Stratification Of Breast Cancer Patients Research Articles

Development and Validation of a Prognostic Nomogram for Breast Cancer Patients With Multi-Organ Metastases: An Analysis of the Surveillance, Epidemiology, and End Results Program Database.

Multi-organ metastases represent a substantial life-threatening risk for breast cancer (BC) patients. Nonetheless, the current dearth of assessment tools for patients with multi-organ metastatic BC adversely impacts their evaluation. We conducted a retrospective analysis of BC patients with multi-organ metastases using data from the SEER database from 2010 to 2019. The patients were randomly allocated into a training cohort and a validation cohort in a 7:3 ratio. Univariate COX regression analysis, the LASSO, and multivariate Cox regression analyses were performed to identify independent prognostic factors in the training set. Based on these factors, a nomogram was constructed to estimate overall survival (OS) probability for BC patients with multi-organ metastases. The performance of the nomogram was evaluated using C-indexes, ROC curves, calibration curves, decision curve analysis (DCA) curves, and the risk classification system for validation. A total of 3626 BC patients with multi-organ metastases were included in the study, with 2538 patients in the training cohort and 1088 patients in the validation cohort. Age, grade, metastasis location, surgery, chemotherapy, and subtype were identified as significant independent prognostic factors for OS in BC patients with multi-organ metastases. A nomogram for predicting 1-year, 3-year, and 5-year OS was constructed. The evaluation metrics, including C-indexes, ROC curves, calibration curves, and DCA curves, demonstrated the excellent predictive performance of the nomogram. Additionally, the risk grouping system effectively stratified BC patients with multi-organ metastases into distinct prognostic categories. The developed nomogram showed high accuracy in predicting the survival probability of BC patients with multi-organ metastases, providing valuable information for patient counseling and treatment decision making.

Abstract PO5-01-09: Exploring cellular heterogeneity of localised breast cancers

Abstract Breast cancer is a heterogeneous disease at multiple levels, ranging from subtype differences between patients (inter-patient heterogeneity) to the diverse composition of malignant cells, heterogeneity of hormone receptor (HR) expression and cellular makeup within single breast cancer samples (intra-tumour heterogeneity). Despite an increase in the number of effective therapies available, many patients will experience an incomplete treatment response and subsequent relapse. These adverse treatment outcomes may be attributed to the often overlooked but critical factor of cellular heterogeneity. In order to gain deeper insights into intra-tumour heterogeneity, we have applied single-cell technologies on a cohort comprising 250 primary, untreated breast cancers. We have optimized methods for tissue cryopreservation, eliminating the need for fresh sample processing, and multiplex tissue profiling. Together, these are cost-efficient processes that allow for improved handling of small tissue sizes, such as biopsies, and reduce batch effect. To ensure accurate and reliable data processing, we have developed a scalable computational workflow that includes benchmarked methods for sample SNP-demultiplexing, doublet detection, high-resolution cell annotation and cellular integration. Finally, we are extending our existing methods1 to study the cellular heterogeneity of breast cancers. Our method, scSubtyper, explores the phenotypic differences between malignant cells within tumours, by comparing each single cell to distinct features associated with different molecular subtypes and assigning each cell to one of these subtypes. Our previous study1 and preliminary results of this project revealed that over 90% of the samples exhibit a mix of malignant cells of different subtypes, and 50% of samples contain cells that have characteristics of all subtypes, demonstrating cellular heterogeneity exists not only exists between malignant cells, but also within malignant cells of a tumour. Our second approach, known as ecotyping, assesses patterns of cell type frequencies across samples and groups them based on similarity of these co-occurences. Our preliminary results have revealed the existence of 5 ecotypes that lack significant associations with samples clinical subtypes. Applying the same approach exclusively within the HR-positive samples identified 4 ecotypes characterized by distinct abundances of immune and stromal cells. This analysis revealed that ecotypes are not a simple surrogate for clinical and molecular subtypes, but their presence could drive different response to treatment. Together, our high-throughput tissue processing and computational approaches to studying intra-tumour heterogeneity are now being applied to our large, well annotated, clinical cohort. Supported by the preliminary results, we hypothesize that this study will play a vital role in optimizing breast cancer patient stratification to improve treatment management and outcome. 1. Wu, Sunny Z., et al. "A single-cell and spatially resolved atlas of human breast cancers." Nature genetics 53.9 (2021): 1334-1347. Citation Format: Beata Kiedik, Daniel Roden, Kate Harvey, Ghamdan Al-Eryani, Sunny Wu, Mun Hui, Sandra O'Toole, Elgene Lim, Charles Perou, Alex Swarbrick. Exploring cellular heterogeneity of localised breast cancers [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-01-09.

Precision unveiled: Synergistic genomic landscapes in breast cancer-Integrating single-cell analysis and decoding drug toxicity for elite prognostication and tailored therapeutics.

Globally, breast cancer, with diverse subtypes and prognoses, necessitates tailored therapies for enhanced survival rates. A key focus is glutamine metabolism, governed by select genes. This study explored genes associated with T cells and linked them to glutamine metabolism to construct a prognostic staging index for breast cancer patients for more precise medical treatment. Two frameworks, T-cell related genes (TRG) and glutamine metabolism (GM), stratified breast cancer patients. TRG analysis identified key genes via hdWGCNA and machine learning. T-cell communication and spatial transcriptomics emphasized TRG's clinical value. GM was defined using Cox analyses and the Lasso algorithm. Scores categorized patients as TRG_high+GM_high (HH), TRG_high+GM_low (HL), TRG_low+GM_high (LH), or TRG_low+GM_low (LL). Similarities between HL and LH birthed a "Mixed" class and the TRG_GM classifier. This classifier illuminated gene variations, immune profiles, mutations, and drug responses. Utilizing a composite of two distinct criteria, we devised a typification index termed TRG_GM classifier, which exhibited robust prognostic potential for breast cancer patients. Our analysis elucidated distinct immunological attributes across the classifiers. Moreover, by scrutinizing the genetic variations across groups, we illuminated their unique genetic profiles. Insights into drug sensitivity further underscored avenues for tailored therapeutic interventions. Utilizing TRG and GM, a robust TRG_GM classifier was developed, integrating clinical indicators to create an accurate predictive diagnostic map. Analysis of enrichment disparities, immune responses, and mutation patterns across different subtypes yields crucial subtype-specific characteristics essential for prognostic assessment, clinical decision-making, and personalized therapies. Further exploration is warranted into multiple fusions between metrics to uncover prognostic presentations across various dimensions.

Distinct profiles of proliferating CD8+/TCF1+ T cells and CD163+/PD-L1+ macrophages predict risk of relapse differently among treatment-naïve breast cancer subtypes

Immunophenotypic analysis of breast cancer microenvironment is gaining attraction as a clinical tool improving breast cancer patient stratification. The aim of this study is to evaluate proliferating CD8 + including CD8 + TCF1 + Τ cells along with PD-L1 expressing tissue-associated macrophages among different breast cancer subtypes. A well-characterized cohort of 791 treatment-naïve breast cancer patients was included. The analysis demonstrated a distinct expression pattern among breast cancer subtypes characterized by increased CD8 + , CD163 + and CD163 + PD-L1 + cells along with high PD-L1 status and decreased fraction of CD8 + Ki67 + T cells in triple negative (TNBC) and HER2 + compared to luminal tumors. Kaplan–Meier and Cox univariate survival analysis revealed that breast cancer patients with high CD8 + , CD8 + Ki67 + , CD8 + TCF1 + cells, PD-L1 score and CD163 + PD-L1 + cells are likely to have a prolonged relapse free survival, while patients with high CD163 + cells have a worse prognosis. A differential impact of high CD8 + , CD8 + Ki67 + , CD8 + TCF1 + T cells, CD163 + PD-L1 + macrophages and PD-L1 status on prognosis was identified among the various breast cancer subtypes since only TNBC patients experience an improved prognosis compared to patients with luminal A tumors. Conversely, high infiltration by CD163 + cells is associated with worse prognosis only in patients with luminal A but not in TNBC tumors. Multivariate Cox regression analysis in TNBC patients revealed that increased CD8 + [hazard ratio (HR) = 0.542; 95% confidence interval (CI) 0.309–0.950; p = 0.032), CD8 + TCF1 + (HR = 0.280; 95% CI 0.101–0.779; p = 0.015), CD163 + PD-L1 + (HR: 0.312; 95% CI 0.112–0.870; p = 0.026) cells along with PD-L1 status employing two different scoring methods (HR: 0.362; 95% CI 0.162–0.812; p = 0.014 and HR: 0.395; 95% CI 0.176–0.884; p = 0.024) were independently linked with a lower relapse rate. Multivariate analysis in Luminal type A patients revealed that increased CD163 + was independently associated with a higher relapse rate (HR = 2.360; 95% CI 1.077–5.170; p = 0.032). This study demonstrates that the evaluation of the functional status of CD8 + T cells in combination with the analysis of immunosuppressive elements could provide clinically relevant information in different breast cancer subtypes.

Prognosis prediction and risk stratification of breast cancer patients based on a mitochondria-related gene signature

As the malignancy with the highest global incidence, breast cancer represents a significant threat to women’s health. Recent advances have shed light on the importance of mitochondrial function in cancer, particularly in metabolic reprogramming within tumors. Recognizing this, we developed a novel risk signature based on mitochondrial-related genes to improve prognosis prediction and risk stratification in breast cancer patients. In this study, transcriptome data and clinical features of breast cancer samples were extracted from two sources: the TCGA, serving as the training set, and the METABRIC, used as the independent validation set. We developed the signature using LASSO-Cox regression and assessed its prognostic efficacy via ROC curves. Furthermore, the signature was integrated with clinical features to create a Nomogram model, whose accuracy was validated through clinical calibration curves and decision curve analysis. To further elucidate prognostic variations between high and low-risk groups, we conducted functional enrichment and immune infiltration analyses. Additionally, the study encompassed a comparison of mutation landscapes and drug sensitivity, providing a comprehensive understanding of the differing characteristics in these groups. Conclusively, we established a risk signature comprising 8 mitochondrial-related genes—ACSL1, ALDH2, MTHFD2, MRPL13, TP53AIP1, SLC1A1, ME3, and BCL2A1. This signature was identified as an independent risk predictor for breast cancer patient survival, exhibiting a significant high hazard ratio (HR = 3.028, 95%CI 2.038–4.499, P < 0.001). Patients in the low-risk group showed a more favorable prognosis, with enhanced immune infiltration, distinct mutation landscapes, and greater sensitivity to anti-tumor drugs. In contrast, the high-risk group exhibited an adverse trend in these aspects. This risk signature represents a novel and effective prognostic indicator, suggesting valuable insights for patient stratification in breast cancer.

Abstract B055: Metabolic stratification of human breast tumors reveal subtypes of clinical and therapeutic relevance

Abstract Metabolic heterogeneity in breast cancer has limited the deployment of metabolic therapies. To enable patient stratification, we studied global metabolic landscape of breast cancer (N&amp;gt;3000 combined) and identified three metabolic subtypes with increasing degrees of metabolic deregulation. M1 exhibited deregulated bile acid biosynthesis whereas M2 showed reliance on methionine pathway and M3 was found to be the most metabolically aggressive engaging fatty, nucleotide, and glucose metabolism. Extent of metabolic deregulation correlated strongly with tumor aggressiveness and patient outcome Moreover bile acid biosynthesis predicted less aggressive tumors with better prognosis while pyrimidine metabolism predicted the opposite trend. Notably this pattern was reproducible in independent datasets and in vivo tumor metabolite data. Genomic characterization of metabolic subtypes revealed TP53 mutation and MYC amplification as the strongest predictor of metabolic deregulation. Using machine learning, we identified robust and generalizable signatures that recaptured metabolic subtypes in tumors and cell lines. Surprisingly, the least deregulated M1 subtype was not found to be represented by any of the 50 cell line models analyzed. Experimental inhibition of metabolic pathways demonstrated subtype specific sensitivity and improved combinatorial efficacy of chemotherapeutic drugs. In addition, breast cancer cell lines representing metabolic subtypes revealed therapeutically relevant drugs. Taken together, this work presents metabolic stratification of breast cancer patients that may have implications in predicting patient outcome and designing precision therapies. Citation Format: Mohammad A Iqbal, Shumaila Siddiqui, Kirk Smith, Prithvi Singh, Bhupender Kumar, Salem Chouaib, Sriram Chandrasekaran. Metabolic stratification of human breast tumors reveal subtypes of clinical and therapeutic relevance [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr B055.

Identification and validation of Golgi apparatus-related signature for predicting prognosis and immunotherapy response in breast cancer

BackgroundThe Golgi apparatus plays a pivotal role in various aspects of cancer. This study aims to investigate the predictive value of Golgi apparatus-related genes (GARGs) in breast cancer prognosis and immunotherapy response evaluation.MethodsTranscriptional and clinical data from the TCGA-BRCA cohort and GSE96058 cohort were utilized to construct and validate a prognostic model for breast cancer using Cox regression analysis. Differences in immune landscape, somatic mutations, gene expression, drug sensitivity, and immunotherapy response between different risk groups were assessed. A prognostic nomogram for breast cancer was further developed and evaluated. qPCR and single-cell sequencing analyses were performed to validate the expression of GARGs.ResultsA total of 394 GARGs significantly associated with breast cancer prognosis were identified, leading to the construction of a prognostic risk feature comprising 10 GARGs. This feature effectively stratified breast cancer patients into high-risk and low-risk groups, with the high-risk group exhibiting significantly worse prognosis. Meanwhile, significant differences in clinicopathological features, immune infiltration, drug sensitivity, and immunotherapy response were observed between the high- and low-risk groups. The constructed nomogram incorporating these factors showed superior performance in prognostic assessment for breast cancer patients. Ultimately, the utilization of qPCR and single-cell sequencing techniques substantiated the disparate expression patterns of these prognostic genes in breast cancer.ConclusionOur findings demonstrate that a prognostic risk feature derived from GARGs holds promising application potential for predicting prognosis and evaluating immunotherapy response in breast cancer patients.

Ferroptosis heterogeneity within the tumor microenvironment revealed a genetic blueprint of breast cancer.

The tumor microenvironment (TME) significantly influences disease progression through immune infiltration, while ferroptosis, a recently discovered cell death mechanism, plays a crucial role in tumor suppression. However, its role in breast cancer is not clear. In this study, we analyzed bulk RNA and single-cell RNA sequencing data from 1217 samples, including 1104 breast cancer patients and 113 controls, to identify ferroptosis-related genes (FRGs) and construct a prognostic model. Using univariate cox regression, LASSO regression, and multivariate cox regression analysis, we discovered 21 FRGs and 3 TME-related immune cell types with prognostic value. Dimensionality reduction clustering and visualization were performed using the UMAP method, while the immune infiltration process was calculated with the TIP online tool. We employed GSEA enrichment analysis, WGCNA clustering analysis, and correlation analysis to examine functional differences, and the mutation analysis of the best and worst prognosis groups was conducted using the maftools package. Our findings revealed that knocking down the expression of the hub gene SLC39A7 significantly impacted cancer cell apoptosis and combining ferroptosis and TME scores yielded high prognostic power. Epithelial cells and B cells exhibited higher ferroptosis scores, which were independently associated with immune checkpoint blockade (ICB) response and ICB gene expression. This study provides a foundation for further exploration of the relationship between ferroptosis and ICB response in breast cancer. In conclusion, we developed a prognostic model based on ferroptosis and infiltrated immune cells that effectively stratified breast cancer patients and demonstrated the role of SLC39A7 in breast cancer pathogenesis through the regulation of apoptosis.

CORONet: A Cross-Sequence Joint Representation and Hypergraph Convolutional Network for Classifying Molecular Subtypes of Breast Cancer Using Incomplete DCE-MRI.

Breast cancer, the predominant malignancy among women, is characterized by significant heterogeneity, leading to the emergence of distinct molecular subtypes. Accurate differentiation of these molecular subtypes holds paramount clinical significance, owing to substantial variations in prognosis, therapeutic strategies, and survival outcomes. In this study, we propose a cross-sequence joint representation and hypergraph convolution network (CORONet) for classifying molecular subtypes of breast cancer using incomplete DCE-MRI. Specifically, we first build a cross-sequence joint representation (COR) module to integrate image imputation and feature representation into a unified framework, encouraging effective feature extraction for subsequent classification. Then, we fuse multiple COR features and applied feature selection to reduce the redundant information between sequences. Finally, we deploy hypergraph structures to model high-order correlation among different subjects and extracted high-level semantic features by hypergraph convolutions for molecular subtyping. Extensive experiments on incomplete DCE-MRIs of 395 patients from the TCIA repository showed a significant improvement of our CORONet over state of the arts, with the area under the curve (AUC) of 0.891 and 0.903 for luminal and triple-negative (TN) subtype prediction, respectively. Similar advantages of CORONet were also confirmed in partial complete DCE-MRIs of 144 patients, achieving an AUC of 0.858 and 0.832 for predicting luminal and TN subtypes of breast cancer, respectively. Nevertheless, both of these values were lower compared to the scenario where DCE-MRIs from all 395 patients were utilized. Our study contributes to the precise molecular subtyping using incomplete multi-sequence DCE-MRI, thereby offering promising prospects for future risk stratification of breast cancer patients.

Structural deep clustering network for stratification of breast cancer patients through integration of somatic mutation profiles

Background and ObjectiveBreast cancer is among of the most malignant tumor that occurs in women and is one of the leading causes of death from gynecologic malignancy worldwide. The high degree of heterogeneity that characterizes breast cancer makes it challenging to devise effective therapeutic strategies. Accumulating evidence highlights the crucial role of stratifying breast cancer patients into clinically significant subtypes to achieve better prognoses and treatments. The structural deep clustering network is a graph convolutional network-based clustering algorithm that integrates structural information and has achieved state-of-the-art performance in various applications. MethodsIn this study, we employed structural deep clustering network to integrate somatic mutation profiles for stratifying 2526 breast cancer patients from the Memorial Sloan Kettering Cancer Center into two clinically differentiable subtypes. ResultsBreast cancer patients in cluster 1 exhibited better prognosis than breast cancer patients in cluster 2, and the difference between them was statistically significant. The immunogenomic landscape further demonstrated that cluster 1 was associated with remarkable infiltration of the tumor infiltrating lymphocytes. The clustering subtype could be used to evaluate the therapeutic benefit of immunotherapy and chemotherapy in breast cancer patients. Furthermore, our approach effectively classified patients from eight different cancer types, demonstrating its generalizability. ConclusionsOur study represents a step towards a generic methodology for classifying cancer patients using only somatic mutation data and structural deep clustering network approaches. Employing structural deep clustering network to identify breast cancer subtypes is promising and can inform the development of more accurate and personalized therapies.

MYC is a clinically significant driver of mTOR inhibitor resistance in breast cancer.

Targeting the PI3K-AKT-mTOR pathway is a promising therapeutic strategy for breast cancer treatment. However, low response rates and development of resistance to PI3K-AKT-mTOR inhibitors remain major clinical challenges. Here, we show that MYC activation drives resistance to mTOR inhibitors (mTORi) in breast cancer. Multiomic profiling of mouse invasive lobular carcinoma (ILC) tumors revealed recurrent Myc amplifications in tumors that acquired resistance to the mTORi AZD8055. MYC activation was associated with biological processes linked to mTORi response and counteracted mTORi-induced translation inhibition by promoting translation of ribosomal proteins. In vitro and in vivo induction of MYC conferred mTORi resistance in mouse and human breast cancer models. Conversely, AZD8055-resistant ILC cells depended on MYC, as demonstrated by the synergistic effects of mTORi and MYCi combination treatment. Notably, MYC status was significantly associated with poor response to everolimus therapy in metastatic breast cancer patients. Thus, MYC is a clinically relevant driver of mTORi resistance that may stratify breast cancer patients for mTOR-targeted therapies.

Development and validation of Ran as a prognostic marker in stage I and stage II primary breast cancer

PurposeExisting prognostic biomarkers are inadequate for stratifying breast cancer patients with the highest risk of tumor progression at the time of diagnosis. Here, we demonstrate that the small GTPase Ran has predictive value for breast cancer (BC) patients as a whole, and for specific BC subtypes. Patients and methodsRan expression was quantified by immunohistochemistry in 263 patients with primary breast cancer diagnosed at the Breast Unit, Royal Liverpool Hospital. Additionally as an independent validation, we also analyzed the mRNA expressions of Ran, ER, PR, and Cerb-2, the triple-negative endocrine receptors, and their associations with patient survival in a combined patient cohorts of multiple public datasets (n = 1079). We analyzed the data with Spearman's rank correlation and Kaplan-Meier plots coupled with Wilcoxon-Gehan tests, respectively. All statistical tests were two-sided. ResultsRan nuclear, cytoplasmic, and total staining are substantially associated with poor survival, independent of conventional prognostic markers such as estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), and lymph node status. According to the datasets, Ran was significantly correlated with distant metastasis-free survival (DMFS) and relapse-free survival (RFS). ConclusionWe found that Ran expression is a unique predictive biomarker for patient survival, metastasis, and tumor relapse. This biomarker could be used for diagnostic purposes, using formalin-fixed, paraffin-embedded tumor biopsy samples from breast cancer patients in the early stages.

Stratifying breast cancer patients by baseline risk of cardiotoxic complications linked to chemotherapy

A majority of modern antitumor pharmaceuticals are accompanied by cardiotoxicity. The &lt;b&gt;study aims&lt;/b&gt; to present practical approaches to stratifying the baseline risk of antitumor therapies' cardiotoxicity in patients newly diagnosed with breast cancer started on anthracyclines and/or trastuzumab.&lt;br /&gt; &lt;b&gt;Material and methods: &lt;/b&gt;Stratifying the risks of antitumor therapy was carried out according to the European Society of Cardiology recommendations. A therapist examined all patients for existing cardiovascular diseases and risk factors detection. The blood levels of glucose, cholesterol, creatinine, cardiac troponin I, and brain natriuretic peptide were determined. Electrocardiography and echocardiography with an assessment of the myocardium global longitudinal strain were performed.&lt;br /&gt; &lt;b&gt;Results: &lt;/b&gt;In total, 128 breast cancer patients with a mean age of 54.3±11.0 years were included in the study. Of them, 84.4% had tumor stages I and II, and 21.1% were HER2-positive. Chronic heart failure and ischemic heart disease were detected in 2.3%.&amp;nbsp; Among the risk factors, the most common were arterial hypertension (51.6%), obesity (29.7%), age 65-70 years (18%), significant smoking history (15.6%), and diabetes mellitus (11.7%). Elevated cardiac troponin I and brain natriuretic peptide baseline levels were present in 5.5% and 7.0% of patients, respectively. To a high-risk group for cardiotoxic complications, 7.8% were allocated, 35.7% were assigned to the moderate-risk group, and 54.7% had low risk. High/very high and moderate-risk patients (43.5%) were referred to a cardiologist. Cardioprotective treatment was started immediately in high/very high-risk patients.&lt;br /&gt; &lt;b&gt;Conclusion&lt;/b&gt;: All revealed differences between the cardiovascular risk groups were related to age, cardiovascular system condition, and the severity of comorbid pathologies. The baseline stratification of patients into risk groups is a crucial step in preventing the cardiotoxicity of anticancer therapy. Comprehensive assessing the patient's condition before and during chemotherapy allows for avoiding the development of fatal cardiovascular complications in at-risk patients.

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

BackgroundThe 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 MethodsThe 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. ResultsDifferential 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. ConclusionsThis 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.

High-throughput proteomics of breast cancer subtypes: Biological characterization and multiple candidate biomarker panels to patients' stratification

Background and aimsThe actual classification of breast tumors in subtypes represents an attempt to stratify patients into clinically cohesive groups, nevertheless, clinicians still lack reproducible and reliable protein biomarkers for breast cancer subtype discrimination. In this study, we aimed to access the differentially expressed proteins between these tumors and its biological implications, contributing to the subtype's biological and clinical characterization, and with protein panels for subtype discrimination. MethodsIn our study, we applied high-throughput mass spectrometry, bioinformatic, and machine learning approaches to investigate the proteome of different breast cancer subtypes. ResultsWe identified that each subtype depends on different protein expression patterns to sustain its malignancy, and also alterations in pathways and processes that can be associated with each subtype and its biological and clinical behaviors. Regarding subtype biomarkers, our panels achieved performances with at least 75% of sensibility and 92% of specificity. In the validation cohort, the panels obtained acceptable to outstanding performances (AUC = 0.740 to 1.00). ConclusionsIn general, our results expand the accuracy of breast cancer subtypes' proteomic landscape and improve the understanding of its biological heterogeneity. In addition, we identified potential protein biomarkers for the stratification of breast cancer patients, improving the repertoire of reliable protein biomarkers. SignificanceBreast cancer is the most diagnosed cancer type worldwide and the most lethal cancer in women. As a heterogeneous disease, breast cancer tumors can be classified into four major subtypes, each presenting particular molecular alterations, clinical behaviors, and treatment responses. Thus, a pivotal step in patient management and clinical decisions is accurately classifying breast tumor subtypes. Currently, this classification is made by the immunohistochemical detection of four classical markers (estrogen receptor, progesterone receptor, HER2 receptor, and the Ki-67 index); however, it is known that these markers alone do not fully discriminate the breast tumor subtypes. Also, the poor understanding of the molecular alterations of each subtype leads to a challenging decision-making process regarding treatment choice and prognostic determination. This study, through high-throughput label-free mass-spectrometry data acquisition and downstream bioinformatic analysis, advances in the proteomic discrimination of breast tumors and achieves an in-depth characterization of the subtype's proteomes. Here, we indicate how the variations in the subtype's proteome can influence the tumor's biological and clinical differences, highlighting the variation in the expression pattern of oncoproteins and tumor suppressor proteins between subtypes. Also, through our machine-learning approach, we propose multi-protein panels with the potential to discriminate the breast cancer subtypes. Our panels achieved high classification performance in our cohort and in the independent validation cohort, demonstrating their potential to improve the current tumor discrimination system as complements to the classical immunohistochemical classification.

Hazard-based risk grouping effectively stratifying breast cancer patients in post-irradiation long-term heart diseases: a population-based cohort study.

Even though advanced radiotherapy techniques provide a better protective effect on surrounding normal tissues, the late sequelae from radiation exposure to the heart are still considerable in breast cancer patients. The present population-based study explored the role of cox-regression-based hazard risk grouping and intended to stratify patients with post-irradiation long-term heart diseases. The present study investigated the Taiwan National Health Insurance (TNHI) database. From 2000 to 2017, we identified 158,798 breast cancer patients. Using a propensity score match of 1:1, we included 21,123 patients in each left and right breast irradiation cohort. Heart diseases, including heart failure (HF), ischemic heart disease (IHD), and other heart diseases (OHD), and anticancer agents, including epirubicin, doxorubicin, and trastuzumab, were included for analysis. Patients received left breast irradiation demonstrated increased risks on IHD (aHR, 1.16; 95% CI, 1.06-1.26; p < 0.01) and OHD (aHR, 1.08; 95% CI, 1.01-1.15; p < 0.05), but not HF (aHR, 1.11; 95% CI, 0.96-1.28; p = 0.14), when compared with patients received right breast irradiation. In patients who received left breast irradiation dose of >6,040 cGy, subsequent epirubicin might have a trend to increase the risk of heart failure (aHR, 1.53; 95% CI, 0.98-2.39; p = 0.058), while doxorubicin (aHR, 0.59; 95% CI, 0.26-1.32; p = 0.19) and trastuzumab (aHR, 0.93; 95% CI, 0.33-2.62; p = 0.89) did not. Older age was the highest independent risk factor for post-irradiation long-term heart diseases. Generally, systemic anticancer agents are safe in conjunction with radiotherapy for managing post-operative breast cancer patients. Hazard-based risk grouping may help stratify breast cancer patients associated with post-irradiation long-term heart diseases. Notably, radiotherapy should be performed cautiously for elderly left breast cancer patients who received epirubicin. Limited irradiation dose to the heart should be critically considered. Regular monitoring of potential signs of heart failure may be conducted.

BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images.

Breast cancer is the most common malignancy in women, with over 40,000 deaths annually in the United States alone. Clinicians often rely on the breast cancer recurrence score, Oncotype DX (ODX), for risk stratification of breast cancer patients, by using ODX as a guide for personalized therapy. However, ODX and similar gene assays are expensive, time-consuming, and tissue destructive. Therefore, developing an AI-based ODX prediction model that identifies patients who will benefit from chemotherapy in the same way that ODX does would give a low-cost alternative to the genomic test. To overcome this problem, we developed a deep learning framework, Breast Cancer Recurrence Network (BCR-Net), which automatically predicts ODX recurrence risk from histopathology slides. Our proposed framework has two steps. First, it intelligently samples discriminative features from whole-slide histopathology images of breast cancer patients. Then, it automatically weights all features through a multiple instance learning model to predict the recurrence score at the slide level. On a dataset of H&E and Ki67 breast cancer resection whole slides images (WSIs) from 99 anonymized patients, the proposed framework achieved an overall AUC of 0.775 (68.9% and 71.1% accuracies for low and high risk) on H&E WSIs and overall AUC of 0.811 (80.8% and 79.2% accuracies for low and high risk) on Ki67 WSIs of breast cancer patients. Our findings provide strong evidence for automatically risk-stratify patients with a high degree of confidence. Our experiments reveal that the BCR-Net outperforms the state-of-the-art WSI classification models. Moreover, BCR-Net is highly efficient with low computational needs, making it practical to deploy in limited computational settings.

Establishment and validation of a multigene model to predict the risk of relapse in hormone receptor-positive early-stage Chinese breast cancer patients.

Breast cancer patients who are positive for hormone receptor typically exhibit a favorable prognosis. It is controversial whether chemotherapy is necessary for them after surgery. Our study aimed to establish a multigene model to predict the relapse of hormone receptor-positive early-stage Chinese breast cancer after surgery and direct individualized application of chemotherapy in breast cancer patients after surgery. In this study, differentially expressed genes (DEGs) were identified between relapse and nonrelapse breast cancer groups based on RNA sequencing. Gene set enrichment analysis (GSEA) was performed to identify potential relapse-relevant pathways. CIBERSORT and Microenvironment Cell Populations-counter algorithms were used to analyze immune infiltration. The least absolute shrinkage and selection operator (LASSO) regression, log-rank tests, and multiple Cox regression were performed to identify prognostic signatures. A predictive model was developed and validated based on Kaplan-Meier analysis, receiver operating characteristic curve (ROC). A total of 234 out of 487 patients were enrolled in this study, and 1588 DEGs were identified between the relapse and nonrelapse groups. GSEA results showed that immune-related pathways were enriched in the nonrelapse group, whereas cell cycle- and metabolism-relevant pathways were enriched in the relapse group. A predictive model was developed using three genes ( CKMT1B , SMR3B , and OR11M1P ) generated from the LASSO regression. The model stratified breast cancer patients into high- and low-risk subgroups with significantly different prognostic statuses, and our model was independent of other clinical factors. Time-dependent ROC showed high predictive performance of the model. A multigene model was established from RNA-sequencing data to direct risk classification and predict relapse of hormone receptor-positive breast cancer in Chinese patients. Utilization of the model could provide individualized evaluation of chemotherapy after surgery for breast cancer patients.

Construction of a DNA damage repair gene signature for predicting prognosis and immune response in breast cancer.

DNA damage repair (DDR) genes are involved in developing breast cancer. Recently, a targeted therapeutic strategy through DNA repair machinery, including PARPi, has initially shown broad development and application prospects in breast cancer therapy. However, few studies that focused on the correlation between the expression level of DNA repair genes, prognosis, and immune response in breast cancer patients have been recently conducted. Herein, we focused on identifying differentially expressed DNA repair genes (DEGs) in breast cancer specimens and normal samples using the Wilcoxon rank-sum test. Biofunction enrichment analysis was performed with DEGs using the R software "cluster Profiler" package. DNA repair genes were involved in multivariate and univariate Cox regression analyses. After the optimization by AIC value, 11 DNA repair genes were sorted as prognostic DNA repair genes for breast cancer patients to calculate risk scores. Simultaneously, a nomogram was used to represent the prognostic model, which was validated using a calibration curve and C-index. Single-sample gene set enrichment analysis (ssGSEA), CIBERSORT algorithms, and ESTIMATE scores were applied to evaluate the immune filtration of tumor samples. Subsequently, anticarcinogen sensitivity analysis was performed using the R software "pRRophetic" package. Unsupervised clustering was used to excavate the correlation between the expression level of prognostic-significant DNA repair genes and clinical features. In summary, 56 DEGs were sorted, and their potential enriched biofunction pathways were revealed. In total, 11 DNA repair genes (UBE2A, RBBP8, RAD50 , FAAP20, RPA3, ENDOV, DDB2, UBE2V2, MRE11 , RRM2B, and PARP3 ) were preserved as prognostic genes to estimate risk score, which was applied to establish the prognostic model and stratified breast cancer patients into two groups with high or low risk. The calibration curve and C-index indicated that they reliably predicted the survival of breast cancer patients. Immune filtration analysis, anticarcinogen sensitivity analysis, and unsupervised clustering were applied to reveal the character of DNA repair genes between low- and high-risk groups. We identified 11 prognosis-significant DNA repair genes to establish prediction models and immune responses in breast cancer patients.

A Novel Inflammatory Response-Related Gene Signature Predicts Immune Status and Prognosis of Breast Cancer.

Breast cancer is the most common type of cancer and the leading cause of cancer-related death in women worldwide. In this study, we aimed to construct an inflammatory response-related gene model for predicting the immune status and prognosis of breast cancer patients. We obtained the inflammatory response-related genes from the Molecular Signatures Database. Furthermore, we used univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO) regression analysis, and multivariate Cox regression to construct an inflammatory response-related gene signature (IRGS) model based on dataset obtained from The Cancer Genome Atlas (TCGA). Patients were consequently categorized into high-risk and low-risk groups. Kaplan-Meier analysis was used to compare the overall survival (OS) of high-risk and low-risk groups. Following that, we validated the model using a dataset (GSE96058) acquired from Gene Expression Omnibus (GEO) database. Univariate and multivariate Cox analyses were used to determine the independent prognostic value of the IRGS in the TCGA and GSE96058 cohorts. A nomogram was constructed to predict the OS in the TCGA cohort. Further, we used Gene Set Enrichment Analysis (GSEA), CIBERSORT, and single-sample Gene Set Enrichment Analysis (ssGSEA) to evaluate the associations of IRGS with immune-associated pathways and immune infiltration. Finally, the relationship between the expression of the signature genes and drug sensitivity was conducted using Pearson correlation analysis. We established an IRGS to stratify breast cancer patients into the low-risk and high-risk groups. In both the training and validation sets, patients in the high-risk group had significantly shorter OS than those in the low-risk group. The risk score was significantly correlated with the clinical characteristics and could be used as a tool to predict the prognosis of breast cancer. Moreover, we found that the IRGS risk score was an independent predictor of OS in breast cancer patients, and a nomogram model based on IRGS risk score and other clinical factors could effectively predict the prognosis of breast cancer patients. Furthermore, the IRGS risk score was correlated with immune characteristics and was inversely associated with the abundance of immune cell infiltration. Patients with a low IRGS risk score had higher expression levels of immune checkpoint genes, suggesting that IRGS can be used as a potential indicator for immunotherapy. Finally, we found that the expression levels of prognostic genes were significantly correlated with tumor cell sensitivity to chemotherapeutic drugs. Overall, these findings suggest that the IRGS can be used to predict the prognosis and immune status of breast cancer patients and provide new therapeutic targets for the treatment of these patients.