Breast Cancer Cohort Research Articles

Immunogenic cell death-related classification reveals prognosis and effectiveness of immunotherapy in breast cancer

Lack of specific biomarkers and effective drug targets constrains therapeutic research in breast cancer (BC). In this regard, therapeutic modulation of damage-associated molecular patterns (DAMPs)-induced immunogenic cell death (ICD) may help improve the effect of immunotherapy in individuals with BC. The aim of this investigation was to develop biomarkers for ICD and to construct ICD-related risk estimation models to predict prognosis and immunotherapy outcomes of BC. RNA-seq transcriptome information and medical data from individuals with BC (n = 943) were obtained from TCGA. Expression data from a separate BC cohort (GEO: GSE20685) were used for validation. We identified subtypes of high and low ICD gene expression by consensus clustering and assessed the connection between ICD subtypes and tumor microenvironment (TME). In addition, different algorithms were used to construct ICD-based prognostic models of BC. BC samples were categorized into subtypes of high and low ICD expression depending on the expression of genes correlated with ICD. The subtype of ICD high-expression subtypes are correlated with poor prognosis in breast cancer, while ICD low-expression subtypes may predict better clinical outcomes. We also created and verified a predictive signature model depending on four ICD-related genes (ATG5, CD8A, CD8B, and HSP90AA1), which correlates with TME status and predicts clinical outcomes of BC patients. We highlight the connection of ICD subtypes with the dynamic evolution of TME in BC and present a novel ICD-based prognostic model of BC. In clinical practice, distinction of ICD subtype and assessment of ICD-related biomarkers should help guide treatment planning and improve the effectiveness of tumor immunotherapy.

Contrastive learning-based histopathological features infer molecular subtypes and clinical outcomes of breast cancer from unannotated whole slide images

The artificial intelligence-powered computational pathology has led to significant improvements in the speed and precision of tumor diagnosis, while also exhibiting substantial potential to infer genetic mutations and gene expression levels. However, current studies remain limited in predicting molecular subtypes and clinical outcomes in breast cancer. In this paper, we proposed a weakly supervised contrastive learning framework to address this challenge. Our framework first performed contrastive learning pretraining on a large number of unlabeled patches tiled from whole slide images (WSIs) to extract patch-level features. The gated attention mechanism was leveraged to aggregate patch-level features to produce slide feature that was then applied to various downstream tasks. To confirm the effectiveness of the proposed method, three public cohorts and one external independent cohort of breast cancer have been used to conducted evaluation experiments. The predictive powers of our model to infer gene expression, molecular subtypes, recurrence events and drug responses were validated across cohorts. In addition, the learned patch-level attention scores enabled us to generate heatmaps that were highly consistent with pathologist annotations and spatial transcriptomic data. These findings demonstrated that our model effectively established the high-order genotype-phenotype associations, thereby potentially extend the application of digital pathology in clinical practice.

Soluble urokinase plasminogen activator receptor and cardiotoxicity in doxorubicin-treated breast cancer patients: a prospective exploratory study

BackgroundSoluble urokinase plasminogen activator receptor is an inflammatory biomarker that may prognosticate cardiovascular outcomes. We sought to determine the associations between soluble urokinase plasminogen activator receptor and established markers of cardiotoxicity in breast cancer patients receiving doxorubicin.MethodsWe conducted a prospective cohort study of women with newly diagnosed breast cancer receiving standard-dose doxorubicin (240 mg/m2) at Rush University Medical Center and Rush Oak Park Hospital (Chicago, IL) between January 2017 and May 2019. Left ventricular ejection fraction, global longitudinal strain, and cardiac biomarkers (N-terminal prohormone B-type natriuretic peptide, troponin-I, and high-sensitivity C-reactive protein) were measured at baseline and at intervals up to 12-month follow-up after end of treatment. The associations between soluble urokinase plasminogen activator receptor and these endpoints were evaluated using multivariable mixed effects linear regression.ResultsOur study included 37 women (mean age 47.0 ± 9.3 years, 60% white) with a median baseline soluble urokinase plasminogen activator receptor level of 2.83 ng/dL. No participant developed cardiomyopathy based on serial echocardiography by one-year follow-up. The median percent change in left ventricular strain was -4.3% at 6-month follow-up and absolute changes in cardiac biomarkers were clinically insignificant. There were no significant associations between soluble urokinase plasminogen activator receptor and these markers of cardiotoxicity (all p > 0.05).ConclusionsIn this breast cancer cohort, doxorubicin treatment was associated with a very low risk for cardiotoxicity. Across this narrow range of clinical endpoints, soluble urokinase plasminogen activator receptor was not associated with markers of subclinical cardiotoxicity. Further studies are needed to clarify the prognostic utility of soluble urokinase plasminogen activator receptor in doxorubicin-associated cardiomyopathy and should include a larger cohort of leukemia and lymphoma patients who receive higher doses of doxorubicin.

CD301 and LSECtin glycan-binding receptors of innate immune cells serve as prognostic markers and potential predictors of immune response in breast cancer subtypes.

Glycosylation is a prominent posttranslational modification, and alterations in glycosylation are a hallmark of cancer. Glycan-binding receptors, primarily expressed on immune cells, play a central role in glycan recognition and immune response. Here, we used the recombinant C-type glycan-binding receptors CD301, Langerin, SRCL, LSECtin, and DC-SIGNR to recognize their ligands on tissue microarrays (TMA) of a large cohort (n=1859) of invasive breast cancer of different histopathological types to systematically determine the relevance of altered glycosylation in breast cancer. Staining frequencies of cancer cells were quantified in an unbiased manner by a computer-based algorithm. CD301 showed the highest overall staining frequency (40%), followed by LSECtin (16%), Langerin (4%) and DC-SIGNR (0.5%). By Kaplan-Meier analyses, we identified LSECtin and CD301 as prognostic markers in different breast cancer subtypes. Positivity for LSECtin was associated with inferior disease-free survival in all cases, particularly in estrogen receptor positive (ER+) breast cancer of higher histological grade. In triple negative breast cancer, positivity for CD301 correlated with a worse prognosis. Based on public RNA single-cell sequencing data of human breast cancer infiltrating immune cells, we found CLEC10A (CD301) and CLEC4G (LSECtin) exclusively expressed in distinct subpopulations, particularly in dendritic cells and macrophages, indicating that specific changes in glycosylation may play a significant role in breast cancer immune response and progression.

Environmental pressures, tumor characteristics, and death rate in a female breast cancer cohort: a seven-years Bayesian survival analysis using cancer registry data from a contaminated area in Italy.

In Taranto, Southern Italy, adverse impacts on the environment and human health due to industrial installations have been studied. In the literature, few associations have been reported between environmental factors and breast cancer mortality in women. The aim of this study was to investigate the relationships between residence in areas with high environmental pressures, female breast cancer characteristics, and death rate. Data from the Taranto Cancer Registry were used, including all women with invasive breast cancer diagnosed between 01 January 2015 and 31 December 2020 and with follow-up to 31 December 2021. Bayesian mixed effects logistic and Cox regression models were fitted with the approach of integrated nested Laplace approximation, adjusting for patients and disease characteristics. A total of 10,445 person-years were observed. Variables associated with higher death rate were residence in the contaminated site of national interest (SIN) (HR 1.22, 95% CrI 1.01-1.48), pathological/clinical stage III (HR 2.77, 95% CrI 1.93-3.97) and IV (HR 17.05, 95% CrI 11.94-24.34), histological grade 3 (HR 2.50, 95% CrI 1.20-5.23), Ki-67 proliferation index of 21-50% (HR 1.42, 95% CrI 1.10-1.83) and > 50% (HR 1.81, 95% CrI 1.29-2.55), and bilateral localization (HR 1.65, 95% CrI 1.01-2.68). Variables associated with lower death rate were estrogen and/or progesterone receptor positivity (HR 0.61, 95% CrI 0.45-0.81) and HER2/neu oncogene positivity (HR 0.59, 95% CrI 0.44-0.79). The findings confirmed the independent prognostic values of different female breast cancer characteristics. Even after adjusting for patients and disease characteristics, residence in the SIN of Taranto appeared to be associated with an increased death rate.

TRPS1 is a promising marker for all subtypes of breast cancer.

Trichorhinophalangeal syndrome-1 (TRPS1) has been proposed as a novel breast marker with equally high expression in breast cancer (BC) subtypes, making it a useful diagnostic tool. Here, its expression was evaluated alongside other commonly used markers [GATA3, GCDFP15, mammaglobin (MGB) and SOX10] in a large cohort of BCs (n = 1852) and their corresponding nodal metastases. Its usefulness as a diagnostic tool and its correlation with clinicopathological features were assessed. TRPS1 was expressed at 75.8% overall in the BC cohort, with at least 58% expression among BC subtypes. It was less sensitive than GATA3 for luminal and HER2-overexpressing (HER2-OE) cancers (luminal A: 82 versus 97%; luminal B: 80 versus 95%; HER2-OE: 62 versus 76%), but it was the most sensitive for TNBC (60 versus ≤ 41%). It showed a stable expression in nodal metastases (primary tumour 76 versus nodal metastasis 78%), unlike a reduced nodal expression for GATA3 (86 versus 77%). TRPS1 outperformed GATA3 in detecting non-luminal cancers whenpaired with other breast markers. TRPS1 and GCDFP15 was the most sensitive combination in TNBC detection, with a 76% detection rate. For TRPS1-negative and GCDFP15-negative TNBCs, SOX10 was more sensitive than GATA3 (29 versus 24%). TRPS1 is a highly sensitive marker for all breast cancer subtypes, outperforming GATA3 in non-luminal cancers and displaying the highest sensitivity for TNBC detection when combined with GCDFP15. It is a valuable addition to the breast marker panel for accurate identification of BC.

Integrating single-cell and bulk transcriptomic analyses to develop a cancer-associated fibroblast-derived biomarker for predicting prognosis and therapeutic response in breast cancer.

Cancer-associated fibroblasts (CAFs) contribute to the progression and treatment of breast cancer (BRCA); however, risk signatures and molecular targets based on CAFs are limited. This study aims to identify novel CAF-related biomarkers to develop a risk signature for predicting the prognosis and therapeutic response of patients with BRCA. CAF-related genes (CAFRGs) and a risk signature based on these genes were comprehensively analyzed using publicly available bulk and single-cell transcriptomic datasets. Modular genes identified from bulk sequencing data were intersected with CAF marker genes identified from single-cell analysis to obtain reliable CAFRGs. Signature CAFRGs were screened via Cox regression and least absolute shrinkage and selection operator (LASSO) analyses. Multiple patient cohorts were used to validate the prognosis and therapeutic responsiveness of high-risk patients stratified based on the CAFRG-based signature. In addition, the relationship between the CAFRG-based signature and clinicopathological factors, tumor immune landscape, functional pathways, chemotherapy sensitivity and immunotherapy sensitivity was examined. External datasets were used and sample experiments were performed to examine the expression pattern of MFAP4, a key CAFRG, in BRCA. Integrated analyses of single-cell and bulk transcriptomic data as well as prognostic screening revealed a total of 43 prognostic CAFRGs; of which, 14 genes (TLN2, SGCE, SDC1, SAV1, RUNX1, PDLIM4, OSMR, NT5E, MFAP4, IGFBP6, CTSO, COL12A1, CCDC8 and C1S) were identified as signature CAFRGs. The CAFRG-based risk signature exhibited favorable efficiency and accuracy in predicting survival outcomes and clinicopathological progression in multiple BRCA cohorts. Functional enrichment analysis suggested the involvement of the immune system, and the immune infiltration landscape significantly differed between the risk groups. Patients with high CAF-related risk scores (CAFRSs) exhibited tumor immunosuppression, enhanced cancer hallmarks and hyposensitivity to chemotherapy and immunotherapy. Five compounds were identified as promising therapeutic agents for high-CAFRS BRCA. External datasets and sample experiments validated the downregulation of MFAP4 and its strong correlation with CAFs in BRCA. A novel CAF-derived gene signature with favorable predictive performance was developed in this study. This signature may be used to assess prognosis and guide individualized treatment for patients with BRCA.

Aberrant HIF1- α and SIX-1 Expression is Associated with Poor Prognosis in Acute Myeloid Leukemia Patients with Isocitrate Dehydrogenase 1 Mutations.

IDH1 mutations are common in many cancers, however, their role in promoting the Warburg effect remains elusive. This study elucidates the putative involvement of mutant-IDH1 in regulating hypoxia-inducible factor (HIF1-α) and Sine-Oculis Homeobox-1 (SIX-1) expression. Genetic screening was performed using the ARMS-PCR in acute myeloid leukemia (AML), brain, and breast cancer (BC) cohorts, while transcript expression was determined using qPCR. Further, a meta-analysis of risk factors associated with the R132 mutation was performed. Approximately 32% of AML and ∼60% of glioma cases were mutants, while no mutation was found in the BC cohort. 'AA' and TT' were associated with higher disease risk (OR = 12.18 & 4.68) in AML and had significantly upregulated IDH1 expression. Moreover, downregulated HIF1-α and upregulated SIX-1 expression was also observed in these patients, suggesting that mutant-IDH1 may alter glucose metabolism. Perturbed IDH1 and HIF-α levels exhibited poor prognosis in univariate and multivariate analysis, while age and gender were found to be contributory factors as well. Based on the ROC model, these had a good potential to be used as prognostic markers. A significant variation in frequencies of R132 mutations in AML among different populations was observed. Cytogenesis (R2 = 12.2%), NMP1 mutation status (R2 = 18.5%), and ethnic contributions (R2 = 73.21%) were critical moderators underlying these mutations. Women had a higher risk of R132 mutation (HR = 1.3, P < 0.04). The pooled prevalence was calculated to be 0.29 (95% CI 0.26-0.33, P < 0.01), indicating that IDH1 mutations are a significant prognostic factor in AML. IDH1 and HIF1-α profiles are linked to poor survival and prognosis, while high SIX-1 expression in IDH1 mutants suggests a role in leukemic transformation and therapy response in AML.

Transcriptome-based identification of key actin-binding proteins associated with high metastatic potential in breast cancer.

Actin-binding proteins (ABPs) are essential for the regulation of morphological plasticity required for tumor cells to metastasize. The aim of this study was to perform an unbiased bioinformatic approach to identify the key ABPs significantly associated with the metastatic potential of breast cancer cells. Microarray data from 181 primary breast cancer samples from our hospital were used, and all genes belonging to the Gene Ontology term actin cytoskeleton organization were obtained from QuickGO. Association with metastasis-free survival probability was tested using Cox proportional hazards regression, and pairwise co-expression was tested by Pearson correlations. Differential expression between different subgroups was analyzed using Wilcoxon tests for dichotomous traits and Kruskal-Wallis tests for categorical traits. Validation was performed using four publicly available breast cancer datasets. ARHGAP25 was significantly associated with a low metastatic potential, and CFL1, TMSB15A, and ACTL8 were significantly associated with a high metastatic potential. A significantly higher expression of CFL1, TMSB15A, and ACTL8 mRNA was found in the more aggressive Her2-positive and triple-negative subtypes as well as in ER-negative samples. Also, these genes were co-expressed in the same tumors. However, only mRNA levels of CFL1 were increased in pN1 compared to pN0 patients. External validation revealed that CFL1 and TMSB15A had significant associations with consistent hazard ratios in two breast cancer cohorts, and among these, CFL1 exhibited the highest hazard ratios. CFL1 showed the strongest correlation with the metastatic potential of breast tumors. Thus, targeted inhibition of CFL1 might be a promising approach to treat malignant breast cancer cells.

The Prognostic Value of Serum Albumin to Globulin Ratio in Patients with Breast Cancer: A Retrospective Study.

This study examined the potential risk value of the serum albumin to globulin ratio (AGR) in patients with breast cancer (BC). This study employed a retrospective design, enrolling 332 patients with BC and 38 patients without BC treated at Taizhou People's Hospital between September 2015 and May 2021. Multivariate Cox proportional hazard regression models were used to identify potential risk factors. A prognostic nomogram was developed based on the multivariate analyses. The receiver operating characteristic curve determined the optimal cutoff value for AGR. The results indicated a statistically significant decrease in AGR among patients with BC. Significant disparities were observed in globulin and AGR levels between the two cohorts. AGR was significantly associated with tumor size and stage, with a marked decline in advanced stages of BC. Additionally, AGR and aspartate transaminase/Alanine aminotransferase (AST/ALT) emerged as significant diagnostic indicators for invasive carcinoma and advanced stages (II-IV) of BC. Specifically, AGR exhibited an area under the curve of 0.645 (P < 0.003), highlighting the discriminatory capacity of serum globulin levels in distinguishing between BC and non-BC cohorts. The AGR, routinely assessed due to its simplicity, objectivity, and cost-effectiveness, holds promise as a potential risk factor for BC and may have practical implications in clinical settings.

Multi-omics Deep-learning Prediction of Homologous Recombination Deficiency-like Phenotype Improved Risk Stratification and Guided Therapeutic Decisions in Gynecological Cancers.

Homologous recombination deficiency (HRD) is a well-recognized important biomarker in determining the clinical benefits of platinum-based chemotherapy and PARP inhibitor therapy for patients diagnosed with gynecologic cancers. Accurate prediction of HRD phenotype remains challenging. Here, we proposed a novel Multi-Omics integrative Deep-learning framework named MODeepHRD for detecting HRD-positive phenotype. MODeepHRD utilizes a convolutional attention autoencoder that effectively leverages omics-specific and cross-omics complementary knowledge learning. We trained MODeepHRD on 351 ovarian cancer (OV) patients using transcriptomic, DNA methylation and mutation data, and validated it in 2133 OV samples of 22 datasets. The predicted HRD-positive tumors were significantly associated with improved survival (HR = 0.68; 95% CI, 0.60-0.77; log-rank p < 0.001 for meta-cohort; HR = 0.5; 95% CI, 0.29-0.86; log-rank p = 0.01 for ICGC-OV cohort) and higher responsetoplatinum-basedchemotherapy compared to predicted HRD-negative tumors. The translational potential of MODeepHRDs was further validated in multicenter breast and endometrial cancer cohorts. Furthermore, MODeepHRD outperforms conventional machine-learning methods and other similar task approaches. In conclusion, our study demonstrates the promising value of deep learning as a solution for HRD testing in the clinical setting. MODeepHRD holds potential clinical applicability in guiding patient risk stratification and therapeutic decisions, providing valuable insights for precision oncology and personalized treatment strategies.

Genomic Alterations Correlated to Trastuzumab Resistance and Clinical Outcomes in HER2+/HR- Breast Cancers of Patients Living in Northwestern China.

Anti-HER2 therapy has significantly improved the survival rates of patients with HER2+ breast cancer. However, a subset of these patients eventually experience treatment failure, and the underlying genetic mechanisms remain largely unexplored. This underscores the need to investigate the genomic heterogeneity of HER2+ breast cancer. In this study, we focus on HER2+/HR- breast cancer, as it differs from HER2+/HR+ breast cancer in terms of genetic and biological characteristics. We performed gene-targeted genome sequencing on 45 HER2+/HR- breast cancer samples and identified 650 mutations across 268 cancer-related genes. TP53 (71.1%) and PIK3CA (35.6%) were the most frequently mutated genes in our sample. Additionally, ERBB2 (77.8%), CDK12 (42.2%), and MYC (11.1%) exhibited a high frequency of copy number amplifications (CNAs). Comparative analysis with two other HER2+/HR- breast cancer cohorts revealed that our cohort had higher genetic variation rates in ARID1A, PKHD1, PTPN13, FANCA, SETD2, BRCA2, BLM, STAG2, FAT1, TOP2A, POLE, ATM, KMT2B, FGFR4, and EPAS1. Notably, in our cohort, NF1 and ATM mutations were more prevalent in trastuzumab-resistant patients (NF1, p=0.016; ATM, p=0.006) and were associated with primary trastuzumab resistance (NF1, p=0.042; ATM, p=0.021). Moreover, patients with NF1 mutations (p=0.009) and high histological grades (p=0.028) were more likely to experience early relapse. Ultimately, we identified a unique cancer-related gene mutation profile and a subset of genes associated with primary resistance to trastuzumab and RFS in patients with HER2+/HR- breast cancer in Northwest China. These findings could lay the groundwork for future studies aimed at elucidating the mechanisms of resistance to trastuzumab and improving HER2-targeted treatment strategies.

Upregulated bone morphogenetic protein 8A (BMP8A) in triple negative breast cancer (TNBC) and its involvement in the bone metastasis.

The present study aimed to investigate the involvement of aberrant BMP8A expression in TNBC and bone metastasis. Aberrant expression of BMP8A in breast cancer was first determined by analyzing The Cancer Genome Atlas breast cancer cohort (TCGA-BRCA) and an immunohistochemical (IHC) staining of BMP8A in a breast cancer tissue microarray (TMA). Clinical relevance of deregulated BMP8A in breast cancer was assessed using Kaplan-Meier online analysis. The influence of BMP8A on cellular functions of two TNBC cell lines was assessed using in vitro assays. Conditional medium (CM) collected from the supernatant of hFOB cells and bone matrix extract (BME) was applied to mimic the bone micro-environment to evaluate the role played by BMP8A in bone metastasis. Correlations with both osteolytic and osteoblastic markers were evaluated in the TCGA-BRCA cohort. Expression of certain responsive genes was quantified in the BMP8A overexpression cell lines. Additionally, signal transduction through both Smad-dependent and independent pathways was evaluated using Western blot assay. Compared to the adjacent normal tissues, BMP8A expression was significantly increased in primary tumors (p < 0.05) which was associated with shorter distant metastasis free survival (DMFS) in TNBC (p < 0.05). BMP8A was observed to enhance cell invasion and migration within TNBC cells. In the simulated bone milieu, both MDA-MB-231BMP8Aexp and BT549BMP8Aexp cells presented enhanced invasiveness. BMP8A level was strongly correlated with most osteolytic and osteoblastic markers, suggesting the potential involvement of BMP8A in bone metastasis in TNBC. Receptor activator of nuclear factor kappa-B ligand (RANKL) expression was significantly increased in BMP8A overexpressed triple-negative cell lines (MDA-MB-231 and BT549). Furthermore, enhanced phosphorylation of Smad3 and increased expression of epidermal growth factor receptor (EGFR) were observed in MDA-MB-231 cells overexpressing BMP8A. BMP8A was upregulated in TNBC which was associated with poorer DMFS. BMP8A overexpression enhanced the invasion and migration of TNBC cells. With a putative role in osteolytic bone metastasis in TNBC, BMP8A represents a promising candidate for further investigation into its therapeutic potential.

Artificial Intelligence-Based Mitosis Scoring in Breast Cancer: Clinical Application

In recent years, artificial intelligence (AI) has demonstrated exceptional performance in mitosis identification and quantification. However, the implementation of AI in clinical practice needs to be evaluated against the existing methods. This study is aimed at assessing the optimal method of using AI-based mitotic figure scoring in breast cancer (BC). We utilized whole slide images from a large cohort of BC with extended follow-up comprising a discovery (n = 1715) and a validation (n = 859) set (Nottingham cohort). The Cancer Genome Atlas of breast invasive carcinoma (TCGA-BRCA) cohort (n = 757) was used as an external test set. Employing automated mitosis detection, the mitotic count was assessed using 3 different methods, the mitotic count per tumor area (MCT; calculated by dividing the number of mitotic figures by the total tumor area), the mitotic index (MI; defined as the average number of mitotic figures per 1000 malignant cells), and the mitotic activity index (MAI; defined as the number of mitotic figures in 3 mm2 area within the mitotic hotspot). These automated metrics were evaluated and compared based on their correlation with the well-established visual scoring method of the Nottingham grading system and Ki67 score, clinicopathologic parameters, and patient outcomes. AI-based mitotic scores derived from the 3 methods (MCT, MI, and MAI) were significantly correlated with the clinicopathologic characteristics and patient survival (P < .001). However, the mitotic counts and the derived cutoffs varied significantly between the 3 methods. Only MAI and MCT were positively correlated with the gold standard visual scoring method used in Nottingham grading system (r = 0.8 and r = 0.7, respectively) and Ki67 scores (r = 0.69 and r = 0.55, respectively), and MAI was the only independent predictor of survival (P < .05) in multivariate Cox regression analysis. For clinical applications, the optimum method of scoring mitosis using AI needs to be considered. MAI can provide reliable and reproducible results and can accurately quantify mitotic figures in BC.

Comprehensive genomic profiling of breast cancers characterizes germline-somatic mutation interactions mediating therapeutic vulnerabilities

Germline-somatic mutation interactions are universal and associated with tumorigenesis, but their role in breast cancer, especially in non-Caucasians, remains poorly characterized. We performed large-scale prospective targeted sequencing of matched tumor-blood samples from 4079 Chinese females, coupled with detailed clinical annotation, to map interactions between germline and somatic alterations. We discovered 368 pathogenic germline variants and identified 5 breast cancer DNA repair-associated genes (BCDGs; BRCA1/BRCA2/CHEK2/PALB2/TP53). BCDG mutation carriers, especially those with two-hit inactivation, demonstrated younger onset, higher tumor mutation burden, and greater clinical benefits from platinum drugs, PARP inhibitors, and immune checkpoint inhibitors. Furthermore, we leveraged a multiomics cohort to reveal that clinical benefits derived from two-hit events are associated with increased genome instability and an immune-activated tumor microenvironment. We also established an ethnicity-specific tool to predict BCDG mutation and two-hit status for genetic evaluation and therapeutic decisions. Overall, this study leveraged the large sequencing cohort of Chinese breast cancers, optimizing genomics-guided selection of DNA damaging-targeted therapy and immunotherapy within a broader population.

Targeting eIF4A triggers an interferon response to synergize with chemotherapy and suppress triple-negative breast cancer.

Protein synthesis is frequently dysregulated in cancer and selective inhibition of mRNA translation represents an attractive cancer therapy. Here, we show that therapeutically targeting the RNA helicase eIF4A with zotatifin, the first-in-class eIF4A inhibitor, exerts pleiotropic effects on both tumor cells and the tumor immune microenvironment in a diverse cohort of syngeneic triple-negative breast cancer (TNBC) mouse models. Zotatifin not only suppresses tumor cell proliferation but also directly repolarizes macrophages toward an M1-like phenotype and inhibits neutrophil infiltration, which sensitizes tumors to immune checkpoint blockade. Mechanistic studies revealed that zotatifin reprograms the tumor translational landscape, inhibits the translation of Sox4 and Fgfr1, and induces an interferon (IFN) response uniformly across models. The induction of an IFN response is partially due to the inhibition of Sox4 translation by zotatifin. A similar induction of IFN-stimulated genes was observed in breast cancer patient biopsies following zotatifin treatment. Surprisingly, zotatifin significantly synergizes with carboplatin to trigger DNA damage and an even heightened IFN response, resulting in T cell-dependent tumor suppression. These studies identified a vulnerability of eIF4A in TNBC, potential pharmacodynamic biomarkers for zotatifin, and provide a rationale for new combination regimens consisting of zotatifin and chemotherapy or immunotherapy as treatments for TNBC.

Establishment of a lysosome-related prognostic signature in breast cancer to predict immune infiltration and therapy response.

Lysosomes are instrumental in intracellular degradation and recycling, with their functional alterations holding significance in tumor growth. Nevertheless, the precise role of lysosome-related genes (LRGs) in breast cancer (BC) remains elucidated. This study aimed to establish a prognostic model for BC based on LRGs. Employing The Cancer Genome Atlas (TCGA) BC cohort as a training dataset, this study identified differentially expressed lysosome-related genes (DLRGs) through intersecting LRGs with differential expression genes (DEGs) between tumor and normal samples. A prognostic model of BC was subsequently developed using Cox regression analysis and validated within two Gene Expression Omnibus (GEO) external validation sets. Further analyses explored functional pathways, the immune microenvironment, immunotherapeutic responses, and sensitivity to chemotherapeutic drugs in different risk groups. Additionally, the mRNA and protein expression levels of genes within the risk model were examined by utilizing the Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas (HPA) databases. Clinical tissue specimens obtained from patients were gathered to validate the expression of the model genes via Real-Time Polymerase Chain Reaction (RT-PCR). We developed a risk model of BC based on five specific genes (ATP6AP1, SLC7A5, EPDR1, SDC1, and PIGR). The model was validated for overall survival (OS) in two GEO validation sets (p=0.00034 for GSE20685 and p=0.0095 for GSE58812). In addition, the nomogram incorporating clinical factors showed better predictive performance. Compared to the low-risk group, the high-risk group had a higher level of certain immune cell infiltration, including regulatory T cells (Tregs) and type 2 T helper cells (Th2). The high-risk patients appeared to respond less well to general immunotherapy and chemotherapeutic drugs, according to the Tumor Immune Dysfunction and Exclusion (TIDE), Immunophenotype Score (IPS), and drug sensitivity scores. The RT-PCR results validated the expression trends of some prognostic-related genes in agreement with the previous differential expression analysis. Our innovative lysosome-associated signature can predict the prognosis for BC patients, offering insights for guiding subsequent immunotherapeutic and chemotherapeutic interventions. Furthermore, it has the potential to provide a scientific foundation for identifying prospective therapeutic targets.

Quantitative expression of oestrogen receptor in breast cancer: Clinical and molecular significance

BackgroundOestrogen receptor (ER) positive breast cancer (BC) patients are eligible for endocrine therapy (ET), regardless of ER immunohistochemical expression level. There is a wide spectrum of ER expression and the response to ET is not uniform. This study aimed to assess the clinical and molecular consequences of ER heterogeneity with respect to ET-response. MethodsER expression, categorised by percentage and staining intensity in a large BC cohort (n = 7559) was correlated with clinicopathological parameters and patient ET response. The Cancer Genome Atlas Data BC cohort (n = 1047) was stratified by ER expression and transcriptomic analysis completed to better understand the molecular basis of ER heterogeneity. ResultsThe quantitative proportional increase in ER expression was positively associated with favourable prognostic parameters. Tumours with 1–9% ER expression were characteristically similar to ER-negative (<1%) tumours. Maximum ET-response was observed in tumours with 100% ER expression, with responses significantly different to tumours exhibiting ER at < 100% and significantly decreased survival rates were observed in tumours with 50% and 10% of ER expression. The Histochemical-score (H-score), which considers both staining intensity and percentage, added significant prognostic value over ER percentage alone with significant outcome differences observed at H-scores of 30, 100 and 200. There was a positive correlation between ER expression and ESR1 mRNA expression and expression of ER-regulated genes. Pathway analysis identified differential expression in key cancer-related pathways in different ER-positive groups. ConclusionET-response is statistically proportionally related to ER expression with significant differences observed at 10%, 50% and 100%. The H-score adds prognostic and predictive information.

Confidence intervals of survival predictions with neural networks trained on molecular data

In medicine, an important objective is predicting patients’ survival based on their molecular and clinical characteristics. In this context, neural networks have recently been used for their ability to capture complex interactions in the data. Measuring the uncertainty associated with survival estimates obtained by neural networks is essential to enhance predictions’ reliability. We compared four methods adapted to multilayer perceptrons (MLPs) for building confidence intervals at the patient level. The methods were based either on bootstrap with Boot (Efron, 1979), ensembling with DeepEns (Lakshminarayanan et al., 2016), or Monte-Carlo Dropout with MCDrop and BMask (Gal and Ghahramani, 2016; Mancini et al., 2020). A comparison was made through MLP-based survival models: CoxCC and CoxTime (Kvamme et al., 2019) in a continuous time framework, DeepHit (Lee et al., 2018) and PLANN (Biganzoli et al., 1998) in a discrete time framework. We applied the methods to a simulation study, enabling us to estimate a coverage rate of the estimated confidence intervals. We also applied them to real-world datasets, and predicted the survival probability for patients with breast cancer and patients with lung cancer.In the simulation study, CoxCC and CoxTime obtained the mean C-indices numerically closest to those from the Oracle model (mean C-index of 0.723 for CoxCC, 0.726 for CoxTime, versus 0.743 for the Oracle model). Regarding the confidence intervals of survival probabilities, Boot with CoxCC obtained a coverage rate of 96.5%, the closest to the nominal value of 95%. MCDrop was slightly anticonservative and obtained a coverage rate of 89.8% with CoxTime. This method may represent a reasonable compromise in terms of coverage with regards of computational time. In the breast cancer cohort, MLPs had difficulty capturing additional prognostic information from the molecular data. In contrast, in the lung cancer cohort, the models led to substantially stronger discrimination values when adding molecular data to the clinical variables. In conclusion, we were able to represent uncertainty in the survival estimates at particular time points at the patient level using MLPs in the form of 95% confidence intervals. We recommend using CoxTime with either Boot or, for a less intensive computation time, MCDrop.

Single-cell histone chaperones patterns guide intercellular communication of tumor microenvironment that contribute to breast cancer metastases

BackgroundHistone chaperones (HCs) are crucial for governing genome stability and gene expression in multiple cancers. However, the functioning of HCs in the tumor microenvironment (TME) is still not clearly understood.MethodsSelf-tested single-cell RNA-seq data derived from 6 breast cancer (BC) patients with brain and liver metastases were reanalyzed by nonnegative matrix factorization (NMF) algorithm for 36 HCs. TME subclusters were observed with BC and immunotherapy public cohorts to assess their prognosis and immune response. The biological effect of HSPA8, one of the HCs, was verified by transwell assay and wound-healing assays.ResultsCells including fibroblasts, macrophages, B cells, and T cells, were classified into various subclusters based on marker genes. Additionally, it showed that HCs might be strongly associated with biological and clinical features of BC metastases, along with the pseudotime trajectory of each TME cell type. Besides, the results of bulk-seq analysis revealed that TME cell subclusters mediated by HCs distinguished significant prognostic value for BC patients and were relevant to patients’ immunotherapy responses, especially for B cells and macrophages. In particular, CellChat analysis exhibited that HCs-related TME cell subclusters revealed extensive and diverse interactions with malignant cells. Finally, transwell and wound-healing assays exhibited that HSPA8 deficiency inhibited BC cell migration and invasion.ConclusionsCollectively, our study first dissected HCs-guided intercellular communication of TME that contribute to BC metastases.