Morphologic and Genomic Heterogeneity in the Evolution and Progression of Breast Cancer

Spatial molecular profiling of mixed invasive ductal and lobular breast cancers reveals heterogeneity in intrinsic molecular subtypes, oncogenic signatures, and mutations

Mixed invasive ductal and lobular carcinoma (MDLC) is a rare histologic subtype of breast cancer displaying both E-cadherin positive ductal and E-cadherin negative lobular morphologies within the same tumor, posing challenges with regard to anticipated clinical management. It remains unclear whether these distinct morphologies also have distinct biology and risk of recurrence. Our spatially resolved transcriptomic, genomic, and single-cell profiling revealed clinically significant differences between ductal and lobular tumor regions including distinct intrinsic subtype heterogeneity - e.g., MDLC with triple-negative breast cancer (TNBC) or basal ductal and estrogen receptor positive (ER+) luminal lobular regions, distinct enrichment of cell cycle arrest/senescence and oncogenic (ER and MYC) signatures, genetic and epigenetic CDH1 inactivation in lobular but not ductal regions, and single-cell ductal and lobular subpopulations with unique oncogenic signatures further highlighting intraregional heterogeneity. Altogether, we demonstrated that the intratumoral morphological/histological heterogeneity within MDLC is underpinned by intrinsic subtype and oncogenic heterogeneity which may result in prognostic uncertainty and therapeutic dilemma.

Spatial molecular profiling of mixed invasive ductal-lobular breast cancers reveals heterogeneity in intrinsic molecular subtypes, oncogenic signatures, and mutations

Mixed invasive ductal and lobular carcinoma (MDLC) is a rare histologic subtype of breast cancer displaying both E-cadherin positive ductal and E-cadherin negative lobular morphologies within the same tumor, posing challenges with regard to anticipated clinical management. It remains unclear whether these distinct morphologies also have distinct biology and risk of recurrence. Our spatially-resolved transcriptomic, genomic, and single-cell profiling revealed clinically significant differences between ductal and lobular tumor regions including distinct intrinsic subtype heterogeneity (e.g., MDLC with TNBC/basal ductal and ER+/luminal lobular regions), distinct enrichment of senescence/dormancy and oncogenic (ER and MYC) signatures, genetic and epigenetic CDH1 inactivation in lobular, but not ductal regions, and single-cell ductal and lobular sub-populations with unique oncogenic signatures further highlighting intra-regional heterogeneity. Altogether, we demonstrated that the intra-tumoral morphological/histological heterogeneity within MDLC is underpinned by intrinsic subtype and oncogenic heterogeneity which may result in prognostic uncertainty and therapeutic dilemma.

Significance

MDLC displays both ductal and lobular tumor regions. Our multi-omic profiling approach revealed that these morphologically distinct tumor regions harbor distinct intrinsic subtypes and oncogenic features that may cause prognostic uncertainty and therapeutic dilemma. Thus histopathological/molecular profiling of individual tumor regions may guide clinical decision making and benefit patients with MDLC, particularly in the advanced setting where there is increased reliance on next generation sequencing.

An Automated Breast Volume Scanner-Based Intra- and Peritumoral Radiomics Nomogram for the Preoperative Prediction of Expression of Ki-67 in Breast Malignancy

RATIONALE AND OBJECTIVES

This study aimed to create and verify a nomogram for preoperative prediction of Ki-67 expression in breast malignancy to assist in the development of personalized treatment strategies.

MATERIALS AND METHODS

This retrospective study received approval from the institutional review board and included a cohort of 197 patients with breast malignancy who were admitted to our hospital. Ki-67 expression was divided into two groups based on a 14% threshold: low and high. A radiomics signature was built utilizing 1702 radiomics features based on an intra- and peritumoral (10 mm) regions of interest. Using multivariate logistic regression, radiomics signature, and ultrasound (US) characteristics, the nomogram was developed. To evaluate the model's calibration, clinical application, and predictive ability, decision curve analysis (DCA), the calibration curve, and the receiver operating characteristic curve were used, respectively.

RESULTS

The final nomogram included three independent predictors: tumor size (P = .037), radiomics signature (P < .001), and US-reported lymph node status (P = .018). The nomogram exhibited satisfactory performance in the training cohort, demonstrating a specificity of 0.944, a sensitivity of 0.745, and an area under the curve (AUC) of 0.905. The validation cohort recorded a specificity of 0.909, a sensitivity of 0.727, and an AUC of 0.882. The DCA showed the nomogram's clinical utility, and the calibration curve revealed a high consistency among the expected and detected values.

CONCLUSION

The nomogram used in this investigation can accurately predict Ki-67 expression in people with malignant breast tumors, helping to develop personalized treatment approaches.

Development of an interpretable machine learning model for Ki-67 prediction in breast cancer using intratumoral and peritumoral ultrasound radiomics features

Background

Traditional immunohistochemistry assessment of Ki-67 in breast cancer (BC) via core needle biopsy is invasive, inaccurate, and nonrepeatable. While machine learning (ML) provides a promising alternative, its effectiveness depends on extensive data. Although the current mainstream MRI-centered radiomics offers sufficient data, its unsuitability for repeated examinations, along with limited accessibility and an intratumoral focus, constrain the application of predictive models in evaluating Ki-67 levels.

Objective

This study aims to explore ultrasound (US) image-based radiomics, incorporating both intra- and peritumoral features, to develop an interpretable ML model for predicting Ki-67 expression in BC patients.

Methods

A retrospective analysis was conducted on 263 BC patients, divided into training and external validation cohorts. From intratumoral and peritumoral regions of interest (ROIs) in US images, 849 distinctive radiomics features per ROI were derived. These features underwent systematic selection to analyze Ki-67 expression relationships. Four ML models-logistic regression, random forests, support vector machine (SVM), and extreme gradient boosting-were formulated and internally validated to identify the optimal predictive model. External validation was executed to ascertain the robustness of the optimal model, followed by employing Shapley Additive Explanations (SHAP) to reveal the significant features of the model.

Results

Among 231 selected BC patients, 67.5% exhibited high Ki-67 expression, with consistency observed across both training and validation cohorts as well as other clinical characteristics. Of the 1698 radiomics features identified, 15 were significantly correlated with Ki-67 expression. The SVM model, utilizing combined ROI, demonstrated the highest accuracy [area under the receiver operating characteristic curve (AUROC): 0.88], making it the most suitable for predicting Ki-67 expression. External validation sustained an AUROC of 0.82, affirming the model's robustness above a 40% threshold. SHAP analysis identified five influential features from intra- and peritumoral ROIs, offering insight into individual prediction.

Conclusion

This study emphasized the potential of SVM model using radiomics features from both intra- and peritumoral US images, for predicting elevated Ki-67 levels in BC patients. The model exhibited strong performance in validations, indicating its promise as a noninvasive tool to enable personalized decision-making in BC care.

Towards dual function of autophagy in breast cancer: A potent regulator of tumor progression and therapy response

As a devastating disease, breast cancer has been responsible for decrease in life expectancy of females and its morbidity and mortality are high. Breast cancer is the most common tumor in females and its treatment has been based on employment of surgical resection, chemotherapy and radiotherapy. The changes in biological behavior of breast tumor relies on genomic and epigenetic mutations and depletions as well as dysregulation of molecular mechanisms that autophagy is among them. Autophagy function can be oncogenic in increasing tumorigenesis, and when it has pro-death function, it causes reduction in viability of tumor cells. The carcinogenic function of autophagy in breast tumor is an impediment towards effective therapy of patients, as it can cause drug resistance and radio-resistance. The important hallmarks of breast tumor such as glucose metabolism, proliferation, apoptosis and metastasis can be regulated by autophagy. Oncogenic autophagy can inhibit apoptosis, while it promotes stemness of breast tumor. Moreover, autophagy demonstrates interaction with tumor microenvironment components such as macrophages and its level can be regulated by anti-tumor compounds in breast tumor therapy. The reasons of considering autophagy in breast cancer therapy is its pleiotropic function, dual role (pro-survival and pro-death) and crosstalk with important molecular mechanisms such as apoptosis. Moreover, current review provides a pre-clinical and clinical evaluation of autophagy in breast tumor.

Prediction of Ki-67 of Invasive Ductal Breast Cancer Based on Ultrasound Radiomics Nomogram

PURPOSE

The objective of this research was to develop and validate an ultrasound-based radiomics nomogram for the pre-operative assessment of Ki-67 in breast cancer (BC).

MATERIALS AND METHODS

From December 2016 to December 2018, 515 patients with invasive ductal breast cancer who received two-dimensional (2D) ultrasound and Ki-67 examination were studied and analyzed retrospectively. The dataset was distributed at random into a training cohort (n = 360) and a test cohort (n = 155) in the ratio of 7:3. Each tumor region of interest was defined based on 2D ultrasound images and radiomics features were extracted. ANOVA, maximum correlation minimum redundancy (mRMR) algorithm, and minimum absolute shrinkage and selection operator (LASSO) were performed to pick features, and independent clinical predictors were integrated with radscore to construct the nomogram for predicting Ki-67 index by univariate and multivariate logistic regression analysis. The performance and utility of the models were evaluated by plotting receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves.

RESULTS

In the testing cohort, the area under the receiver characteristic curve (AUC) of the nomogram was 0.770 (95% confidence interval, 0.690-0.860). In both cohorts, the nomogram outperformed both the clinical model and the radiomics model (P < .05 according to the DeLong test). The analysis of DCA proved that the model has clinical utility.

CONCLUSIONS

The nomogram based on 2D ultrasound images offered an approach for predicting Ki-67 in BC.

Radioproteomics in Breast Cancer: Prediction of Ki-67 Expression With MRI-based Radiomic Models

RATIONALE AND OBJECTIVES

We aimed to investigate the value of magnetic resonance image (MRI)-based radiomics in predicting Ki-67 expression of breast cancer.

METHODS

In this retrospective study, 159 lesions from 154 patients were included. Radiomic features were extracted from contrast-enhanced T1-weighted MRI (C+MRI) and apparent diffusion coefficient (ADC) maps, with open-source software. Dimension reduction was done with reliability analysis, collinearity analysis, and feature selection. Two different Ki-67 expression cut-off values (14% vs 20%) were studied as reference standard for the classifications. Input for the models were radiomic features from individual MRI sequences or their combination. Classifications were performed using a generalized linear model.

RESULTS

Considering Ki-67 cut-off value of 14%, training and testing AUC values were 0.785 (standard deviation [SD], 0.193) and 0.849 for ADC; 0.696 (SD, 0.150) and 0.695 for C+MRI; 0.755 (SD, 0.171) and 0.635 for the combination of both sequences, respectively. Regarding Ki-67 cut-off value of 20%, training and testing AUC values were 0.744 (SD, 0.197) and 0.617 for ADC; 0.629 (SD, 0.251) and 0.741 for C+MRI; 0.761 (SD, 0.207) and 0.618 for the combination of both sequences, respectively.

CONCLUSION

ADC map-based selected radiomic features coupled with generalized linear modeling might be a promising non-invasive method to determine the Ki-67 expression level of breast cancer.

Circulating Tumor Cells as a Tool to Untangle the Breast Cancer Heterogeneity Issue

Breast cancer (BC) is a disease characterized by high degrees of heterogeneity at morphologic, genomic, and genetic levels, even within the same tumor mass or among patients. As a consequence, different subpopulations coexist and less represented clones may have a selective advantage, significantly influencing the outcome of BC patients. Circulating tumor cells (CTCs) represent a rare population of cells with a crucial role in metastatic cascade, and in recent years have represented a fascinating alternative to overcome the heterogeneity issue as a “liquid biopsy”. However, besides the raw enumeration of these cells in advanced epithelial tumors, there are no CTC-based assays applied in the clinical practice to improve personalized medicine. In this review, we report the latest findings in the field of CTCs for intra-tumoral heterogeneity unmasking in BC, supporting the need to deepen their analysis to investigate their role in metastatic process and include the molecular characterization in the clinical practice. In the future, CTCs will be helpful in monitoring patients during treatment, as well as to better address therapeutic strategies.

Characterization of Suspicious Microcalcifications on Mammography Using 2D Shear-Wave Elastography

Our aim was to investigate the correlations between the findings of two-dimensional shear-wave elastography (2D-SWE) and the histopathologic results of microcalcifications (MCs) visualized using ultrasonography (USG). Fifty people with suspicious MCs without accompanying mass were evaluated. They underwent USG and 2D-SWE before USG-guided tru-cut biopsy. SWE values and histopathologic features were compared statistically. The variables between groups were analyzed using the Mann-Whitney U test. Receiver operating characteristic analysis was performed and cut-off values determined to discriminate malignancy, invasiveness and high grade. Pathology confirmed 27 malignant lesions (18 invasive ductal carcinomas, one invasive lobular and eight ductal carcinomas in situ) and 23 benign ones. There was a statistically significant difference between the SWE values of malignant and benign MCs (p < 0.001). The diagnostic performance of SWE for malignancy, invasiveness and high grade were as follows, repectively: sensitivity (93%, 83%, 88%), specificity (91%, 88%, 53%), positive predictive value (93%, 94%, 44%), negative predictive value (91%, 70%, 90%) and area under the curve (0.952, 0.885, 0.776). Cut-off values were determined as 57 kPa for malignancy, 124 kPa for invasiveness and 124.5 kPa for high grade. In conclusion, SWE is a useful method in clinical practice for characterizing MCs that can be visualized with USG.

Estrogen receptor-α signaling in post-natal mammary development and breast cancers

17β-estradiol controls post-natal mammary gland development and exerts its effects through Estrogen Receptor ERα, a member of the nuclear receptor family. ERα is also critical for breast cancer progression and remains a central therapeutic target for hormone-dependent breast cancers. In this review, we summarize the current understanding of the complex ERα signaling pathways that involve either classical nuclear “genomic” or membrane “non-genomic” actions and regulate in concert with other hormones the different stages of mammary development. We describe the cellular and molecular features of the luminal cell lineage expressing ERα and provide an overview of the transgenic mouse models impacting ERα signaling, highlighting the pivotal role of ERα in mammary gland morphogenesis and function and its implication in the tumorigenic processes. Finally, we describe the main features of the ERα-positive luminal breast cancers and their modeling in mice.

The Ratio of RAC1B to RAC1 Expression in Breast Cancer Cell Lines as a Determinant of Epithelial/Mesenchymal Differentiation and Migratory Potential

Breast cancer (BC) is a heterogenous disease encompassing tumors with different histomorphological phenotypes and transcriptionally defined subtypes. However, the non-mutational/epigenetic alterations that are associated with or causally involved in phenotype diversity or conversion remain to be elucidated. Data from the pancreatic cancer model have shown that the small GTPase RAC1 and its alternatively spliced isoform, RAC1B, antagonistically control epithelial–mesenchymal transition and cell motility induced by transforming growth factor β. Using a battery of established BC cell lines with either a well-differentiated epithelial or poorly differentiated mesenchymal phenotype, we observed subtype-specific protein expression of RAC1B and RAC1. While epithelial BC lines were RAC1B^high and RAC1^low, mesenchymal lines exhibited the reverse expression pattern. High RAC1B and/or low RAC1 abundance also correlated closely with a poor invasion potential, and vice versa, as revealed by measuring random cell migration (chemokinesis), the preferred mode of cellular movement in cells that have undergone mesenchymal transdifferentiation. We propose that a high RAC1B:RAC1 ratio in BC cells is predictive of an epithelial phenotype, while low RAC1B along with high RAC1 is a distinguishing feature of the mesenchymal state. The combined quantitative assessment of RAC1B and RAC1 in tumor biopsies of BC patients may represent a novel diagnostic tool for probing molecular subtype and eventually predict malignant potential of breast tumors.

Multi-parametric characterization of drug effects on cells

We present here a novel multi-parametric approach for the characterization of multiple cellular features, using images acquired by high-throughput and high-definition light microscopy. We specifically used this approach for deep and unbiased analysis of the effects of a drug library on five cultured cell lines. The presented method enables the acquisition and analysis of millions of images, of treated and control cells, followed by an automated identification of drugs inducing strong responses, evaluating the median effect concentrations and those cellular properties that are most highly affected by the drug. The tools described here provide standardized quantification of multiple attributes for systems level dissection of complex functions in normal and diseased cells, using multiple perturbations. Such analysis of cells, derived from pathological samples, may help in the diagnosis and follow-up of treatment in patients.

The FGF/FGFR System in Breast Cancer: Oncogenic Features and Therapeutic Perspectives

One of the major challenges in the treatment of breast cancer is the heterogeneous nature of the disease. With multiple subtypes of breast cancer identified, there is an unmet clinical need for the development of therapies particularly for the less tractable subtypes. Several transduction mechanisms are involved in the progression of breast cancer, therefore making the assessment of the molecular landscape that characterizes each patient intricate. Over the last decade, numerous studies have focused on the development of tyrosine kinase inhibitors (TKIs) to target the main pathways dysregulated in breast cancer, however their effectiveness is often limited either by resistance to treatments or the appearance of adverse effects. In this context, the fibroblast growth factor/fibroblast growth factor receptor (FGF/FGFR) system represents an emerging transduction pathway and therapeutic target to be fully investigated among the diverse anti-cancer settings in breast cancer. Here, we have recapitulated previous studies dealing with FGFR molecular aberrations, such as the gene amplification, point mutations, and chromosomal translocations that occur in breast cancer. Furthermore, alterations in the FGF/FGFR signaling across the different subtypes of breast cancer have been described. Next, we discussed the functional interplay between the FGF/FGFR axis and important components of the breast tumor microenvironment. Lastly, we pointed out the therapeutic usefulness of FGF/FGFR inhibitors, as revealed by preclinical and clinical models of breast cancer.