Breast cancer heterogeneity through the lens of single-cell analysis and spatial pathologies

Multi-omic profiling in breast cancer: utility for advancing diagnostics and clinical care

INTRODUCTION

Breast cancer remains a major global health challenge. While advances in precision oncology have contributed to improvements in patient outcomes and provided a deeper understanding of the biological mechanisms that drive the disease, historically, research and patients' allocation to treatment have heavily relied on single-omic approaches, analyzing individual molecular dimensions such as genomics, transcriptomics, or proteomics. While these have provided deep insights into breast cancer biology, they often fail to offer a complete understanding of the disease's complex molecular landscape.

AREAS COVERED

In this review, the authors explore the recent advancements in multi-omic research in the realm of breast cancer and use clinical data to show how multi-omic integration can offer a more holistic understanding of the molecular alterations and their functional consequences underlying breast cancer.

EXPERT OPINION

The overall developments in multi-omic research and AI are expected to complement precision diagnostics through potentially refining prognostic models, and treatment selection. Overcoming challenges such as cost, data complexity, and lack of standardization is crucial for unlocking the full potential of multi-omics and AI in breast cancer patient care to enable the advancement of personalized treatments and improve patient outcomes.

Investigation of TMEM41A's function in breast cancer prognosis and its connection to immune cell infiltration

BACKGROUND

Globally, breast cancer is the most common type of malignant tumor. It has been demonstrated that TMEM41A is abnormally expressed in a number of cancers and is linked to a dismal prognosis. TMEM41A's involvement in breast cancer remains unknown, though.

METHODS

Data from databases such as TCGA were used in this study. Expression differences were compared using non-parametric tests. Cox regression analysis was employed, and analyses such as Nomogram were used to assess the significance of TMEM41A in predicting the prognosis of breast cancer. Lastly, it was looked into how immune cell infiltration in breast cancer is related to TMEM41A expression levels.

RESULTS

The results suggest that TMEM41A is overexpressed in breast cancer and correlates with poor prognosis (P = 0.01), particularly in early-stage and ductal A breast cancer (P < 0.01). Breast cancer patients' expression of TMEM41A was found to be an independent risk factor (HR = 1.132, 95% CI 1.036-1.237) by multifactorial Cox regression analysis. The Nomogram prediction model's c-index was 0.736 (95% CI 0.684-0.787). The results of GSEA biofunctional enrichment analysis included the B cell receptor signaling pathway (P < 0.05). Ultimately, there was a significant correlation (P < 0.05) between TMEM41A expression in breast cancer and an infiltration of twenty immune cells.

CONCLUSIONS

Breast cancer tissues overexpress TMEM41A, which is linked to immune cell infiltration and prognosis (particularly in early stage and luminal A breast cancer). Overexpression of TMEM41A is anticipated to serve as a novel prognostic indicator and therapeutic target for breast cancer.

Unveiling the role of MDH1 in breast cancer drug resistance through single-cell sequencing and schottenol intervention

This study utilizes single-cell RNA sequencing data to reveal the transcriptomic characteristics of breast cancer and normal epithelial cells. Nine significant cell populations were identified through stringent quality control and batch effect correction. Further classification of breast cancer epithelial cells based on the PAM50 method and clinical subtypes highlighted significant heterogeneity between triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (NTNBC). The study also analyzed myeloid cells and tumor-infiltrating lymphocytes (TILs) within the breast cancer immune microenvironment, identifying 14 TIL subpopulations and assessing their proportion variations across different patients. The CellChat tool revealed a complex cellular communication network within the tumor microenvironment, showing notable differences in communication intensity and patterns between TNBC and NTNBC patients. Additionally, the key regulatory role of the senescence-associated gene MDH1 in breast cancer was confirmed, and its impact on drug sensitivity was explored. Finally, it was discovered that the phytosterol Schottenol inhibits breast cancer cell proliferation by downregulating MDH1 expression and enhances sensitivity to paclitaxel. These findings provide new insights into MDH1 as a therapeutic target and suggest Schottenol as a potential strategy to overcome breast cancer drug resistance.

Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images

SIGNIFICANCE

A deep learning model, trained using transcriptomic data, inexpensively quantifies and fine-maps ITH due to subtype admixture in routine images of LumA breast cancer, the most favorable subtype. This new approach could facilitate exploration of the mechanisms behind such heterogeneity and its impact on selection of therapy for individual patients.

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

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

A new therapeutic strategy for luminal A-breast cancer treatment: vulpinic acid as an anti-neoplastic agent induces ferroptosis and apoptosis mechanisms

Breast cancer is a common invasive tumor in women, and the most common subtype of breast cancer is luminal A. Hormonal therapies are the primary treatment for luminal A, but treatment options are limited. Vulpinic acid (VA), a lichen compound, inhibited cancer cells. Here, we aimed to reveal the functional role and mechanism of VA in luminal A breast cancer. Experiments associated with the ferroptosis mechanism were performed to reveal the role of vulpinic acid on luminal A-breast cancer and the underlying mechanisms. The results showed that VA induced the ferroptosis pathway by decreasing glutathione (GSH) levels while increasing lipid reactive oxygen species (ROS), lipid peroxidation (MDA), and intracellular Fe2+ levels in MCF-7 cells. After treatment of MCF-7 cells with VA, the ferroptosis-related gene expression profile was significantly altered. Western blot analysis showed that GPX4 protein levels were down-regulated and LPCAT3 protein levels were up-regulated after VA treatment. Our study suggests that apoptosis and ferroptosis act together in VA-mediated tumor suppression in MCF-7 breast cancer cells. These findings suggest that VA, an anti-neoplastic agent, could potentially treat luminal A targeted breast cancer via the ferroptosis pathway.

Applications of spatial transcriptomics and artificial intelligence to develop integrated management of pancreatic cancer

Cancer is a complex disease intrinsically associated with cellular processes and gene expression. With the development of techniques such as single-cell sequencing and sequential fluorescence in situ hybridization (seqFISH), it was possible to map the location of cells based on their gene expression with more precision. Moreover, in recent years, many tools have been developed to analyze these extensive datasets by integrating machine learning and artificial intelligence in a comprehensive manner. Since these tools analyze sequencing data, they offer the chance to analyze any tissue regardless of its origin. By applying this to cancer settings, spatial transcriptomic analysis based on artificial intelligence may help us understand cell-cell communications within the tumor microenvironment. Another advantage of this analysis is the identification of new biomarkers and therapeutic targets. The integration of such analysis with other omics data and with routine exams such as magnetic resonance imaging can help physicians with the earlier diagnosis of tumors as well as establish a more personalized treatment for pancreatic cancer patients. In this review, we give an overview description of pancreatic cancer, describe how spatial transcriptomics and artificial intelligence have been used to study pancreatic cancer and provide examples of how integrating these tools may help physicians manage pancreatic cancer in a more personalized approach.

Deep learning-guided adjuvant chemotherapy selection for elderly patients with breast cancer

PURPOSE

The efficacy of adjuvant chemotherapy in elderly breast cancer patients is currently controversial. This study aims to provide personalized adjuvant chemotherapy recommendations using deep learning (DL).

METHODS

Six models with various causal inference approaches were trained to make individualized chemotherapy recommendations. Patients who received actual treatment recommended by DL models were compared with those who did not. Inverse probability treatment weighting (IPTW) was used to reduce bias. Linear regression, IPTW-adjusted risk difference (RD), and SurvSHAP(t) were used to interpret the best model.

RESULTS

A total of 5352 elderly breast cancer patients were included. The median (interquartile range) follow-up time was 52 (30-80) months. Among all models, the balanced individual treatment effect for survival data (BITES) performed best. Treatment according to following BITES recommendations was associated with survival benefit, with a multivariate hazard ratio (HR) of 0.78 (95% confidence interval (CI): 0.64-0.94), IPTW-adjusted HR of 0.74 (95% CI: 0.59-0.93), RD of 12.40% (95% CI: 8.01-16.90%), IPTW-adjusted RD of 11.50% (95% CI: 7.16-15.80%), difference in restricted mean survival time (dRMST) of 12.44 (95% CI: 8.28-16.60) months, IPTW-adjusted dRMST of 7.81 (95% CI: 2.93-11.93) months, and p value of the IPTW-adjusted Log-rank test of 0.033. By interpreting BITES, the debiased impact of patient characteristics on adjuvant chemotherapy was quantified, which mainly included breast cancer subtype, tumor size, number of positive lymph nodes, TNM stages, histological grades, and surgical type.

CONCLUSION

Our results emphasize the potential of DL models in guiding adjuvant chemotherapy decisions for elderly breast cancer patients.

Mapping spatial heterogeneity in gastric cancer microenvironment

Gastric cancer (GC) is difficult to characterize due to its heterogeneity, and the complicated heterogeneity leads to the difficulty of precisely targeted therapy. The spatially heterogeneous composition plays a crucial role in GC onset, progression, treatment efficacy, and drug resistance. In recent years, the technological advancements in spatial omics has shifted our understanding of the tumor microenvironment (TME) from cancer-centered model to a dynamic and variant whole. In this review, we concentrated on the spatial heterogeneity within the primary lesions and between the primary and metastatic lesions of GC through the TME heterogeneity including the tertiary lymphoid structures (TLSs), the uniquely spatial organization. Meanwhile, the immune phenotype based on spatial distribution was also outlined. Furthermore, we recapitulated the clinical treatment in mediating spatial heterogeneity in GC, hoping to provide a systematic view of how spatial information could be integrated into anti-cancer immunity.

The value of Demetics ultrasound-assisted diagnosis system in diagnosis of breast lesions and in assessment Ki-67 status of breast cancer

PURPOSE

This study aims to explore the diagnostic efficiency of the Demetics for breast lesions and assessment of Ki-67 status.

MATERIAL

This retrospective study included 291 patients. Three combined methods (method 1: upgraded BI-RADS when Demetics classified the breast lesion as malignant; method 2: downgraded BI-RADS when Demetics classified the breast lesion as benign; method 3: BI-RADS was upgraded or downgraded according to Demetrics' diagnosis) were used to compare the diagnostic efficiency of two radiologists with different seniority before and after using Demetics. The correlation between the visual heatmap by Demetics and the Ki-67 expression level of breast cancer was explored.

RESULTS

The sensitivity, specificity, and area under curve (AUC) of diagnosis by Demetics, junior radiologist and senior radiologist were 89.5%, 83.1%, 0.863; 76.9%, 82.4%, 0.797 and 81.1%, 89.9%, 0.855, respectively. Method 1 was the best for senior radiologist, which increased AUC from 0.855 to 0.884. For junior radiologist, Method 3 was the best method, improving sensitivity (88.8% vs. 76.9%) and specificity (87.2% vs. 82.4%). Demetics paid more attention to the peripheral area of breast cancer with high expression of Ki-67.

CONCLUSION

Demetics has shown good diagnostic efficiency in the assisted diagnosis of breast lesions and is expected to further distinguish Ki-67 status of breast cancer.

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

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

Global and Spatial Metabolomics of Individual Cells Using a Tapered Pneumatically Assisted nano-DESI Probe

Single-cell metabolomics has the potential to reveal unique insights into intracellular mechanisms and biological processes. However, the detection of metabolites from individual cells is challenging due to their versatile chemical properties and concentrations. Here, we demonstrate a tapered probe for pneumatically assisted nanospray desorption electrospray ionization (PA nano-DESI) mass spectrometry that enables both chemical imaging of larger cells and global metabolomics of smaller 15 μm cells. Additionally, by depositing cells in predefined arrays, we show successful metabolomics from three individual INS-1 cells per minute, which enabled the acquisition of data from 479 individual cells. Several cells were used to optimize analytical conditions, and 93 or 97 cells were used to monitor metabolome alterations in INS-1 cells after exposure to a low or high glucose concentration, respectively. Our analytical approach offers insights into cellular heterogeneity and provides valuable information about cellular processes and responses in individual cells.

Identifying prognostic markers in spatially heterogeneous breast cancer microenvironment

To gain deeper insights into the microenvironment of breast cancer, we utilized GeoMx Digital Spatial Profiling (DSP) technology to analyze transcripts from 107 regions of interest in 65 untreated breast cancer tissue samples. Our study revealed spatial heterogeneity in the expression of marker genes in tumor cell enriched, immune cell enriched, and normal epithelial areas. We evaluated a total of 55 prognostic markers in tumor cell enriched regions and 15 in immune cell enriched regions, identifying that tumor cell enriched regions had higher levels of follicular helper T cells, resting dendritic cells, and plasma cells than immune cell enriched regions, while the levels of resting CD4 memory in T cells and regulatory (Treg) T cells were lower. Additionally, we analyzed the heterogeneity of HLA gene families, immunological checkpoints, and metabolic genes in these areas. Through univariate Cox analysis, we identified 5 prognosis-related metabolic genes. Furthermore, we conducted immunostaining experiments, including EMILIN2, SURF4, and LYPLA1, to verify our findings. Our investigation into the spatial heterogeneity of the breast cancer tumor environment has led to the discovery of specific diagnostic and prognostic markers in breast cancer.

Breast cancers as ecosystems: a metabolic perspective

Breast cancer (BC) is the most frequently diagnosed cancer and one of the major causes of cancer death. Despite enormous progress in its management, both from the therapeutic and early diagnosis viewpoints, still around 700,000 patients succumb to the disease each year, worldwide. Late recurrency is the major problem in BC, with many patients developing distant metastases several years after the successful eradication of the primary tumor. This is linked to the phenomenon of metastatic dormancy, a still mysterious trait of the natural history of BC, and of several other types of cancer, by which metastatic cells remain dormant for long periods of time before becoming reactivated to initiate the clinical metastatic disease. In recent years, it has become clear that cancers are best understood if studied as ecosystems in which the impact of non-cancer-cell-autonomous events-dependent on complex interaction between the cancer and its environment, both local and systemic-plays a paramount role, probably as significant as the cell-autonomous alterations occurring in the cancer cell. In adopting this perspective, a metabolic vision of the cancer ecosystem is bound to improve our understanding of the natural history of cancer, across space and time. In BC, many metabolic pathways are coopted into the cancer ecosystem, to serve the anabolic and energy demands of the cancer. Their study is shedding new light on the most critical aspect of BC management, of metastatic dissemination, and that of the related phenomenon of dormancy and fostering the application of the knowledge to the development of metabolic therapies.

Unfolded protein response signature unveils novel insights into breast cancer prognosis and tumor microenvironment

BACKGROUND

The critical role of the unfolded protein response (UPR) in tumorigenesis is widely acknowledged, yet the precise molecular mechanisms underlying its contribution to breast cancer (BC) have not been fully elucidated. The present study aimed to comprehensively explore the expression characteristics and prognostic significance of UPR-related genes in breast cancer METHODS: The transcriptome and clinical data of breast cancer were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively. Differential expression analysis was conducted on UPR-related genes, and the resulting genes were employed for consensus clustering analysis. A breast cancer prognosis risk model was constructed using univariate, least absolute shrinkage and selection operator (LASSO), and multivariable Cox regression analyses. Difference in survival outcomes between groups were analyzed Kaplan-Meier survival analysis, and receiver operating characteristic (ROC) curve was used to assess predictive performance. The relationship between the risk model and clinical-pathological characteristics, immune infiltration, immunotherapy response, and drug sensitivity was assessed.

RESULTS

Differential expression analysis identified 10 UPR-related genes that were differentially expressed in breast cancer. Using the expression matrix of these genes, two molecular subtypes of breast cancer were characterized, which displayed significant differences in prognostic and immune infiltration characteristics. Drawing from the gene expression profiles that distinguish between the molecular subtypes, a prognostic risk scoring model comprising eight genes was developed. This model stratified BC patients from both the training and validation cohorts into high-risk and low-risk groups. Patients in the low-risk group had better prognoses, while those with advanced clinical stage and T stage exhibited higher risk scores. The high- and low-risk groups exhibited notable disparities in immune cell infiltration and the expression of multiple immune checkpoint-related genes. Additionally, the low-risk group demonstrated elevated immunophenoscore, Merck18, CD274, and CAF scores compared to the high-risk group, along with a lesser sensitivity to chemotherapy drugs. These results suggest that patients within the low-risk group may potentially benefit more from immunotherapy and chemotherapy interventions.

CONCLUSIONS

This study developed a novel UPR-derived risk signature, which could robustly predict the survival outcome, immune microenvironment, and chemotherapy response of patients with breast cancer.

Chitosan-Based Nano Systems for Natural Antioxidants in Breast Cancer Therapy

Breast cancer is a major cause of death globally, accounting for around 13% of all deaths. Chemotherapy, the common treatment for cancer, can have side effects that lead to the production of reactive oxygen species (ROS) and an increase in oxidative stress in the body. Antioxidants are important for maintaining the health of cells and helping the immune system function properly. They play a crucial role in balancing the body's internal environment. Using natural antioxidants is an alternative to mitigate the harmful effects of oxidative stress. However, around 80% of natural antioxidants have limited effectiveness when taken orally because they do not dissolve well in water or other solvents. This poor solubility affects their ability to be absorbed by the body and limits their bioavailability. One strategy that has been considered is to increase their water solubility to increase their oral bioavailability. Chitosan-based nanoparticle (CSNP) systems have been extensively explored due to their reliability and simpler synthesis routes. This review focuses on the various methods of chitosan-based nanoformulation for developing effective oral dosage forms for natural antioxidants based on the pharmacokinetics and pharmacodynamics properties. Chitosan (CS) could be a model, because of its wide use in polymeric NPs research, thus providing a better understanding of the role of vehicles that carry natural antioxidants in maintaining the stability and enhancing the performance of cancer drugs.

Quantification of subtype purity in Luminal A breast cancer predicts clinical characteristics and survival

PURPOSE

PAM50 profiling assigns each breast cancer to a single intrinsic subtype based on a bulk tissue sample. However, individual cancers may show evidence of admixture with an alternate subtype that could affect prognosis and treatment response. We developed a method to model subtype admixture using whole transcriptome data and associated it with tumor, molecular, and survival characteristics for Luminal A (LumA) samples.

METHODS

We combined TCGA and METABRIC cohorts and obtained transcriptome, molecular, and clinical data, which yielded 11,379 gene transcripts in common and 1,178 cases assigned to LumA. We used semi-supervised non-negative matrix factorization (ssNMF) to compute the subtype admixture proportions of the four major subtypes-pLumA, pLumB, pHER2, and pBasal-for each case and measured associations with tumor characteristics, molecular features, and survival.

RESULTS

Luminal A cases in the lowest versus highest quartile for pLumA transcriptomic proportion had a 27% higher prevalence of stage > 1, nearly a threefold higher prevalence of TP53 mutation, and a hazard ratio of 2.08 for overall mortality. We found positive associations between pHER2 and HER2 positivity by IHC or FISH; between pLumB and PR negativity; and between pBasal and younger age, node positivity, TP53 mutation, and EGFR expression. Predominant basal admixture, in contrast to predominant LumB or HER2 admixture, was not associated with shorter survival.

CONCLUSION

Bulk sampling for genomic analyses provides an opportunity to expose intratumor heterogeneity, as reflected by subtype admixture. Our results elucidate the striking extent of diversity among LumA cancers and suggest that determining the extent and type of admixture holds promise for refining individualized therapy. LumA cancers with a high degree of basal admixture appear to have distinct biological characteristics that warrant further study.

Novel computational biology modeling system can accurately forecast response to neoadjuvant therapy in early breast cancer

BACKGROUND

Generalizable population-based studies are unable to account for individual tumor heterogeneity that contributes to variability in a patient's response to physician-chosen therapy. Although molecular characterization of tumors has advanced precision medicine, in early-stage and locally advanced breast cancer patients, predicting a patient's response to neoadjuvant therapy (NAT) remains a gap in current clinical practice. Here, we perform a study in an independent cohort of early-stage and locally advanced breast cancer patients to forecast tumor response to NAT and assess the stability of a previously validated biophysical simulation platform.

METHODS

A single-blinded study was performed using a retrospective database from a single institution (9/2014-12/2020). Patients included: ≥ 18 years with breast cancer who completed NAT, with pre-treatment dynamic contrast enhanced magnetic resonance imaging. Demographics, chemotherapy, baseline (pre-treatment) MRI and pathologic data were input into the TumorScope Predict (TS) biophysical simulation platform to generate predictions. Primary outcomes included predictions of pathological complete response (pCR) versus residual disease (RD) and final volume for each tumor. For validation, post-NAT predicted pCR and tumor volumes were compared to actual pathological assessment and MRI-assessed volumes. Predicted pCR was pre-defined as residual tumor volume ≤ 0.01 cm³ (≥ 99.9% reduction).

RESULTS

The cohort consisted of eighty patients; 36 Caucasian and 40 African American. Most tumors were high-grade (54.4% grade 3) invasive ductal carcinomas (90.0%). Receptor subtypes included hormone receptor positive (HR+)/human epidermal growth factor receptor 2 positive (HER2+, 30%), HR+/HER2- (35%), HR-/HER2+ (12.5%) and triple negative breast cancer (TNBC, 22.5%). Simulated tumor volume was significantly correlated with post-treatment radiographic MRI calculated volumes (r = 0.53, p = 1.3 × 10^-7, mean absolute error of 6.57%). TS prediction of pCR compared favorably to pathological assessment (pCR: TS n = 28; Path n = 27; RD: TS n = 52; Path n = 53), for an overall accuracy of 91.2% (95% CI: 82.8% - 96.4%; Clopper-Pearson interval). Five-year risk of recurrence demonstrated similar prognostic performance between TS predictions (Hazard ratio (HR): - 1.99; 95% CI [- 3.96, - 0.02]; p = 0.043) and clinically assessed pCR (HR: - 1.76; 95% CI [- 3.75, 0.23]; p = 0.054).

CONCLUSION

We demonstrated TS ability to simulate and model tumor in vivo conditions in silico and forecast volume response to NAT across breast tumor subtypes.

Obesity as a Risk Factor for Breast Cancer-The Role of miRNA

Breast cancer (BC) is the most common cancer diagnosed among women in the world, with an ever-increasing incidence rate. Due to the dynamic increase in the occurrence of risk factors, including obesity and related metabolic disorders, the search for new regulatory mechanisms is necessary. This will help a complete understanding of the pathogenesis of breast cancer. The review presents the mechanisms of obesity as a factor that increases the risk of developing breast cancer and that even initiates the cancer process in the female population. The mechanisms presented in the paper relate to the inflammatory process resulting from current or progressive obesity leading to cell metabolism disorders and disturbed hormonal metabolism. All these processes are widely regulated by the action of microRNAs (miRNAs), which may constitute potential biomarkers influencing the pathogenesis of breast cancer and may be a promising target of anti-cancer therapies.

Identification of cuproptosis-related patterns and construction of a scoring system for predicting prognosis, tumor microenvironment-infiltration characteristics, and immunotherapy efficacy in breast cancer

Background

Cuproptosis, a recently discovered refreshing form of cell death, is distinct from other known mechanisms. As copper participates in cell death, the induction of cancer cell death with copper ionophores may emerge as a new avenue for cancer treatment. However, the role of cuproptosis in tumor microenvironment (TME) cell infiltration remains unknown.

Methods

We systematically evaluated the cuproptosis patterns in The Cancer Genome Atlas (TCGA) database in breast cancer (BRCA) samples based on 10 cuproptosis-related genes (CRGs), and correlated these patterns with the prognosis and characteristics of TME cell infiltration. A principal component analysis algorithm was used to construct a cuproptosis score to quantify the cuproptosis pattern in individual tumors. Further, the relationships between the cuproptosis score and transcription background, clinical features, characteristics of TME cell infiltration, drug response, and efficacy of immunotherapy were assessed.

Results

Two distinct cuproptosis patterns with distinct prognoses were identified; their TME characteristics were found to be consistent with the immune-excluded and immune-inflamed phenotypes, respectively. The cuproptosis patterns in individual patients were evaluated using the cuproptosis score based on the cuproptosis phenotype-related genes, contributing to distinguishing biological processes, clinical outcome, immune cell infiltration, genetic variation, and drug response. Univariate and multivariate Cox regression analyses verified this score as an independent prognostic predictor in BRCA. A high cuproptosis score, characterized by immune activation, suggests an inflamed tumor and immune-inflamed phenotype with poor survival and a low cuproptosis score, characterized by immune suppression, indicates a non-inflamed tumor and immune-excluded phenotype with better survival. Significant differences were observed in the IC50 between the high and low cuproptosis score groups receiving chemotherapy and targeted therapy drugs. In the two immunotherapy cohorts, patients with a higher cuproptosis score experienced considerable therapeutic advantages and clinical benefits.

Conclusions

This study is the first to elucidate the prominent role of cuproptosis in the clinical outcome and the formation of TME diversity and complexity in BRCA. Estimating cuproptosis patterns in tumors could help predict the prognosis and characteristics of TME cell infiltration and guide more effective chemotherapeutic and immunotherapeutic strategies.

Predictive Biomarkers of Response to Neoadjuvant Chemotherapy in Breast Cancer: Current and Future Perspectives for Precision Medicine

Pathological complete response (pCR) after neoadjuvant chemotherapy in patients with early breast cancer is correlated with better survival. Meanwhile, an expanding arsenal of post-neoadjuvant treatment strategies have proven beneficial in the absence of pCR, leading to an increased use of neoadjuvant systemic therapy in patients with early breast cancer and the search for predictive biomarkers of response. The better prediction of response to neoadjuvant chemotherapy could enable the escalation or de-escalation of neoadjuvant treatment strategies, with the ultimate goal of improving the clinical management of early breast cancer. Clinico-pathological prognostic factors are currently used to estimate the potential benefit of neoadjuvant systemic treatment but are not accurate enough to allow for personalized response prediction. Other factors have recently been proposed but are not yet implementable in daily clinical practice or remain of limited utility due to the intertumoral heterogeneity of breast cancer. In this review, we describe the current knowledge about predictive factors for response to neoadjuvant chemotherapy in breast cancer patients and highlight the future perspectives that could lead to the better prediction of response, focusing on the current biomarkers used for clinical decision making and the different gene signatures that have recently been proposed for patient stratification and the prediction of response to therapies. We also discuss the intratumoral phenotypic heterogeneity in breast cancers as well as the emerging techniques and relevant pre-clinical models that could integrate this biological factor currently limiting the reliable prediction of response to neoadjuvant systemic therapy.

High AUF1 level in stromal fibroblasts promotes carcinogenesis and chemoresistance and predicts unfavorable prognosis among locally advanced breast cancer patients

Background

Locally advanced breast cancer (LABC), the most aggressive form of the disease, is a serious threat for women's health worldwide. The AU-rich RNA-binding factor 1 (AUF1) promotes the formation of chemo-resistant breast cancer stem cells. Thereby, we investigated the power of AUF1 expression, in both cancer cells and their stromal fibroblasts, as predictive biomarker for LABC patients’ clinical outcome following neoadjuvant treatment.

Methods

We have used immunohistochemistry to assess the level of AUF1 on formalin-fixed paraffin-embedded tissues. Immunoblotting was utilized to show the effect of AUF1 ectopic expression in breast stromal fibroblasts on the expression of various genes both in vitro and in orthotopic tumor xenografts. Cytotoxicity was evaluated using the WST1 assay, while a label-free real-time setting using the xCELLigence RTCA technology was utilized to assess the proliferative, migratory and invasive abilities of cells.

Results

We have shown that high AUF1 immunostaining (≥ 10%) in both cancer cells and their adjacent cancer-associated fibroblasts (CAFs) was significantly associated with higher tumor grade. Kaplan–Meier univariate analysis revealed a strong correlation between high AUF1 level in CAFs and poor patient’s survival. This correlation was highly significant in patients with triple negative breast cancer, who showed poor disease-free survival (DFS) and overall survival (OS). High expression of AUF1 in CAFs was also associated with poor OS of ER+/Her2− patients. Similarly, AUF1-positive malignant cells tended to be associated with shorter DFS and OS of ER+/Her2+ patients. Interestingly, neoadjuvant therapy downregulated AUF1 to a level lower than 10% in malignant cells in a significant number of patients, which improved both DFS and OS. In addition, ectopic expression of AUF1 in breast fibroblasts activated these cells and enhanced their capacity to promote, in an IL-6-dependent manner, the epithelial-to-mesenchymal transition and stemness processes. Furthermore, these AUF1-expressing cells enhanced the chemoresistance of breast cancer cells and their growth in orthotopic tumor xenografts.

Conclusions

The present findings show that the CAF-activating factor AUF1 has prognostic/predictive value for breast cancer patients and could represent a great therapeutic target in order to improve the precision of cancer treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13058-022-01543-x.

A Novel YTHDF3-Based Model to Predict Prognosis and Therapeutic Response in Breast Cancer

Background: Due to high tumor heterogeneity, breast cancer (BC) patients still suffer poor survival outcomes. YTHDF3 plays a critical role in the prognosis of BC patients. Hence, we aimed to construct a YTHDF3-based model for the prediction of the overall survival (OS) and the sensitivity of therapeutic agents in BC patients.

Methods: Based on The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) database, we obtained BC patients’ data (n = 999) with YTHDF3 expression profiles. The association between YTHDF3 expression and 5-year OS was determined via Cox proportional hazards regression (CPHR) analysis. By integrating the variables, we established a prognostic nomogram. The model was estimated via discrimination, calibration ability, and decision curve analysis (DCA). The performance of the model was compared with the TNM stage system through receiver operating characteristic (ROC) curves and DCA. By means of the Genomics of Drug Sensitivity in Cancer (GDSC) database (https://www.cancerrxgene.org/), the therapeutic agents’ response was estimated. Gene set enrichment analysis (GSEA) demonstrated possible biological mechanisms related to YTHDF3. TIMER and CIBERSORTx were employed to analyze the association between YTHDF3 and tumor-infiltrating immune cells.

Results: The high YTHDF3 expression was significantly correlated with poor 5-year OS in BC patients. Through multivariate CPHR, four independent prognostic variables (age, TNM stage, YTHDF3 expression, and molecular subtype) were determined. On the basis of the four factors, a YTHDF3-based nomogram was built. The area under the curve (AUC) of the ROC curve for the model surpassed that of the TNM stage system (0.72 vs. 0.63, p = 0.00028). The model predictions showed close consistency with the actual observations via the calibration plot. Therapeutic response prediction was conducted in high- and low-risk groups and compared with each other. The BC patients with higher risk scores showed more therapeutic resistance than those with a lower risk score.

Conclusion: YTHDF3 was verified as a prognostic biomarker of BC, and a novel YTHDF3-based model was constructed to predict the 5-year OS of BC patients. Our model could be applied to effectively predict the therapeutic response of commonly used agents for BC patients.

Pyroptosis-related molecular classification and immune microenvironment infiltration in breast cancer: A novel therapeutic target

The underlying role of pyroptosis in breast cancer (BC) remains unknown. Herein, we investigated the correlations of 33 pyroptosis‐related genes (PRGs) with immune checkpoints and immune cell infiltrations in BC patients based on The Cancer Genome Atlas cohort (n = 996) and Gene Expression Omnibus cohort (n = 3,262). Enrichment analysis revealed that these PRGs mainly functioned in pyroptosis, inflammasomes and regulation of autophagy pathway. Four prognostic independent PRGs (CASP9, TIRAP, GSDMC and IL18) were identified. Then, cluster 1/2 was recognized using consensus clustering for these four PRGs. Patients from cluster 1 had a favourable prognosis and diverse immune cell infiltrations. A nomogram was developed based on age, TNM stage, tumour subtype and pyroptosis score. Patients with the high‐risk group exhibited worse 5‐year OS, and the result was consistent in the external cohort. Additionally, high‐risk group patients were associated with downregulated immune checkpoint expression. Further analysis suggested that the high‐risk group patients were associated with a higher IC50 of paclitaxel, doxorubicin, cisplatin, methotrexate and vinorelbine. In summarizing, the pyroptosis score‐based nomogram might serve as an independent prognostic predictor and could guide medication for chemotherapy. Additionally, it may bring novel insight into the regulation of tumour immune microenvironment in BC and help to achieve precision immunotherapy.