, 0.78).

CONCLUSIONS

In conclusion, multimodal ultrasound features had differences between molecular subtypes of breast cancer and models based on multimodal ultrasound data facilitated the prediction of molecular subtypes.

Non-genetic heterogeneity and immune subtyping in breast cancer: Implications for immunotherapy and targeted therapeutics

Breast cancer (BC) is a complex and multifactorial disease, driven by genetic alterations that promote tumor growth and progression. However, recent research has highlighted the importance of non-genetic factors in shaping cancer evolution and influencing therapeutic outcomes. Non-genetic heterogeneity refers to diverse subpopulations of cancer cells within breast tumors, exhibiting distinct phenotypic and functional properties. These subpopulations can arise through various mechanisms, including clonal evolution, genetic changes, epigenetic changes, and reversible phenotypic transitions. Although genetic and epigenetic changes are important points of the pathology of breast cancer yet, the immune system also plays a crucial role in its progression. In clinical management, histologic and molecular classification of BC are used. Immunological subtyping of BC has gained attention in recent years as compared to traditional techniques. Intratumoral heterogeneity revealed by immunological microenvironment (IME) has opened novel opportunities for immunotherapy research. This systematic review is focused on non-genetic variability to identify and interlink immunological subgroups in breast cancer. This review provides a deep understanding of adaptive methods adopted by tumor cells to withstand changes in the tumor microenvironment and selective pressure imposed by medications. These adaptive methods include alterations in drug targets, immune system evasion, activation of survival pathways, and alterations in metabolism. Understanding non-genetic heterogeneity is essential for the development of targeted therapies.

Factors affecting heterogeneity in breast cancer microenvironment: A narrative mini review

Breast cancer (BC) heterogeneity is a key trait of BC tumors with crucial implications on tumorigenesis, diagnosis, and therapeutic modalities. It is influenced by tumor intrinsic features and by the tumor microenvironment (TME) composition of different intra-tumoral regions, which in turn affect cancer progression within patients. In this mini review, we will highlight the mechanisms that generate cancer heterogeneity in BC and how they affect the responses to cancer therapies.

CRIPTO-based micro-heterogeneity of mouse muscle satellite cells enables adaptive response to regenerative microenvironment

Skeletal muscle repair relies on heterogeneous populations of satellite cells (SCs). The mechanisms that regulate SC homeostasis and state transition during activation are currently unknown. Here, we investigated the emerging role of non-genetic micro-heterogeneity, i.e., intrinsic cell-to-cell variability of a population, in this process. We demonstrate that micro-heterogeneity of the membrane protein CRIPTO in mouse-activated SCs (ASCs) identifies metastable cell states that allow a rapid response of the population to environmental changes. Mechanistically, CRIPTO micro-heterogeneity is generated and maintained through a process of intracellular trafficking coupled with active shedding of CRIPTO from the plasma membrane. Irreversible perturbation of CRIPTO micro-heterogeneity affects the balance of proliferation, self-renewal, and myogenic commitment in ASCs, resulting in increased self-renewal in vivo. Our findings demonstrate that CRIPTO micro-heterogeneity regulates the adaptative response of ASCs to microenvironmental changes, providing insights into the role of intrinsic heterogeneity in preserving stem cell population diversity during tissue repair.

Analysis of Changes in the Expression of Selected Genes from the ABC Family in Patients with Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is characterized by a lack of expression of hormone receptors (estrogen and progesterone), as cancer cells also do not overexpress the HER2 receptor. Due to their molecular profile, treatments for this type of breast cancer are limited. In some cases, the pharmacotherapy of patients with TNBC is hindered by the occurrence of multidrug resistance, which is largely conditioned by proteins encoded by genes from the ABC family. The aim of our study was to determine the expression profile of 14 selected genes from the ABC family using real-time PCR in 68 patients with TNBC by comparing the obtained results with clinical data and additionally using bioinformatics tools (Ualcan and The Breast Cancer Gene Expression Miner v4.8 (bc -GenExMiner v4.8)), as well as by comparing experimental data with data in the Cancer Genome Atlas (TCGA) database. Based on the conducted studies, we found different levels of gene expression depending on the age of patients, tumor sizes, metastases to lymph nodes, cell infiltration into adipose tissue, tumor stages, or lymphovascularinvasion. The results of the presented studies demonstrate the effect of the expression level of the studied genes on the clinical course and prognosis of patients with TNBC, and suggest how profiling the expression level of genes from the ABC family may be a useful tool in determining personalized TNBC treatment.

Evaluation of Adjuvant Treatments for Adenoid Cystic Carcinoma of the Breast: A Population-Based, Propensity Score Matched Cohort Study from the SEER Database

Adenoid cystic carcinoma (ACC) is an extremely rare type of breast cancer. The role of adjuvant treatments for ACC remains controversial. Patients with a histology-confirmed diagnosis of ACC of the breast were identified based on the SEER (Surveillance, Epidemiology and End Results) database. Propensity score matching (PSM) was performed to balance the baseline characteristics. The Kaplan–Meier method and Cox regression models were performed to determine the impact of the adjuvant chemotherapy (CT) and radiotherapy (RT) associated with breast cancer-specific survival (BCSS) and overall survival (OS). A total of 1036 patients with ACC of the breast were included. After a median follow-up of 11.3 years, the 10-year OS and BCSS rate was 76.2% and 92.6%, respectively. After PSM, adjuvant CT converted into neither OS (Log-rank p = 1.000) nor BCSS (Log-rank p = 0.900) benefits in patients with ACC of the breast. Adjuvant RT also did not improve OS (Log-rank p = 0.060) and BCSS (Log-rank p = 0.400). According to the univariate stratified analysis, adjuvant RT was favorable for OS in patients who underwent breast-conserving surgery (HR 0.66, 95% CI 0.45, 0.99, p = 0.042). From the multivariate analysis, histology grade and nodal status were independent prognostic factors for both OS and BCSS. In conclusion, ACC of the breast presented a favorable prognosis. Adjuvant treatment, especially adjuvant CT, might not be essential for patients with this disease.