Breast cancer intra-tumor heterogeneity. Breast Cancer Res. 2014;16:210. [PMID: 25928070 PMCID: PMC4053234 DOI: 10.1186/bcr3658]

Human-derived Tumor-On-Chip model to study the heterogeneity of breast cancer tissue

One of the leading causes that complicate the treatment of some malignancies, including breast cancer, is tumor heterogeneity. In addition to inter-heterogeneity and intra-heterogeneity of tumors that reflect the differences between cancer cell characteristics, heterogeneity in the tumor microenvironment plays a critical role in tumor progression and could be considered an overlooked and a proper target for the effective selection of therapeutic approaches. Due to the difficulty of completely capturing tumor heterogeneity in conventional detection methods, Tumor-on-Chip (TOC) devices with culturing patient-derived spheroids could be an appropriate alternative. In this research, human-derived spheroids from breast cancer individuals were cultured for 6 days in microfluidic devices. To compare TOC data with conventional detection methods, immunohistochemistry (IHC) and ITRAQ data were employed, and various protein expressions were validated using the transcriptomic databases. The behavior of the spheroids in the collagen matrix and the cell viability were monitored over 6 days of culture. IHC and immunocytochemistry (ICC) results revealed that inter and intra-heterogeneity of tumor spheroids are associated with HER2/ER expression. HER2 expression levels revealed a more important biomarker associated with invasion in the 3D culturing of spheroids. The expression levels of CD163 (as a marker for Ma2 macrophages) and CD44 (a marker for cancer stem cells (CSCs)) were also evaluated. Interestingly, the levels of M2a macrophages and CSCs were higher in triple-negative specimens and samples that showed higher migration and invasion. Cell density and extracellular matrix (ECM) stiffness were also important factors affecting the migration and invasion of the spheroids through the matrix. Among these, rigid ECM revealed a more crucial role than cell density. To sum up, these research findings demonstrated that human-derived spheroids from breast cancer specimens in microfluidic devices provide a dynamic condition for predicting tumor heterogeneity in patients, which can help move the field forward for better and more accurate therapeutic strategies.

Changes in expression of breast cancer tumor biomarkers between primary tumors and corresponding metastatic sites: common patterns and relationships with survival

PURPOSE

In metastatic breast cancer, differences in expression patterns of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2) between the primary tumor (PT) and metastatic site (MET) have been reported. However, there is limited understanding of the relationship of tumor subtype discordance and overall survival (OS). We evaluated patterns of ER/PR/HER2 in PTs and corresponding METs and assessed the relationship between these patterns and OS.

METHODS

Patients diagnosed at our center with metastatic breast cancer (2011-2020) were included. ER/PR were stratified as < 1%/1-10%/ > 10% by immunohistochemistry and HER2 as positive/negative by immunohistochemistry/FISH. Tumor subtypes were classified as ER or PR + /HER2-, HER2+ , or triple-negative. Biomarker discordance data from PTs to METs were analyzed for expression patterns. OS was assessed.

RESULTS

Of 254 patients, 41 (16.1%) had synchronous and 213 (83.9%) had metachronous METs. Category change of ER/PR/HER2 expression was observed in 56 (22.0%), 117 (40.5%), and 30 (11.8%) patients, respectively. Tumor subtype changed in 56 (22.0%) patients. We identified a difference between PT and MET from ER > 10% to ER < 1% (n = 28,16.2% p < 0.01); PR > 10% to PR < 1% (n = 54,48.2%, p < 0.001); PR > 10% to PR 1-10% (n = 18,16.1%, p < 0.001), and ER or PR+/HER2- to triple-negative (n = 19,13.0%, p = 0.03). In log-rank analysis, change from an ER or PR+/HER2- (5-year OS 88.6%) PT to a HER2+(67.5%) or triple-negative (54.6%) MET was associated with decreased survival (p < 0.01); however, in multivariate analysis, discordant biomarker expression was not associated with decreased survival (p > 0.05).

CONCLUSION

Tumor expression of ER/PR/HER2 can differ between the PT and MET. Loss of ER/PR expression is common and may be related to worse survival. Routine assessment of MET tumor markers could inform prognosis and therapeutic decision-making.

Enhancing Ki-67 Prediction in Breast Cancer: Integrating Intratumoral and Peritumoral Radiomics From Automated Breast Ultrasound via Machine Learning

RATIONALE AND OBJECTIVES

Traditional Ki-67 evaluation in breast cancer (BC) via core needle biopsy is limited by repeatability and heterogeneity. The automated breast ultrasound system (ABUS) offers reproducibility but is constrained to morphological and echoic assessments. Radiomics and machine learning (ML) offer solutions, but their integration for improving Ki-67 predictive accuracy in BC remains unexplored. This study aims to enhance ABUS by integrating ML-assisted radiomics for Ki-67 prediction in BC, with a focus on both intratumoral and peritumoral regions.

MATERIALS AND METHODS

A retrospective analysis was conducted on 936 BC patients, split into training (n = 655) and testing (n = 281) cohorts. Radiomics features were extracted from intra- and peritumoral regions via ABUS. Feature selection involved Z-score normalization, intraclass correlation, Wilcoxon rank sum tests, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator logistic regression. ML classifiers were trained and optimized for enhanced predictive accuracy. The interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP).

RESULTS

Of the 2632 radiomics features in each patient, 15 were significantly associated with Ki-67 levels. The support vector machine (SVM) was identified as the optimal classifier, with area under the receiver operating characteristic curve values of 0.868 (training) and 0.822 (testing). SHAP analysis indicated that five peritumoral and two intratumoral features, along with age and lymph node status, were key determinants in the predictive model.

CONCLUSION

Integrating ML with ABUS-based radiomics effectively enhances Ki-67 prediction in BC, demonstrating the SVM model's strong performance with both radiomics and clinical factors.

Comprehensive multi-omics analysis of breast cancer reveals distinct long-term prognostic subtypes

Breast cancer (BC) is a leading cause of cancer-related death worldwide. The diverse nature and heterogeneous biology of BC pose challenges for survival prediction, as patients with similar diagnoses often respond differently to treatment. Clinically relevant BC intrinsic subtypes have been established through gene expression profiling and are implemented in the clinic. While these intrinsic subtypes show a significant association with clinical outcomes, their long-term survival prediction beyond 5 years often deviates from expected clinical outcomes. This study aimed to identify naturally occurring long-term prognostic subgroups of BC based on an integrated multi-omics analysis. This study incorporates a clinical cohort of 335 untreated BC patients from the Oslo2 study with long-term follow-up (>12 years). Multi-Omics Factor Analysis (MOFA+) was employed to integrate transcriptomic, proteomic, and metabolomic data obtained from the tumor tissues. Our analysis revealed three prominent multi-omics clusters of BC patients with significantly different long-term prognoses (p = 0.005). The multi-omics clusters were validated in two independent large cohorts, METABRIC and TCGA. Importantly, a lack of prognostic association to long-term follow-up above 12 years in the previously established intrinsic subtypes was shown for these cohorts. Through a systems-biology approach, we identified varying enrichment levels of cell-cycle and immune-related pathways among the prognostic clusters. Integrated multi-omics analysis of BC revealed three distinct clusters with unique clinical and biological characteristics. Notably, these multi-omics clusters displayed robust associations with long-term survival, outperforming the established intrinsic subtypes.

Integration of multiomics analyses reveals unique insights into CD24-mediated immunosuppressive tumor microenvironment of breast cancer

BACKGROUND

Tumor immunotherapy brings new light and vitality to breast cancer patients, but low response rate and limitations of therapeutic targets become major obstacles to its clinical application. Recent studies have shown that CD24 is involved in an important process of tumor immune regulation in breast cancer and is a promising target for immunotherapy.

METHODS

In this study, singleR was used to annotate each cell subpopulation after t-distributed stochastic neighbor embedding (t-SNE) methods. Pseudo-time trace analysis and cell communication were analyzed by Monocle2 package and CellChat, respectively. A prognostic model based on CD24-related genes was constructed using several machine learning methods. Multiple quantitative immunofluorescence (MQIF) was used to evaluate the spatial relationship between CD24+PANCK+cells and exhausted CD8+T cells.

RESULTS

Based on the scRNA-seq analysis, 1488 CD24-related differential genes were identified, and a risk model consisting of 15 prognostic characteristic genes was constructed by combining the bulk RNA-seq data. Patients were divided into high- and low-risk groups based on the median risk score. Immune landscape analysis showed that the low-risk group showed higher infiltration of immune-promoting cells and stronger immune reactivity. The results of cell communication demonstrated a strong interaction between CD24+epithelial cells and CD8+T cells. Subsequent MQIF demonstrated a strong interaction between CD24+PANCK+ and exhausted CD8+T cells with FOXP3+ in breast cancer. Additionally, CD24+PANCK+ and CD8+FOXP3+T cells were positively associated with lower survival rates.

CONCLUSION

This study highlights the importance of CD24+breast cancer cells in clinical prognosis and immunosuppressive microenvironment, which may provide a new direction for improving patient outcomes.

Diagnostic performance of ultrasound-based artificial intelligence for predicting key molecular markers in breast cancer: A systematic review and meta-analysis

BACKGROUND

Breast cancer (BC) diagnosis and treatment rely heavily on molecular markers such as HER2, Ki67, PR, and ER. Currently, these markers are identified by invasive methods.

OBJECTIVE

This meta-analysis investigates the diagnostic accuracy of ultrasound-based radiomics as a novel approach to predicting these markers.

METHODS

A comprehensive search of PubMed, EMBASE, and Web of Science databases was conducted to identify studies evaluating ultrasound-based radiomics in BC. Inclusion criteria encompassed research on HER2, Ki67, PR, and ER as key molecular markers. Quality assessment using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS) was performed. The data extraction step was performed systematically.

RESULTS

Our meta-analysis quantifies the diagnostic accuracy of ultrasound-based radiomics with a sensitivity and specificity of 0.76 and 0.78 for predicting HER2, 0.80, and 0.76 for Ki67 biomarkers. Studies did not provide sufficient data for quantitative PR and ER prediction analysis. The overall quality of studies based on the RQS tool was moderate. The QUADAS-2 evaluation showed that the studies had an unclear risk of bias regarding the flow and timing domain.

CONCLUSION

Our analysis indicated that AI models have a promising accuracy for predicting key molecular biomarkers' status in BC patients. We performed the quantitative analysis for HER2 and Ki67 biomarkers which yielded a moderate to high accuracy. However, studies did not provide adequate data for meta-analysis of ER and PR prediction accuracy of developed models. The overall quality of the studies was acceptable. In future research, studies need to report the results thoroughly. Also, we suggest more prospective studies from different centers.

Evolvability of cancer-associated genes under APOBEC3A/B selection

Evolvability is an emergent hallmark of cancer that depends on intra-tumor heterogeneity and genetic variation. Mutations generated by APOBEC3 contribute to genetic variation and tumor evolvability. However, the influence of APOBEC3 on the evolvability of the genome and its differential impact on cancer genes versus non-cancer genes remains unclear. Analyzing over 40,000 human protein-coding transcripts, we identified distinct distribution patterns of APOBEC3A/B TC motifs between cancer and non-cancer genes, suggesting unique associations with cancer. Studying a bat species with numerous APOBEC3 genes, we found distinct motif patterns in orthologs of cancer genes compared to non-cancer genes, as in humans, suggesting APOBEC3 evolution to reduce impacts on the genome rather than the converse. Simulations confirmed that APOBEC3-induced heterogeneity enhances cancer evolution through bimodal patterns of mutations in certain classes of genes. Our results suggest the bimodal distribution of APOBEC-induced mutations can significantly increase cancer heterogeneity.

Computational Analysis of Deleterious nsSNPs in INS Gene Associated with Permanent Neonatal Diabetes Mellitus

Insulin gene mutations affect the structure of insulin and are considered a leading cause of neonatal diabetes and permanent neonatal diabetes mellitus PNDM. These mutations can affect the production and secretion of insulin, resulting in inadequate insulin levels and subsequent hyperglycemia. Early discovery or prediction of PNDM can aid in better management and treatment. The current study identified potential deleterious non-synonymous single nucleotide polymorphisms nsSNPs in the INS gene. The analysis of the nsSNPs in the INS gene was conducted using bioinformatics tools by implementing computational algorithms including SIFT, PolyPhen2, SNAP2, SNPs & GO, PhD-SNP, MutPred2, I-Mutant, MuPro, and HOPE tools to investigate the prediction of the potential association between nsSNPs in the INS gene and PNDM. Three mutations, C96Y, P52R, and C96R, were shown to potentially reduce the stability and function of the INS protein. These mutants were subjected to MDSs for structural analysis. Results suggested that these three potential pathogenic mutations may affect the stability and functionality of the insulin protein encoded by the INS gene. Therefore, these changes may influence the development of PNDM. Further researches are required to fully understand the various effects of mutations in the INS gene on insulin synthesis and function. These data can aid in genetic testing for PNDM to evaluate its risk and create treatment and prevention strategies in personalized medicine.

A Channel-Dimensional Feature-Reconstructed Deep Learning Model for Predicting Breast Cancer Molecular Subtypes on Overall b-Value Diffusion-Weighted MRI

BACKGROUND

Dynamic contrast-enhanced (DCE) MRI commonly outperforms diffusion-weighted (DW) MRI in breast cancer discrimination. However, the side effects of contrast agents limit the use of DCE-MRI, particularly in patients with chronic kidney disease.

PURPOSE

To develop a novel deep learning model to fully exploit the potential of overall b-value DW-MRI without the need for a contrast agent in predicting breast cancer molecular subtypes and to evaluate its performance in comparison with DCE-MRI.

STUDY TYPE

Prospective.

SUBJECTS

486 female breast cancer patients (training/validation/test: 64%/16%/20%).

FIELD STRENGTH/SEQUENCE

3.0 T/DW-MRI (13 b-values) and DCE-MRI (one precontrast and five postcontrast phases).

ASSESSMENT

The breast cancers were divided into four subtypes: luminal A, luminal B, HER2+, and triple negative. A channel-dimensional feature-reconstructed (CDFR) deep neural network (DNN) was proposed to predict these subtypes using pathological diagnosis as the reference standard. Additionally, a non-CDFR DNN (NCDFR-DNN) was built for comparative purposes. A mixture ensemble DNN (ME-DNN) integrating two CDFR-DNNs was constructed to identify subtypes on multiparametric MRI (MP-MRI) combing DW-MRI and DCE-MRI.

STATISTICAL TESTS

Model performance was evaluated using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Model comparisons were performed using the one-way analysis of variance with least significant difference post hoc test and the DeLong test. P < 0.05 was considered significant.

RESULTS

The CDFR-DNN (accuracies, 0.79 ~ 0.80; AUCs, 0.93 ~ 0.94) demonstrated significantly improved predictive performance than the NCDFR-DNN (accuracies, 0.76 ~ 0.78; AUCs, 0.92 ~ 0.93) on DW-MRI. Utilizing the CDFR-DNN, DW-MRI attained the predictive performance equal (P = 0.065 ~ 1.000) to DCE-MRI (accuracies, 0.79 ~ 0.80; AUCs, 0.93 ~ 0.95). The predictive performance of the ME-DNN on MP-MRI (accuracies, 0.85 ~ 0.87; AUCs, 0.96 ~ 0.97) was superior to those of both the CDFR-DNN and NCDFR-DNN on either DW-MRI or DCE-MRI.

DATA CONCLUSION

The CDFR-DNN enabled overall b-value DW-MRI to achieve the predictive performance comparable to DCE-MRI. MP-MRI outperformed DW-MRI and DCE-MRI in subtype prediction.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY STAGE: 1.

Segregation of co-cultured multicellular systems: review and modeling consideration

Cell segregation caused by collective cell migration (CCM) is crucial for morphogenesis, functional development of tissue parts, and is an important aspect in other diseases such as cancer and its metastasis process. Efficiency of the cell segregation depends on the interplay between: (1) biochemical processes such as cell signaling and gene expression and (2) physical interactions between cells. Despite extensive research devoted to study the segregation of various co-cultured systems, we still do not understand the role of physical interactions in cell segregation. Cumulative effects of these physical interactions appear in the form of physical parameters such as: (1) tissue surface tension, (2) viscoelasticity caused by CCM, and (3) solid stress accumulated in multicellular systems. These parameters primarily depend on the interplay between the state of cell-cell adhesion contacts and cell contractility. The role of these physical parameters on the segregation efficiency is discussed on model systems such as co-cultured breast cell spheroids consisting of two subpopulations that are in contact. This review study aims to: (1) summarize biological aspects related to cell segregation, mechanical properties of cell collectives, effects along the biointerface between cell subpopulations and (2) describe from a biophysical/mathematical perspective the same biological aspects summarized before. So that overall it can illustrate the complexity of the biological systems that translate into very complex biophysical/mathematical equations. Moreover, by presenting in parallel these two seemingly different parts (biology vs. equations), this review aims to emphasize the need for experiments to estimate the variety of parameters entering the resulting complex biophysical/mathematical models.

An inflamed tumor cell subpopulation promotes chemotherapy resistance in triple negative breast cancer

Individual cancers are composed of heterogeneous tumor cells with distinct phenotypes and genotypes, with triple negative breast cancers (TNBC) demonstrating the most heterogeneity among breast cancer types. Variability in transcriptional phenotypes could meaningfully limit the efficacy of monotherapies and fuel drug resistance, although to an unknown extent. To determine if transcriptional differences between tumor cells lead to differential drug responses we performed single cell RNA-seq on cell line and PDX models of breast cancer revealing cell subpopulations in states associated with resistance to standard-of-care therapies. We found that TNBC models contained a subpopulation in an inflamed cellular state, often also present in human breast cancer samples. Inflamed cells display evidence of heightened cGAS/STING signaling which we demonstrate is sufficient to cause tumor cell resistance to chemotherapy. Accordingly, inflamed cells were enriched in human tumors taken after neoadjuvant chemotherapy and associated with early recurrence, highlighting the potential for diverse tumor cell states to promote drug resistance.

Development of a novel prognostic signature based on single-cell combined bulk RNA analysis in breast cancer

BACKGROUND

Breast cancer (BC), a malignant tumor, is a significant cause of death and disability among women globally. Recent research indicates that copy number variation plays a crucial role in tumor development. In this study, we employed the Single-Cell Variational Aneuploidy Analysis (SCEVAN) algorithm to differentiate between malignant and non-malignant cells, aiming to identify genetic signatures with prognostic relevance for predicting patient survival.

METHODS

We analyzed gene expression profiles and associated clinical data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Using the SCEVAN algorithm, we distinguished malignant from non-malignant cells and investigated cellular interactions within the tumor microenvironment (TME). We categorized TCGA samples based on differentially expressed genes (DEGs) between these cell types. Subsequent Kyoto Encyclopedia of Genes and Genomes pathway analysis was conducted. Additionally, we developed polygenic models for the DEGs using least absolute shrinkage and selection operator-penalized Cox regression analysis. To assess the prognostic accuracy of these characteristics, we generated Kaplan-Meier and receiver operating characteristic curves from training and validation datasets. We also monitored the expression variations of prognostic genes across the pseudotime of malignant cells. Patients were divided into high-risk and low-risk groups based on median risk scores to compare their TME and identify potential therapeutic agents. Lastly, polymerase chain reaction was used to validate seven pivotal genes.

RESULTS

The SCEVAN algorithm identified distinct malignant and non-malignant cells in GSE180286. Cellchat analysis revealed significantly increased cellular communication, particularly between fibroblasts, endothelial cells and malignant cells. The DEGs were predominantly involved in immune-related pathways. TCGA samples were classified into clusters A and B based on these genes. Cluster A, enriched in immune pathways, was associated with poorer prognosis, whereas cluster B, predominantly involved in circadian rhythm pathways, showed better outcomes. We constructed a 14-gene prognostic signature, validated in a 1:1 internal TCGA cohort and external GEO datasets (GSE42568 and GSE146558). Kaplan-Meier analysis confirmed the prognostic signature's accuracy (p < 0.001). Receiver operating characteristic curve analysis demonstrated the predictive reliability of these prognostic features. Single-cell pseudotime analysis with monocle2 highlighted the distinct expression trends of these genes in malignant cells, underscoring the intratumoral heterogeneity. Furthermore, we explored the differences in TME between high- and low-risk groups and identified 16 significantly correlated drugs.

CONCLUSION

Our findings suggest that the 14-gene prognostic signature could serve as a novel biomarker for forecasting the prognosis of BC patients. Additionally, the immune cells and pathways in different risk groups indicate that immunotherapy may be a crucial component of treatment strategies for BC patients.

Application of Single-Cell Sequencing Technology in Research on Colorectal Cancer

Colorectal cancer (CRC) is the third most prevalent and second most lethal cancer globally, with gene mutations and tumor metastasis contributing to its poor prognosis. Single-cell sequencing technology enables high-throughput analysis of the genome, transcriptome, and epigenetic landscapes at the single-cell level. It offers significant insights into analyzing the tumor immune microenvironment, detecting tumor heterogeneity, exploring metastasis mechanisms, and monitoring circulating tumor cells (CTCs). This article provides a brief overview of the technical procedure and data processing involved in single-cell sequencing. It also reviews the current applications of single-cell sequencing in CRC research, aiming to enhance the understanding of intratumoral heterogeneity, CRC development, CTCs, and novel drug targets. By exploring the diverse molecular and clinicopathological characteristics of tumor heterogeneity using single-cell sequencing, valuable insights can be gained into early diagnosis, therapy, and prognosis of CRC. Thus, this review serves as a valuable resource for identifying prognostic markers, discovering new therapeutic targets, and advancing personalized therapy in CRC.

Discordance of Oncotype DX scores in synchronous bilateral and unilateral multifocal breast cancers

PURPOSE

Oncotype DX, a 21-gene expression profiling test, has become standard of care in the management of estrogen receptor (ER)-positive breast cancer. In multifocal tumors, it is unclear whether testing of the different foci is necessary. We evaluated the concordance of Oncotype DX recurrence scores (RS) between 2 tumor foci in synchronous bilateral or unilateral multifocal tumors and characterized pathological predictors of discordance.

METHODS

We reviewed 713 ER+, HER2- primary invasive breast cancer patients with Oncotype RS and identified 17 bilateral synchronous patients (34 tumors) and 13 unilateral multifocal patients (26 tumors) with available Oncotype RS on all foci. Discordance in Oncotype RS between synchronous tumors was recorded and associations with clinicopathologic features including tumor size, histology, Nottingham histologic grade, progesterone receptor staining, and Ki67 index were analyzed.

RESULTS

Bilateral synchronous tumors were present in older patients (median age 59 years) and had larger tumor (median size 17 mm) and more discordant histology (10/17, 59%) as compared to unilateral multifocal tumors (median age 49 years, p < 0.01; median tumor size 12 mm, p = 0.01; discordant histology 2/13, 15%, p = 0.03). Oncotype RS were discordant in 47% (8/17) of bilateral and 54% (7/13) of unilateral multifocal tumors. Concordant Oncotype RS was associated with similar histologic grade and Ki67 index in 78% (7/9) of bilateral and 100% (6/6) of multifocal tumors. In contrast, only 25% (2/8) of bilateral (p = 0.06) and 14% (1/7) of unilateral multifocal (p < 0.01) cases with discordant Oncotype RS had concordant histology grades and Ki67 levels. In synchronous tumors with discordant Oncotype RS and Ki67 index, all (4/4) foci with higher RS had higher Ki67 index.

CONCLUSION

Discordance of Oncotype RS is common in both bilateral and unilateral multifocal breast cancer and is likely associated with discordant histologic grade or Ki67.

Evolvability of cancer-associated genes under APOBEC3A/B selection

Evolvability is an emergent hallmark of cancer that depends on intra-tumor heterogeneity and, ultimately, genetic variation. Mutations generated by APOBEC3 cytidine deaminases can contribute to genetic variation and the consequences of APOBEC activation differ depending on the stage of cancer, with the most significant impact observed during the early stages. However, how APOBEC activity shapes evolutionary patterns of genes in the host genome and differential impacts on cancer-associated and non-cancer genes remain unclear. Analyzing over 40,000 human protein-coding transcripts, we identified distinct distribution patterns of APOBEC3A/B TC motifs between cancer-related genes and controls, suggesting unique associations with cancer. Studying a bat species with many more APOBEC3 genes, we found diverse motif patterns in orthologs of cancer genes compared to controls, similar to humans and suggesting APOBEC evolution to reduce impacts on the genome rather than the converse. Simulations confirmed that APOBEC-induced heterogeneity enhances cancer evolution, shaping clonal dynamics through bimodal introduction of mutations in certain classes of genes. Our results suggest that a major consequence of the bimodal distribution of APOBEC affects greater cancer heterogeneity.

Targeting ABCA12-controlled ceramide homeostasis inhibits breast cancer stem cell function and chemoresistance

Cancer stem cells (CSCs) drive tumor growth, metastasis, and chemoresistance. While emerging evidence suggests that CSCs have a unique dependency on lipid metabolism, the functions and regulation of distinct lipid species in CSCs remain poorly understood. Here, we developed a stem cell factor SOX9-based reporter for isolating CSCs in primary tumors and metastases of spontaneous mammary tumor models. Transcriptomic analyses uncover that SOX9high CSCs up-regulate the ABCA12 lipid transporter. ABCA12 down-regulation impairs cancer stemness and chemoresistance. Lipidomic analyses reveal that ABCA12 maintains cancer stemness and chemoresistance by reducing intracellular ceramide abundance, identifying a CSC-associated function of ABCA subfamily transporter. Ceramide suppresses cancer stemness by inhibiting the YAP-SOX9 signaling pathway in CSCs. Increasing ceramide levels in tumors enhances their sensitivity to chemotherapy and prevents the enrichment of SOX9high CSCs. In addition, SOX9high and ABCA12high cancer cells contribute to chemoresistance in human patient-derived xenografts. These findings identify a CSC-suppressing lipid metabolism pathway that can be exploited to inhibit CSCs and overcome chemoresistance.

AI-enabled routine H&E image based prognostic marker for early-stage luminal breast cancer

Breast cancer (BC) grade is a well-established subjective prognostic indicator of tumour aggressiveness. Tumour heterogeneity and subjective assessment result in high degree of variability among observers in BC grading. Here we propose an objective Haematoxylin & Eosin (H&E) image-based prognostic marker for early-stage luminal/Her2-negative BReAst CancEr that we term as the BRACE marker. The proposed BRACE marker is derived from AI based assessment of heterogeneity in BC at a detailed level using the power of deep learning. The prognostic ability of the marker is validated in two well-annotated cohorts (Cohort-A/Nottingham: n = 2122 and Cohort-B/Coventry: n = 311) on early-stage luminal/HER2-negative BC patients treated with endocrine therapy and with long-term follow-up. The BRACE marker is able to stratify patients for both distant metastasis free survival (p = 0.001, C-index: 0.73) and BC specific survival (p < 0.0001, C-index: 0.84) showing comparable prediction accuracy to Nottingham Prognostic Index and Magee scores, which are both derived from manual histopathological assessment, to identify luminal BC patients that may be likely to benefit from adjuvant chemotherapy.

Computational single cell oncology: state of the art

Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.

Comparison of Dynamic Contrast-Enhanced MRI and Non-Mono-Exponential Model-Based Diffusion-Weighted Imaging for the Prediction of Prognostic Biomarkers and Molecular Subtypes of Breast Cancer Based on Radiomics

BACKGROUND

Dynamic contrast-enhanced (DCE) MRI and non-mono-exponential model-based diffusion-weighted imaging (NME-DWI) that does not require contrast agent can both characterize breast cancer. However, which technique is superior remains unclear.

PURPOSE

To compare the performances of DCE-MRI, NME-DWI and their combination as multiparametric MRI (MP-MRI) in the prediction of breast cancer prognostic biomarkers and molecular subtypes based on radiomics.

STUDY TYPE

Prospective.

POPULATION

A total of 477 female patients with 483 breast cancers (5-fold cross-validation: training/validation, 80%/20%).

FIELD STRENGTH/SEQUENCE

A 3.0 T/DCE-MRI (6 dynamic frames) and NME-DWI (13 b values).

ASSESSMENT

After data preprocessing, high-throughput features were extracted from each tumor volume of interest, and optimal features were selected using recursive feature elimination method. To identify ER+ vs. ER-, PR+ vs. PR-, HER2+ vs. HER2-, Ki-67+ vs. Ki-67-, luminal A/B vs. nonluminal A/B, and triple negative (TN) vs. non-TN, the following models were implemented: random forest, adaptive boosting, support vector machine, linear discriminant analysis, and logistic regression.

STATISTICAL TESTS

Student's t, chi-square, and Fisher's exact tests were applied on clinical characteristics to confirm whether significant differences exist between different statuses (±) of prognostic biomarkers or molecular subtypes. The model performances were compared between the DCE-MRI, NME-DWI, and MP-MRI datasets using the area under the receiver-operating characteristic curve (AUC) and the DeLong test. P < 0.05 was considered significant.

RESULTS

With few exceptions, no significant differences (P = 0.062-0.984) were observed in the AUCs of models for six classification tasks between the DCE-MRI (AUC = 0.62-0.87) and NME-DWI (AUC = 0.62-0.91) datasets, while the model performances on the two imaging datasets were significantly poorer than on the MP-MRI dataset (AUC = 0.68-0.93). Additionally, the random forest and adaptive boosting models (AUC = 0.62-0.93) outperformed other three models (AUC = 0.62-0.90).

DATA CONCLUSION

NME-DWI was comparable with DCE-MRI in predictive performance and could be used as an alternative technique. Besides, MP-MRI demonstrated significantly higher AUCs than either DCE-MRI or NME-DWI.

EVIDENCE LEVEL

2.

TECHNICAL EFFICACY

Stage 2.

Impact of media compositions and culture systems on the immunophenotypes of patient-derived breast cancer cells

BACKGROUND

Heterogeneous tumor cells are thought to be a significant factor in the failure of endocrine therapy in estrogen receptor-positive (ER+) cancers. Culturing patient-derived breast cancer cells (PDBCCs) provides an invaluable tool in pre-clinical and translational research for the heterogeneity of cancer cells. This study aimed to investigate the effects of different media components and culture methods on the BCSC-associated immunophenotypes and gene expression in ER + PDBCCs.

METHODS

Ten patients with ER + breast cancer were employed in this study, six of whom had neoadjuvant chemotherapy and four of whom did not. PDBCCs were isolated by enzymatic methods using collagen I and hyaluronidase. PDBCCs were grown as monolayers in mediums with different compositions and as multicellular spheroid in a suspended condition. Collagen I-coated plate and ultralow attachment plate coated with polymer-X were used for monolayer and spheroid culture. Flow cytometry, immunofluorescent staining, RT-PCR, and RNA-sequencing were employed to examine the immunophenotype and genetic profile of PDBCCs.

RESULTS

More than 95% of PDBCCs sustain EpCAM high/+/fibroblast marker- phenotypes in monolayer conditions by subculturing 3-4 times. A83-01 removal induced senescent cells with high β-galactosidase activity. PDBCCs grown as monolayers were characterized by the majority of cells having an EpCAM+/CD49f + phenotype. Compared to full media in monolayer culture, EGF removal increased EpCAM+/CD49f - phenotype (13.8-fold, p = 0.028), whereas R-spondin removal reduced it (0.8-fold, p = 0.02). A83-01 removal increased EpCAM+/CD24 + phenotype (1.82-fold, p = 0.023) and decreased EpCAM low/-/CD44+/CD24- phenotype (0.45-fold, p = 0.026). Compared to monolayer, spheroid resulted in a significant increase in the population with EpCAM-/CD49+ (14.6-fold, p = 0.006) and EpCAM low/-/CD44+/CD24- phenotypes (4.16-fold, p = 0.022) and ALDH high activity (9.66-fold, p = 0.037). ALDH1A and EMT-related genes were upregulated. In RNA-sequencing analysis between spheroids and monolayers, a total of 561 differentially expressed genes (2-fold change, p < 0.05) were enriched in 27 KEGG pathways including signaling pathways regulating pluripotency of stem cells. In a recurrence-free survival analysis based on the Kaplan-Meier Plotter database of the up-and down-regulated genes identified in spheroids, 15 up-, and 14 down-regulated genes were associated with poor prognosis of breast cancer patients.

CONCLUSION

The media composition and spheroid culture method change in the BCSCs and EMT markers of PDBCCs, implying the importance of defining the media composition and culture method for studying PDBCCs in vitro.

A pH/GSH/Glucose Responsive Nanozyme for Tumor Cascade Amplified Starvation and Chemodynamic Theranostics

Nanozymes have brought enormous opportunities for disease theranostics. Here, a self-enhanced catalytic nanocrystal based on a bismuth-manganese core-shell nanoflower containing glucose oxide (GOx), termed BDS-GOx@MnOx, was designed for 4T1 tumor theranostics in vitro and in vivo. The BDS-GOx@MnOx nanozymes enable enhanced starvation treatment (ST) and chemotherapy (CDT) with high efficacy and exhibit sensitive tumor microenvironment (TME) responsive character for tumor therapy as well as for tumor-enhanced computer tomography (CT) and magnetic resonance (MR) diagnostic imaging. The characters and mechanism of the BDS-GOx@MnOx nanozymes have also been systematically studied and revealed.

Comparison of prognostic value of different metabolic response criteria determined by PET/CT in patients with metastatic breast cancer under CDK 4/6 inhibitor treatment

PURPOSE

This study evaluates the prognostic role of different [18F]FDG PET/CT metabolic response criteria in metastatic breast cancer (MBC) patients treated with cyclin-dependent kinase 4/6 inhibitors (CDK 4/6).

MATERIALS AND METHODS

We retrospectively evaluated the data of MBC patients treated with CDK 4/6 inhibitors who underwent an [18F]FDG PET/CT scan before starting and during treatment. [18F]FDG PET/CT response was assessed with the European Organization for Research and Treatment of Cancer (EORTC), PET Response Criteria in Solid Tumors (PERCIST), and whole-body total lesion glycolysis (WBTLG) criteria. Fleiss kappa was computed to assess the agreement between metabolic response criteria. The endpoint of the study was progression-free survival (PFS). PFS data were analyzed by the Kaplan-Meier method and compared using the log-rank test.

RESULTS

The study included sixteen MBC patients who received CDK 4/6 inhibitors therapy. According to PERCIST, partial metabolic response (PMR) was found in seven patients, stable metabolic disease (SMD) in seven patients, and progressive metabolic disease (PMD) in two patients. According to EORTC, PMR was detected in eight patients, SMD in seven patients, and PMD in one patient. According to WBTLG, PMR was found in 10 patients, SMD in four patients, and PMD in two patients. There was a fair agreement between the three criteria. While progression was detected in seven of the patients during follow-up, no progression was detected in nine of them. Kaplan-Meier analysis revealed that the responders according to WBTLG showed significantly longer PFS than non-responders.

CONCLUSION

Treatment response according to WBTLG criteria during treatment appears to be associated with prolonged PFS in patients treated with CDK 4/6 inhibitors for MBC.

Evaluation of breast cancer malignancy, prognostic factors and molecular subtypes using a continuous-time random-walk MR diffusion model

PURPOSE

To assess the continuous-time random-walk (CTRW) model's diagnostic value in breast lesions and to explore the associations between the CTRW parameters and breast cancer pathologic factors.

METHOD

This retrospective study included 85 patients (70 malignant and 18 benign lesions) who underwent 3.0T MRI examinations. Diffusion-weighted images (DWI) were acquired with 16b-values to fit the CTRW model. Three parameters (Dm, α, and β) derived from CTRW and apparent diffusion coefficient (ADC) from DWI were compared among the benign/malignant lesions, molecular prognostic factors, and molecular subtypes by Mann-Whitney U test. Spearman correlation was used to evaluate the associations between the parameters and prognostic factors. The diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC) based on the diffusion parameters.

RESULTS

All parameters, ADC, Dm, α, and β were significantly lower in the malignant than benign lesions (P < 0.05). The combination of all the CTRW parameters (Dm, α, and β) provided the highest AUC (0.833) and the best sensitivity (94.3%) in differentiating malignant status. And the positive status of estrogen receptor (ER) and progesterone receptor (PR) showed significantly lower β compared with the negative counterparts (P < 0.05). The high Ki-67 expression produced significantly lower Dm and ADC values (P < 0.05). Additionally, combining multiple CTRW parameters improved the performance of diagnosing molecular subtypes of breast cancer. Moreover, Spearman correlations analysis showed that β produced significant correlations with ER, PR and Ki-67 expression (P < 0.05).

CONCLUSIONS

The CTRW parameters could be used as non-invasive quantitative imaging markers to evaluate breast lesions.

Delineating intra-tumoral heterogeneity and tumor evolution in breast cancer using precision-based approaches

The burden of breast cancer continues to increase worldwide as it remains the most diagnosed tumor in females and the second leading cause of cancer-related deaths. Breast cancer is a heterogeneous disease characterized by different subtypes which are driven by aberrations in key genes such as BRCA1 and BRCA2, and hormone receptors. However, even within each subtype, heterogeneity that is driven by underlying evolutionary mechanisms is suggested to underlie poor response to therapy, variance in disease progression, recurrence, and relapse. Intratumoral heterogeneity highlights that the evolvability of tumor cells depends on interactions with cells of the tumor microenvironment. The complexity of the tumor microenvironment is being unraveled by recent advances in screening technologies such as high throughput sequencing; however, there remain challenges that impede the practical use of these approaches, considering the underlying biology of the tumor microenvironment and the impact of selective pressures on the evolvability of tumor cells. In this review, we will highlight the advances made thus far in defining the molecular heterogeneity in breast cancer and the implications thereof in diagnosis, the design and application of targeted therapies for improved clinical outcomes. We describe the different precision-based approaches to diagnosis and treatment and their prospects. We further propose that effective cancer diagnosis and treatment are dependent on unpacking the tumor microenvironment and its role in driving intratumoral heterogeneity. Underwriting such heterogeneity are Darwinian concepts of natural selection that we suggest need to be taken into account to ensure evolutionarily informed therapeutic decisions.

Development and External Validation of 18F-FDG PET-Based Radiomic Model for Predicting Pathologic Complete Response after Neoadjuvant Chemotherapy in Breast Cancer

The aim of our retrospective study is to develop and externally validate an 18F-FDG PET-derived radiomics model for predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer patients. A total of 87 breast cancer patients underwent curative surgery after NAC at Soonchunhyang University Seoul Hospital and were randomly assigned to a training cohort and an internal validation cohort. Radiomic features were extracted from pretreatment PET images. A radiomic-score model was generated using the LASSO method. A combination model incorporating significant clinical variables was constructed. These models were externally validated in a separate cohort of 28 patients from Soonchunhyang University Buscheon Hospital. The model performances were assessed using area under the receiver operating characteristic (AUC). Seven radiomic features were selected to calculate the radiomic-score. Among clinical variables, human epidermal growth factor receptor 2 status was an independent predictor of pCR. The radiomic-score model achieved good discriminability, with AUCs of 0.963, 0.731, and 0.729 for the training, internal validation, and external validation cohorts, respectively. The combination model showed improved predictive performance compared to the radiomic-score model alone, with AUCs of 0.993, 0.772, and 0.906 in three cohorts, respectively. The 18F-FDG PET-derived radiomic-based model is useful for predicting pCR after NAC in breast cancer.

Spatial topology of organelle is a new breast cancer cell classifier

Genomics and proteomics have been central to identify tumor cell populations, but more accurate approaches to classify cell subtypes are still lacking. We propose a new methodology to accurately classify cancer cells based on their organelle spatial topology. Herein, we developed an organelle topology-based cell classification pipeline (OTCCP), which integrates artificial intelligence (AI) and imaging quantification to analyze organelle spatial distribution and inter-organelle topology. OTCCP was used to classify a panel of human breast cancer cells, grown as 2D monolayer or 3D tumor spheroids using early endosomes, mitochondria, and their inter-organelle contacts. Organelle topology allows for a highly precise differentiation between cell lines of different subtypes and aggressiveness. These findings lay the groundwork for using organelle topological profiling as a fast and efficient method for phenotyping breast cancer function as well as a discovery tool to advance our understanding of cancer cell biology at the subcellular level.

Drug screening at single-organoid resolution via bioprinting and interferometry

High throughput drug screening is an established approach to investigate tumor biology and identify therapeutic leads. Traditional platforms use two-dimensional cultures which do not accurately reflect the biology of human tumors. More clinically relevant model systems such as three-dimensional tumor organoids can be difficult to scale and screen. Manually seeded organoids coupled to destructive endpoint assays allow for the characterization of treatment response, but do not capture transitory changes and intra-sample heterogeneity underlying clinically observed resistance to therapy. We present a pipeline to generate bioprinted tumor organoids linked to label-free, time-resolved imaging via high-speed live cell interferometry (HSLCI) and machine learning-based quantitation of individual organoids. Bioprinting cells gives rise to 3D structures with unaltered tumor histology and gene expression profiles. HSLCI imaging in tandem with machine learning-based segmentation and classification tools enables accurate, label-free parallel mass measurements for thousands of organoids. We demonstrate that this strategy identifies organoids transiently or persistently sensitive or resistant to specific therapies, information that could be used to guide rapid therapy selection.

CRISPR-Cas9 screen reveals a role of purine synthesis for estrogen receptor α activity and tamoxifen resistance of breast cancer cells

In breast cancer, resistance to endocrine therapies that target estrogen receptor α (ERα), such as tamoxifen and fulvestrant, remains a major clinical problem. Whether and how ERα⁺ breast cancers switch from being estrogen-dependent to estrogen-independent remains unclear. With a genome-wide CRISPR-Cas9 knockout screen, we identified previously unknown biomarkers and potential therapeutic targets of endocrine resistance. We demonstrate that high levels of PAICS, an enzyme involved in the de novo biosynthesis of purines, can shift the balance of ERα activity to be more estrogen-independent and tamoxifen-resistant. We find that this may be due to elevated activities of cAMP-activated protein kinase A and mTOR, kinases known to phosphorylate ERα specifically and to stimulate its activity. Genetic or pharmacological targeting of PAICS sensitizes tamoxifen-resistant cells to tamoxifen. Addition of purines renders them more resistant. On the basis of these findings, we propose the combined targeting of PAICS and ERα as a new, effective, and potentially safe therapeutic regimen.

Multimodal ultrasound features of breast cancers: correlation with molecular subtypes

OBJECTIVES

To investigate whether multimodal intratumour and peritumour ultrasound features correlate with specific breast cancer molecular subtypes.

METHODS

From March to November 2021, a total of 85 patients with histologically proven breast cancer underwent B-mode, real-time elastography (RTE), colour Doppler flow imaging (CDFI) and contrast-enhanced ultrasound (CEUS). The time intensity curve (TIC) of CEUS was obtained, and the peak and time to peak (TTP) were analysed. Chi-square and binary logistic regression were used to analyse the connection between multimodal ultrasound imaging features and breast cancer molecular subtype.

RESULTS

Among 85 breast cancers, the subtypes were as follows: luminal A (36 cases, 42.4%), luminal B (20 cases, 23.5%), human epidermal growth factor receptor-2 positive (HER2+) (16 cases, 18.8%), and triple negative breast cancer (TNBC) (13 cases, 15.3%). Binary logistic regression models showed that RTE (P < 0.001) and CDFI (P = 0.036) were associated with the luminal A cancer subtype (C-index: 0.741), RTE (P = 0.016) and the peak ratio between intratumour and corpus mamma (P = 0.036) were related to the luminal B cancer subtype (C-index: 0.788). The peak ratio between peritumour and intratumour (P = 0.039) was related to the HER2 + cancer subtype (C-index: 0.859), and CDFI (P = 0.002) was associated with the TNBC subtype (C-index: 0.847).

CONCLUSIONS

Multimodal ultrasound features could be powerful predictors of specific breast cancer molecular subtypes. The intra- and peritumour CEUS features play assignable roles in separating luminal B and HER2 + breast cancer subtypes.

Liquid Biopsy in the Management of Breast Cancer Patients: Where Are We Now and Where Are We Going

Liquid biopsy (LB) is an emerging diagnostic tool that analyzes biomarkers in the blood (and possibly in other body fluids) to provide information about tumor genetics and response to therapy. This review article provides an overview of LB applications in human cancer with a focus on breast cancer patients. LB methods include circulating tumor cells and cell-free tumor products, such as circulating tumor DNA. LB has shown potential in detecting cancer at an early stage, monitoring tumor progression and recurrence, and predicting patient response to therapy. Several studies have demonstrated its clinical utility in breast cancer patients. However, there are limitations to LB, including the lack of standardized assays and the need for further validation. Future potential applications of LB include identifying the minimal residual disease, early detection of recurrence, and monitoring treatment response in various cancer types. LB represents a promising non-invasive diagnostic tool with potential applications in breast cancer diagnosis, treatment, and management. Further research is necessary to fully understand its clinical utility and overcome its current limitations.

Intratumoral heterogeneity, treatment response, and survival outcome of ER-positive HER2-positive breast cancer

BACKGROUND

ER+HER2+ breast cancer requires most types of systemic therapies perioperatively. However, treatment resistance is often experienced. The current study investigated the predictive and prognostic value of intratumoral heterogeneity and conventional clinicopathological factors in patients with ER+HER2+ breast cancer.

METHODS

This research included two patient cohorts with ER+HER2+ breast cancer. Cohort A included patients who underwent surgery without neoadjuvant chemotherapy (NAC). Cohort B comprised patients who received NAC followed by surgery. Intratumoral heterogeneity was assessed via ER and HER2 double staining, and the number of cells stained with different patterns of ER and HER2 was counted.

RESULTS

In total, 11 of 92 tumors in cohort A and four of 45 tumors in cohort B consisted exclusively of double-positive (ER+ and HER2+) cells (homogeneous). The rest had different combinations of cells (heterogeneous). The pathological complete response (pCR) rates differed based on tumoral cell components but not intratumoral heterogeneity. The pCR rate of tumors with ER-HER2+ cells but without HER2- cells was higher than that of others (45.5% vs 4.3%; p = 0.0013). Low ER and PgR Allred scores indicated better pCR rates than high scores (p = 0.0005 and 0.024, respectively). Multivariate analysis showed that the ER Allred score and cell component of ER-HER2+ cells without HER2- cells were independent predictors of pCR (p = 0.0055 and 0.0081, respectively). In cohort B, posttreatment Ki67, but not pCR, was a prognostic factor of DFS and OS (p = 0.028 and 0.017, respectively). The prognostic value of combined posttreatment Ki67 and pCR was superior to that of either alone. Combined pCR and posttreatment Ki67 had an independent prognostic value for DFS and OS (p = 0.0068 and 0.0101, respectively).

CONCLUSIONS

In ER+HER2+ breast cancer, the presence of ER-HER2+ cells without HER2- cells was independently associated with pCR. Combined posttreatment Ki67 and pCR can be more precise in predicting prognosis than pCR alone.

Recent progress of nanostructure-based enrichment of circulating tumor cells and downstream analysis

The isolation and detection of circulating tumor cells (CTCs) play an important role in early cancer diagnosis and prognosis, providing easy access to identify metastatic cells before clinically detectable metastases. In the past 20 years, according to the heterogeneous expression of CTCs on the surface and their special physical properties (size, morphology, electricity, etc.), a series of in vitro enrichment methods of CTCs have been developed based on microfluidic chip technology, nanomaterials and various nanostructures. In recent years, the in vivo detection of CTCs has attracted considerable attention. Photoacoustic flow cytometry and fluorescence flow cytometry were used to detect CTCs in a noninvasive manner. In addition, flexible magnetic wire and indwelling intravascular non-circulating CTCs isolation system were developed for in vivo CTCs study. In the aspect of downstream analysis, gene analysis and drug sensitivity tests of enriched CTCs were developed based on various existing molecular analysis techniques. All of these studies constitute a complete study of CTCs. Although the existing reviews mainly focus on one aspect of capturing CTCs study, a review that includes the in vivo and in vitro capture and downstream analysis study of CTCs is highly needed. This review focuses on not only the classic work and latest research progress in in vitro capture but also includes the in vivo capture and downstream analysis, discussing the advantages and significance of the different research methods and providing new ideas for solving the heterogeneity and rarity of CTCs.

Brain metastasis in breast cancer: focus on genes and signaling pathways involved, blood-brain barrier and treatment strategies

Breast cancer (BC) is one of the most prevalent types of cancer in women. Despite advancement in early detection and efficient treatment, recurrence and metastasis continue to pose a significant risk to the life of BC patients. Brain metastasis (BM) reported in 17-20 percent of BC patients is considered as a major cause of mortality and morbidity in these patients. BM includes various steps from primary breast tumor to secondary tumor formation. Various steps involved are primary tumor formation, angiogenesis, invasion, extravasation, and brain colonization. Genes involved in different pathways have been reported to be associated with BC cells metastasizing to the brain. ADAM8 gene, EN1 transcription factor, WNT, and VEGF signaling pathway have been associated with primary breast tumor; MMP1, COX2, XCR4, PI3k/Akt, ERK and MAPK pathways in angiogenesis; Noth, CD44, Zo-1, CEMIP, S0X2 and OLIG2 are involved in invasion, extravasation and colonization, respectively. In addition, the blood-brain barrier is also a key factor in BM. Dysregulation of cell junctions, tumor microenvironment and loss of function of microglia leads to BBB disruption ultimately resulting in BM. Various therapeutic strategies are currently used to control the BM in BC. Oncolytic virus therapy, immune checkpoint inhibitors, mTOR-PI3k inhibitors and immunotherapy have been developed to target various genes involved in BM in BC. In addition, RNA interference (RNAi) and CRISPR/Cas9 are novel interventions in the field of BCBM where research to validate these and clinical trials are being carried out. Gaining a better knowledge of metastasis biology is critical for establishing better treatment methods and attaining long-term therapeutic efficacies against BC. The current review has been compiled with an aim to evaluate the role of various genes and signaling pathways involved in multiple steps of BM in BC. The therapeutic strategies being used currently and the novel ones being explored to control BM in BC have also been discussed at length.

Iron cycle disruption by heme oxygenase-1 activation leads to a reduced breast cancer cell survival

Heme oxygenase-1 (HO-1), which catalyzes heme degradation releasing iron, regulates several processes related to breast cancer. Iron metabolism deregulation is also connected with several tumor processes. However the regulatory relationship between HO-1 and iron proteins in breast cancer remains unclear. Using human breast cancer biopsies, we found that high HO-1 levels significantly correlated with low DMT1 levels. Contrariwise, high HO-1 levels significantly correlated with high ZIP14 and prohepcidin expression, as well as hemosiderin storage. At mRNA level, we found that high HO-1 expression significantly correlated with low DMT1 expression but high ZIP14, L-ferritin and hepcidin expression. In in vivo experiments in mice with genetic overexpression or pharmacological activation of HO-1, we detected the same expression pattern observed in human biopsies. In in vitro experiments, HO-1 activation induced changes in iron proteins expression leading to an increase of hemosiderin, ROS levels, lipid peroxidation and a decrease of the growth rate. Such low growth rate induced by HO-1 activation was reversed when iron levels or ROS levels were reduced. Our findings demonstrate an important role of HO-1 on iron homeostasis in breast cancer. The changes in iron proteins expression when HO-1 is modulated led to the iron accumulation deregulating the iron cell cycle, and consequently, generating oxidative stress and low viability, all contributing to impair breast cancer progression.

Conversion of ER and HER2 status after neoadjuvant therapy in Chinese breast cancer patients

BACKGROUND

Few studies have focused on converting ER-low-positive and HER2-low status following neoadjuvant therapy (NAT). We aimed to assess the evolution in ER and HER2 status after NAT in breast cancer patients.

PATIENTS AND METHODS

Our study included 481 patients with residual invasive breast cancer after NAT. ER and HER2 status were assessed in the primary tumor and residual disease, and associations between ER and HER2 conversion and clinicopathological factors were explored.

RESULTS

In primary tumors, 305 (63.4%) cases were ER-positive (including 36 cases of ER-low-positive), 176 (36.6%) were ER-negative. In residual disease, ER status changed in 76 (15.8%) cases, of which 69 cases switched from positive to negative. ER-low-positive tumors (31/36) were the most likely to change. In primary tumors, 140 (29.1%) tumors were HER2-positive, and 341 (70.9%) were HER2-negative (including 209 cases of HER2-low and 132 cases of HER2-zero). In residual disease, 25 (5.2%) cases had HER2 conversion between positive and negative. Considering HER2-low status, 113 (23.5%) cases had HER2 conversion, mostly driven by cases switching either to or from HER2-low. ER conversion had a positive correlation with pretreatment ER status (r = 0.25; P = .00). There was a positive correlation between HER2 conversion and HER2-targeted therapy (r = 0.18; P = .00).

CONCLUSION

Conversion of ER and HER2 status was observed in some breast cancer patients after NAT. Both ER-low-positive and HER2-low tumors showed high instability from the primary tumor to residual disease. ER and HER2 status should be retested in residual disease for further treatment decisions, especially in ER-low-positive and HER2-low breast cancer.

Evolution of biological features of invasive lobular breast cancer: comparison between primary tumor and metastases

BACKGROUND

Invasive lobular carcinoma (ILC) has unique clinical-biological features. Phenotypical differences between primary tumours (PTs) and metastases (M) have been described for invasive ductal carcinoma, but data on ILC are limited.

METHODS

We retrospectively analysed patients with recurrent ILC from our institution from 2013 to 2020. We evaluated the discordance of the oestrogen receptor (ER), progesterone receptor (PgR) and HER2 between PT and M, to understand prognostic and therapeutic implications.

RESULTS

Thirteen percent (n = 91) of all patients had ILC. We observed 15%, 44% and 5% of ER, PgR and HER2 status discordance between PT and M. ER/PgR discordance was related to receptor loss and HER2 mainly due to gain. PT presented a luminal-like phenotype (93%); 6% and 1% were triple-negative (TNBC) and HER2-positive. In M, there was an increase in TNBC (16%) and HER2-positive (5%). Metastasis-free survival and overall survival (OS) were different according to clinical phenotype, with poorer prognosis for HER2+ and TNBC (p < 0.001); OS after metastatic progression did not differ across phenotypes (p = 0.079). In luminal-like ILC (n = 85) at diagnosis, we found that OS after relapse was poorer in patients experiencing a phenotype switch to TNBC but improved in patients with HER2 gain (p = 0.0028). Poorer survival was reported in patients with a PgR and/or ER expression loss of ≥25%. There was HER2-low enrichment in M1 (from 37% to 58%): this change was not associated with OS (p > 0.05).

CONCLUSION

Our results suggest that phenotype switch after metastatic progression may be associated with patients' outcomes. Tumour biopsy in recurrent ILC could drive treatment decision-making, with prognostic implications.

Design and performance simulation studies of a breast PET insert integrable into a clinical whole-body PET/MRI scanner

Objective. Three different breast positron emission tomography (PET) insert geometries are proposed for integration into an existing magnetic resonance imaging (MRI) breast coil (Breast Biopsy Coil, NORAS MRI products) to be used inside a whole-body PET/MRI scanner (Biograph mMR, Siemens Healthineers) to enhance the sensitivity and spatial resolution of imaging inside the breast.Approach. Monte Carlo simulations were performed to predict and compare the performance characteristics of the three geometries in terms of the sensitivity, spatial resolution, scatter fraction, and noise equivalent count rate (NECR). In addition, the background single count rate due to organ uptake in a clinical scan scenario was predicted using a realistic anthropomorphic phantom.Main results. In the center of the field of view (cFOV), absolute sensitivities of 3.1%, 2.7%, and 2.2% were found for Geometry A (detectors arranged in two cylinders), Geometry B (detectors arranged in two partial cylinders), and Geometry C (detectors arranged in two half cylinders combined with two plates), respectively. The full width at half maximum spatial resolution was determined to be 1.7 mm (Geometry A), 1.8 mm (Geometry B) and 2.0 mm (Geometry C) at 5 mm from the cFOV. Designs with multiple scintillation-crystal layers capable of determining the depth of interaction (DOI) strongly improved the spatial resolution at larger distances from the transaxial cFOV. The system scatter fractions were 33.1% (Geometries A and B) and 32.3% (Geometry C). The peak NECRs occurred at source activities of 300 MBq (Geometry A), 310 MBq (Geometry B) and 340 MBq (Geometry C). The background single-event count rates were 17.1 × 10⁶cps (Geometry A), 15.3 × 10⁶cps (Geometry B) and 14.8 × 10⁶cps (Geometry C). Geometry A in the three-layer DOI variant exhibited the best PET performance characteristics but could be challenging to manufacture. Geometry C had the lowest impact on the spatial resolution and the lowest sensitivity among the investigated geometries.Significance. Geometry B in the two-layer DOI variant represented an effective compromise between the PET performance and manufacturing difficulty and was found to be a promising candidate for the future breast PET insert.

Cysteine Cathepsins in Breast Cancer: Promising Targets for Fluorescence-Guided Surgery

The majority of breast cancer patients is treated with breast-conserving surgery (BCS) combined with adjuvant radiation therapy. Up to 40% of patients has a tumor-positive resection margin after BCS, which necessitates re-resection or additional boost radiation. Cathepsin-targeted near-infrared fluorescence imaging during BCS could be used to detect residual cancer in the surgical cavity and guide additional resection, thereby preventing tumor-positive resection margins and associated mutilating treatments. The cysteine cathepsins are a family of proteases that play a major role in normal cellular physiology and neoplastic transformation. In breast cancer, the increased enzymatic activity and aberrant localization of many of the cysteine cathepsins drive tumor progression, proliferation, invasion, and metastasis. The upregulation of cysteine cathepsins in breast cancer cells indicates their potential as a target for intraoperative fluorescence imaging. This review provides a summary of the current knowledge on the role and expression of the most important cysteine cathepsins in breast cancer to better understand their potential as a target for fluorescence-guided surgery (FGS). In addition, it gives an overview of the cathepsin-targeted fluorescent probes that have been investigated preclinically and in breast cancer patients. The current review underscores that cysteine cathepsins are highly suitable molecular targets for FGS because of favorable expression and activity patterns in virtually all breast cancer subtypes. This is confirmed by cathepsin-targeted fluorescent probes that have been shown to facilitate in vivo breast cancer visualization and tumor resection in mouse models and breast cancer patients. These findings indicate that cathepsin-targeted FGS has potential to improve treatment outcomes in breast cancer patients.

Identifying Associations between DCE-MRI Radiomic Features and Expression Heterogeneity of Hallmark Pathways in Breast Cancer: A Multi-Center Radiogenomic Study

BACKGROUND

To investigate the relationship between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic features and the expression activity of hallmark pathways and to develop prediction models of pathway-level heterogeneity for breast cancer (BC) patients.

METHODS

Two radiogenomic cohorts were analyzed (n = 246). Tumor regions were segmented semiautomatically, and 174 imaging features were extracted. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed to identify significant imaging-pathway associations. Random forest regression was used to predict pathway enrichment scores. Five-fold cross-validation and grid search were used to determine the optimal preprocessing operation and hyperparameters.

RESULTS

We identified 43 pathways, and 101 radiomic features were significantly related in the discovery cohort (p-value < 0.05). The imaging features of the tumor shape and mid-to-late post-contrast stages showed more transcriptional connections. Ten pathways relevant to functions such as cell cycle showed a high correlation with imaging in both cohorts. The prediction model for the mTORC1 signaling pathway achieved the best performance with the mean absolute errors (MAEs) of 27.29 and 28.61% in internal and external test sets, respectively.

CONCLUSIONS

The DCE-MRI features were associated with hallmark activities and may improve individualized medicine for BC by noninvasively predicting pathway-level heterogeneity.

Targeting Platelet Activation Pathways to Limit Tumour Progression: Current State of Affairs

The association between cancer and a hypercoagulatory environment is well described. Thrombotic complications serve not only as a major mortality risk but the underlying molecular structure and function play significant roles in enhancing tumour progression, which is defined as the tumour's capacity to survive, invade and metastasise, amongst other hallmarks of the disease. The use of anticoagulant or antiplatelet drugs in cardiovascular disease lessens thrombotic effects, but the consequences on tumour progression require interrogation. Therefore, this review considered developments in the management of platelet activation pathways (thromboxane, ADP and thrombin), focusing on the use of Aspirin, Clopidogrel and Atopaxar, and their potential impacts on tumour progression. Published data suggested a cautionary tale in ensuring we adequately investigate not only drug-drug interactions but also those unforeseen reciprocal interactions between drugs and their targets within the tumour microenvironment that may act as selective pressures, enhancing tumour survival and progression.

Transcriptomics indicate nuclear division and cell adhesion not recapitulated in MCF7 and MCF10A compared to luminal A breast tumours

Breast cancer (BC) cell lines are useful experimental models to understand cancer biology. Yet, their relevance to modelling cancer remains unclear. To better understand the tumour-modelling efficacy of cell lines, we performed RNA-seq analyses on a combined dataset of 2D and 3D cultures of tumourigenic MCF7 and non-tumourigenic MCF10A. To our knowledge, this was the first RNA-seq dataset comprising of 2D and 3D cultures of MCF7 and MCF10A within the same experiment, which facilitates the elucidation of differences between MCF7 and MCF10A across culture types. We compared the genes and gene sets distinguishing MCF7 from MCF10A against separate RNA-seq analyses of clinical luminal A (LumA) and normal samples from the TCGA-BRCA dataset. Among the 1031 cancer-related genes distinguishing LumA from normal samples, only 5.1% and 15.7% of these genes also distinguished MCF7 from MCF10A in 2D and 3D cultures respectively, suggesting that different genes drive cancer-related differences in cell lines compared to clinical BC. Unlike LumA tumours which showed increased nuclear division-related gene expression compared to normal tissue, nuclear division-related gene expression in MCF7 was similar to MCF10A. Moreover, although LumA tumours had similar cell adhesion-related gene expression compared to normal tissues, MCF7 showed reduced cell adhesion-related gene expression compared to MCF10A. These findings suggest that MCF7 and MCF10A cell lines were limited in their ability to model cancer-related processes in clinical LumA tumours.

Biological and clinical review of IORT-induced wound fluid in breast cancer patients

Intraoperative radiotherapy (IORT) has become a growing therapy for early-stage breast cancer (BC). Some studies claim that wound fluid (seroma), a common consequence of surgical excision in the tumor cavity, can reflect the effects of IORT on cancer inhibition. However, further research by our team and other researchers, such as analysis of seroma composition, affected cell lines, and primary tissues in two-dimensional (2D) and three-dimensional (3D) culture systems, clarified that seroma could not address the questions about IORT effectiveness in the surgical site. In this review, we mention the factors involved in tumor recurrence, direct or indirect effects of IORT on BC, and all the studies associated with BC seroma to attain more information about the impact of IORT-induced seroma to make a better decision to remove or remain after surgery and IORT. Finally, we suggest that seroma studies cannot decipher the mechanisms underlying the effectiveness of IORT in BC patients. The question of whether IORT-seroma has a beneficial effect can only be answered in a trial with a clinical endpoint, which is not even ongoing.

Identification of immune cell function in breast cancer by integrating multiple single-cell data

Breast cancer has now become the most commonly diagnosed cancer worldwide. It is a highly complex and heterogeneous disease that comprises distinct histological features and treatment response. With the development of molecular biology and immunology, immunotherapy has become a new field of breast cancer treatment. Identifying cell-type-specific genes critical to the immune microenvironment contributes to breast cancer treatment. Single-cell RNA sequencing (scRNA-seq) technology could serve as a powerful tool to analyze cellular genetic information at single-cell resolution and to uncover the gene expression status of each cell, thus allowing comprehensive assessment of intercellular heterogeneity. Because of the influence of sample size and sequencing depth, the specificity of genes in different cell types for breast cancer cannot be fully revealed. Therefore, the present study integrated two public breast cancer scRNA-seq datasets aiming to investigate the functions of different type of immune cells in tumor microenvironment. We identified total five significant differential expressed genes of B cells, T cells and macrophage and explored their functions and immune mechanisms in breast cancer. Finally, we performed functional annotation analyses using the top fifteen differentially expressed genes in each immune cell type to discover the immune-related pathways and gene ontology (GO) terms.

Unsupervised Analysis Based on DCE-MRI Radiomics Features Revealed Three Novel Breast Cancer Subtypes with Distinct Clinical Outcomes and Biological Characteristics

Background: This study aimed to reveal the heterogeneity of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of breast cancer (BC) and identify its prognosis values and molecular characteristics. Methods: Two radiogenomics cohorts (n = 246) were collected and tumor regions were segmented semi-automatically. A total of 174 radiomics features were extracted, and the imaging subtypes were identified and validated by unsupervised analysis. A gene-profile-based classifier was developed to predict the imaging subtypes. The prognostic differences and the biological and microenvironment characteristics of subtypes were uncovered by bioinformatics analysis. Results: Three imaging subtypes were identified and showed high reproducibility. The subtypes differed remarkably in tumor sizes and enhancement patterns, exhibiting significantly different disease-free survival (DFS) or overall survival (OS) in the discovery cohort (p = 0.024) and prognosis datasets (p ranged from <0.0001 to 0.0071). Large sizes and rapidly enhanced tumors usually had the worst outcomes. Associations were found between imaging subtypes and the established subtypes or clinical stages (p ranged from <0.001 to 0.011). Imaging subtypes were distinct in cell cycle and extracellular matrix (ECM)-receptor interaction pathways (false discovery rate, FDR < 0.25) and different in cellular fractions, such as cancer-associated fibroblasts (p < 0.05). Conclusions: The imaging subtypes had different clinical outcomes and biological characteristics, which may serve as potential biomarkers.

A single centre experience in Turkey for comparison between core needle biopsy and surgical specimen evaluation results for HER2, SISH, estrogen receptors and progesterone receptors in breast cancer patients

Background

Breast carcinoma diagnosis can be made with core-needle biopsy (CNB), but there are controversies regarding the evaluation of hormone receptor (HR) status in needle biopsy specimens. When preoperative neoadjuvant therapy is required in breast cancer cases, the CNB specimen should be evaluated to decide on the treatment.

Objectives

In this study, we aimed to compare the estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and silver in situ hybridization (SISH) results of the CNB specimens and surgical specimens (SS) of our breast carcinoma cases.

Materials and Methods

This retrospective study included cases diagnosed with breast cancer in our center for approximately 1 year between 2017 and 2018. About 97 cases with both CNB specimens and SS were included in the study. Data such as the ER, PR, HER2, and SISH evaluation results in CNB and SS, age distribution and histopathological type, metastatic lymph nodes, lymphovascular invasion, tumor size, and grade of the tumor were recorded. The data were analyzed using SPSS 22.0 (Statistical Package for the Social Sciences, Armonk, NY, USA) software.

Results

All of the cases were female and 70.1% of them were aged over 45. About 27.8% of the cases were aged 31-45 years, and 2.1% were aged under 30. When evaluated according to the histopathological type of the tumor, 71.1% of cases were invasive ductal carcinoma (IDC), 8.2% were invasive lobular carcinoma (ILC), 6.2% were IDC + ILC, 11.3% were another carcinoma, and 3.1% were in situ carcinoma. 12.4% of the cases were Grade I, 43.3% were Grade II, and 20.6% were Grade III. 43.3% of our cases' tumor size were ≤2 cm and 56.7% >2 cm. 50 (51.5%) of these cases had no lymph node metastasis. It was found out that 36 (37.1%) of the cases had 1-4 metastatic lymph nodes and 11 (11.3%) of them had 5 and more metastatic lymph nodes. It was found out that 44 (45.4.%) of the cases had no lymphovascular invasion and 53 (54.6%) of them had a lymphovascular invasion. When HR statuses in CNB and SS were compared, ER was found to have a sensitivity of 96.1% and a specificity of 100%. PR was found to have a sensitivity of 94.2% and a specificity of 66.7%. HER2 was found to have a sensitivity of 100% and a specificity of 73.4%.

Conclusion

ER evaluation results are reliable in deciding on needle biopsy material. PR may show the heterogeneous distribution in HER2 tumor. Thus, if PR and HER2 results in needle biopsy material are negative, assessments should be repeated in SS.

Differences in sensitivity to new therapies between primary and metastatic breast cancer: A need to stratify the tumor response?

OBJECTIVE

We compared therapeutic response of Varlitinib + Capecitabine (VC) versus Lapatinib + Capecitabine (LC) in patients with human epidermal growth factor receptor 2-positive metastatic breast cancer after trastuzumab therapy by assessing changes in target lesion (TL) diameter and volume per location.

METHODS

We retrospectively analyzed the CT data of the ASLAN001-003 study (NCT02338245). We analyzed TL size and number at each location focusing on therapeutic response from baseline to Week 12. We used TL diameter and volume to conduct an inter-arm comparison of the response according to: RECIST 1.1; stratified per TL location and considering TLs independently. Multiple pairwise intra-arm comparisons of therapeutic responses were performed. Considering TL independently, weighted models were designed by adding weighted mean TL responses grouped by location.

RESULTS

We evaluated 42 patients (88 TL) and 35 patients (74 TL), respectively, at baseline and Week 12. We found reductions in breast TL burden in the VC arm compared to the LC arm (p = 0.002 (diameter), p < 0.001 (volume)). Responses and TL sizes at baseline were not correlated. Explained variabilities of volume change per TL location, patient and patient:TL interaction were 36%, 10% and 4% (VC), and 13%, 1% and 23%, (LC). A test of inter-arm difference of responses yielded p = 0.07 (diameter), and p < 0.001 (volume).

CONCLUSIONS

The therapeutic responses differed across tumors' locations; the magnitude of the differences of responses across the tumors' locations were drug-dependent. Stratified analysis of the response by tumor location improved drug comparisons and is a powerful tool to understand TL heterogeneity.

Highly bioactive novel aryl-, benzyl-, and piperazine-selenoureas: synthesis, structural characterization and in vitro biological evaluation

Selenoureas are widespread as useful elements for constructing important species and biologically active molecules. Finding an efficient and straightforward method to prepare this motif and biologically screen derivatives thereof is crucial. Herein, we demonstrate the effectiveness of using ethanol as a solvent in the preparation of various substituted aryl-, benzyl-, and piperazine-selenoureas from isoselenocyanates and amines. The synthetic method includes mild reaction conditions, large substrate scope, and good isolated yields. Biological evaluation of the prepared products on MDA-MB-231 and MCF-7 cancer cell lines revealed several remarkably active compounds (IC₅₀ < 10 μΜ) with the best one exhibiting IC₅₀ values of 1.8 μΜ and 1.2 μΜ observed against the challenging former triple-negative breast cancer cell line and the latter one, respectively. The chemical structures of all new compounds were fully characterized by multinuclear nuclear magnetic resonance (NMR) spectroscopy and high accuracy mass measurements.