Integrated multiomic profiling of breast cancer in the Chinese population reveals patient stratification and therapeutic vulnerabilities.

Molecular profiling guides precision treatment of breast cancer; however, Asian patients are underrepresented in publicly available large-scale studies. We established a comprehensive multiomics cohort of 773 Chinese patients with breast cancer and systematically analyzed their genomic, transcriptomic, proteomic, metabolomic, radiomic and digital pathology characteristics. Here we show that compared to breast cancers in white individuals, Asian individuals had more targetable AKT1 mutations. Integrated analysis revealed a higher proportion of HER2-enriched subtype and correspondingly more frequent ERBB2 amplification and higher HER2 protein abundance in the Chinese HR+HER2+ cohort, stressing anti-HER2 therapy for these individuals. Furthermore, comprehensive metabolomic and proteomic analyses revealed ferroptosis as a potential therapeutic target for basal-like tumors. The integration of clinical, transcriptomic, metabolomic, radiomic and pathological features allowed for efficient stratification of patients into groups with varying recurrence risks. Our study provides a public resource and new insights into the biology and ancestry specificity of breast cancer in the Asian population, offering potential for further precision treatment approaches.

MFG-E8 facilitates heart repair through M1/M2 polarization after myocardial infarction by inhibiting CaMKII.

BACKGROUND: M1/M2 macrophage polarization affects patient outcomes after myocardial infarction (MI). The relationship between milk fat globule-epidermal growth factor 8 (MFG-E8) and Ca2+/calmodulin-dependent protein kinase II (CaMKII) on macrophage polarization after MI is unknown. To investigate the functional role of MFG-E8 in modulating cardiac M1/M2 macrophage polarization after MI, especially its influence on CaMKII signaling. METHODS: Human ventricular tissue and blood were obtained from patients with MI and controls. MFG-E8-KO mice were constructed (C57BL/6). The mice were randomized to WT-sham, sham-MFG-E8-KO, WT-PBS, rmMFG-E8 (WT injected with rmMFG-E8 10 min after MI), and MFG-E8-KO. The mouse macrophage cell line RAW264.7 was obtained. CaMKII, p-CaMKII, Akt, and NF-κB p65 were determined by qRT-PCR, western blot, and immunofluorescence. RESULTS: The MFG-E8 levels were significantly enhanced after MI in the hearts and plasma of patients with MI compared with controls. The MFG-E8 levels were significantly increased in the hearts and plasma of mice after MI. MFG-E8 was derived from cardiac fibroblasts. The administration of rmMFG-E8 improved ventricular remodeling and cardiac function after MI. rmMFG-E8 did not suppress infiltrating monocyte/macrophages into the peri-infarct area. rmMFG-E8 suppressed the polarization of macrophages to the M1 phenotype and promoted the polarization of macrophages to the M2 phenotype. rmMFG-E8 suppressed CaMKII-dependent signaling in macrophages. CONCLUSIONS: MFG-E8 and CaMKII appear to collaboratively regulate myocardial remodeling and M1/M2 macrophage polarization after MI. These observations suggest new roles for MFG-E8 in inhibiting M1 but promoting M2 macrophage polarization.

Components of the tumor immune microenvironment based on m-IHC correlate with prognosis and subtype of triple-negative breast cancer.

BACKGROUND AND AIM: The spatial distribution and interactions of cells in the tumor immune microenvironment (TIME) might be related to the different responses of triple-negative breast cancer (TNBC) to immunomodulators. The potential of multiplex IHC (m-IHC) in evaluating the TIME has been reported, but the efficacy is insufficient. We aimed to research whether m-IHC results could be used to reflect the TIME, and thus to predict prognosis and complement the TNBC subtyping system. METHODS: The clinical, imaging, and prognosis data for 86 TNBC patients were retrospectively reviewed. CD3, CD4, CD8, Foxp3, PD-L1, and Pan-CK markers were stained by m-IHC. Particular cell spatial distributions and interactions in the TIME were evaluated with the HALO multispectral analysis platform. Then, we calculated the prognostic value of components of the TIME and their correlations with TNBC transcriptomic subtypes and MRI radiomic features reflecting TNBC subtypes. RESULTS: The components of the TIME score were established by m-IHC and demonstrated positive prognostic value for TNBC (p = 0.0047, 0.039, <0.0001 for DMFS, RFS, and OS). The score was calculated from several indicators, including Treg% in the tumor core (TC) or stromal area (SA), PD-L1+ cell% in the SA, CD3 + cell% in the TC, and PD-L1+ /CD8+ cells in the invasive margin and SA. According to the TNBC subtyping system, a few TIME indicators were significantly different in different subtypes and significantly correlated with MRI radiomic features reflecting TNBC subtypes. CONCLUSION: We demonstrated that the m-IHC-based quantitative score and indicators related to the spatial distribution and interactions of cells in the TIME can aid in the accurate diagnosis of TNBC in terms of prognosis and classification.

Artificial intelligence in breast imaging: Current situation and clinical challenges.

Breast cancer ranks among the most prevalent malignant tumours and is the primary contributor to cancer-related deaths in women. Breast imaging is essential for screening, diagnosis, and therapeutic surveillance. With the increasing demand for precision medicine, the heterogeneous nature of breast cancer makes it necessary to deeply mine and rationally utilize the tremendous amount of breast imaging information. With the rapid advancement of computer science, artificial intelligence (AI) has been noted to have great advantages in processing and mining of image information. Therefore, a growing number of scholars have started to focus on and research the utility of AI in breast imaging. Here, an overview of breast imaging databases and recent advances in AI research are provided, the challenges and problems in this field are discussed, and then constructive advice is further provided for ongoing scientific developments from the perspective of the National Natural Science Foundation of China.

Case report: A rare case of left ventricular noncompaction in two Chinese siblings with becker muscular dystrophy caused by deletion of exons 10 to 12 in the DMD gene.

Background: Becker muscular dystrophy (BMD) is an inherited X-linked recessive condition resulting from mutations of the DMD gene encoding dystrophin. Left ventricular noncompaction (LVNC) is a rare cardiomyopathy morphologically characterized by abnormal myocardial trabeculae and deep recesses in the left ventricle. LVNC in BMD patients has only rarely been reported. Case report: In the present study, we identified a deletion mutation in exons 10 to 12 (EX10_12 del) of the DMD gene (reference sequence NM_004006.2) in two Chinese siblings with BMD and LVNC by high throughput targeted next-generation sequencing (NGS) and quantitative polymerase chain reaction (qPCR). The proband was a 22-year-old man admitted with dyspnea, abdominal distention, and polyserositis. It is noteworthy that both the proband and his younger brother manifested progressive muscular atrophy and creatine kinase (CK) elevation. Light and electron microscopy examination of muscle biopsies showed the typical features of dystrophinopathies. Cardiac magnetic resonance imaging and echocardiography demonstrated that both brothers had an enlarged left ventricle, LVNC, and reduced left ventricular ejection fraction. Finally, the proband underwent heart transplantation at age 26 with an event-free follow-up over 4 years post-transplantation. Conclusion: This case further enriches our knowledge of the symptoms, genotype, cardiac performance, management, and prognosis of BMD patients complicated by LVNC. It is recommended that early comprehensive cardiac evaluation should be considered for patients with BMD to exclude LVNC, as this may have a significant impact on their prognosis.

Radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets.

Intratumor heterogeneity (ITH) profoundly affects therapeutic responses and clinical outcomes. However, the widespread methods for assessing ITH based on genomic sequencing or pathological slides, which rely on limited tissue samples, may lead to inaccuracies due to potential sampling biases. Using a newly established multicenter breast cancer radio-multiomic dataset (n = 1474) encompassing radiomic features extracted from dynamic contrast-enhanced magnetic resonance images, we formulated a noninvasive radiomics methodology to effectively investigate ITH. Imaging ITH (IITH) was associated with genomic and pathological ITH, predicting poor prognosis independently in breast cancer. Through multiomic analysis, we identified activated oncogenic pathways and metabolic dysregulation in high-IITH tumors. Integrated metabolomic and transcriptomic analyses highlighted ferroptosis as a vulnerability and potential therapeutic target of high-IITH tumors. Collectively, this work emphasizes the superiority of radiomics in capturing ITH. Furthermore, we provide insights into the biological basis of IITH and propose therapeutic targets for breast cancers with elevated IITH.

Prediction of clinicopathological features, multi-omics events and prognosis based on digital pathology and deep learning in HR+/HER2- breast cancer.

Background: Breast cancer has the highest incidence and mortality rates among women worldwide. Hormone receptor (HR)+/human epidermal growth factor receptor 2 (HER2)- breast cancer is the most common molecular subtype, accounting for 50-79% of breast cancers. Deep learning has been widely used in cancer image analysis, especially for predicting targets related to precise treatment and patient prognosis. However, studies focusing on therapeutic target and prognosis predicting in HR+/HER2- breast cancer are lacking. Methods: This study retrospectively collected hematoxylin and eosin (H&E)-stained slides of HR+/HER2- breast cancer patients between January 2013 and December 2014 at Fudan University Shanghai Cancer Center (FUSCC) and scanned to generate whole-slide images (WSIs). Then, we built a deep-learning-based workflow to train and validate model to predict clinicopathological features, multi-omics molecular features and prognosis; the area under the curve (AUC) of the receiver operating characteristic (ROC) and the concordance index (C-index) of the test set were used to assess model effectiveness. Results: A total of 421 HR+/HER2- breast cancer patients were included in our study. Regarding clinicopathological features, grade III could be predicted with an AUC of 0.90 [95% confidence interval (CI): 0.84-0.97]. Regarding somatic mutations, TP53 and GATA3 mutation could be predicted with AUCs of 0.68 (95% CI: 0.56-0.81) and 0.68 (95% CI: 0.47-0.89), respectively. Regarding gene set enrichment analysis (GSEA) pathways, the G2-M checkpoint pathway was predicted with an AUC of 0.79 (95% CI: 0.69-0.90). Regarding markers of immunotherapy response, intratumoral tumor-infiltrating lymphocytes (iTILs), stromal tumor-infiltrating lymphocytes (sTILs), CD8A, and PDCD1 were predicted with AUCs of 0.78 (95% CI: 0.55-1.00), 0.76 (95% CI: 0.65-0.87), 0.71 (95% CI: 0.60-0.82), and 0.74 (95% CI: 0.63-0.85), respectively. In addition, we found that the integration of clinical prognostic variables and deep features of images can improve the stratification of patient prognosis. Conclusions: Using a deep-learning-based workflow, we developed models to predict the clinicopathological features, multi-omics features and prognosis of patients with HR+/HER2- breast cancer using pathological WSIs. This work may contribute to efficient patient stratification to promote the personalized management of HR+/HER2- breast cancer.

Flexible but Refractory Single-Crystalline Hyperbolic Metamaterials.

The fabrication of flexible single-crystalline plasmonic or photonic components in a scalable way is fundamentally important to flexible electronic and photonic devices with high speed, high energy efficiency, and high reliability. However, it remains a challenge. Here, we have successfully synthesized flexible single-crystalline optical hyperbolic metamaterials by directly depositing refractory nitride superlattices on flexible fluorophlogopite-mica substrates with magnetron sputtering. Interestingly, these flexible hyperbolic metamaterials show dual-band hyperbolic dispersion of dielectric constants with small dielectric losses and high figures of merit in the visible to near-infrared ranges. More importantly, the optical properties of these nitride-based flexible hyperbolic metamaterials show remarkable stability during 1000 °C heating or after being bent 1000 times. Therefore, the strategy developed in this work offers an easy and scalable route for fabricating flexible, high-performance, and refractory plasmonic or photonic components, which can significantly expand the applications of current electronic and photonic devices.

Association between multiple vitamins and bone mineral density: a cross-sectional and population-based study in the NHANES from 2005 to 2006.

BACKGROUND: Bone mineral density (BMD) alterations in response to multivitamin exposure were rarely studied. Our study assessed the association of coexposure to six types of vitamins (i.e., vitamins B12, B9, C, D, A and E) with BMD measurements in adults in the US. METHODS: Data were collected from participants aged ≥ 20 years (n = 2757) in the U.S. National Health and Nutrition Examination Surveys (NHANES) from 2005 to 2006. Multiple linear regression, restricted cubic splines, principal component analysis (PCA) and weighted quantile sum (WQS) regression were performed for statistical analysis. RESULTS: The circulating levels of vitamins B12 and C were positively associated with BMDs, and an inverted L-shaped exposure relationship was observed between serum vitamin C and BMDs. PCA identified two principal components: one for 'water-soluble vitamins', including vitamins B12, B9 and C, and one for 'fat-soluble vitamins', including vitamins A, D and E. The former was positively associated with total femur (ß = 0.009, 95%CI: 0.004, 0.015) and femoral neck (ß = 0.007, 95%CI: 0.002, 0.013) BMDs, and the latter was negatively associated with BMDs with non-statistical significance. The WQS index constructed for the six vitamins was significantly related to total femur (ß = 0.010, 95%CI: 0.001, 0.018) and femoral neck (ß = 0.008, 95%CI: 0.001, 0.015) BMDs, and vitamins B12 and C weighted the most. The WQS index was inversely related to BMDs with non-statistical significance, and vitamins E and A weighted the most. CONCLUSION: Our findings suggested a positive association between water-soluble vitamin coexposure and BMD, and the association was mainly driven by vitamins B12 and C. Negative association between fat-soluble vitamin coexposure and BMD was indicated, mainly driven by vitamins E and A. An inverted L-shaped exposure relationship was found between vitamin C and BMD.

Uncovering the molecular subtypes of triple-negative breast cancer with a noninvasive radiomic methodology.

In this Backstory, we illustrate how a recent work from our multidisciplinary collaborative group identifies molecular subtypes of triple-negative breast cancer with a noninvasive radiomic pipeline. These findings appeared in the July 2022 issue of Cell Reports Medicine (https://www.cell.com/cell-reports-medicine/fulltext/S2666-3791(22)00230-0).

Radiomics features for assessing tumor-infiltrating lymphocytes correlate with molecular traits of triple-negative breast cancer.

BACKGROUND: Tumor-infiltrating lymphocytes (TILs) have become a promising biomarker for assessing tumor immune microenvironment and predicting immunotherapy response. However, the assessment of TILs relies on invasive pathological slides. METHODS: We retrospectively extracted radiomics features from magnetic resonance imaging (MRI) to develop a radiomic cohort of triple-negative breast cancer (TNBC) (n = 139), among which 116 patients underwent transcriptomic sequencing. This radiomic cohort was randomly divided into the training cohort (n = 98) and validation cohort (n = 41) to develop radiomic signatures to predict the level of TILs through a non-invasive method. Pathologically evaluated TILs in the H&E sections were set as the gold standard. Elastic net and logistic regression were utilized to perform radiomics feature selection and model training, respectively. Transcriptomics was utilized to infer the detailed composition of the tumor microenvironment and to validate the radiomic signatures. RESULTS: We selected three radiomics features to develop a TILs-predicting radiomics model, which performed well in the validation cohort (AUC 0.790, 95% confidence interval (CI) 0.638-0.943). Further investigation with transcriptomics verified that tumors with high TILs predicted by radiomics (Rad-TILs) presented activated immune-related pathways, such as antigen processing and presentation, and immune checkpoints pathways. In addition, a hot immune microenvironment, including upregulated T cell infiltration gene signatures, cytokines, costimulators and major histocompatibility complexes (MHCs), as well as more CD8+ T cells, follicular helper T cells and memory B cells, was found in high Rad-TILs tumors. CONCLUSIONS: Our study demonstrated the feasibility of radiomics model in predicting TILs status and provided a method to make the features interpretable, which will pave the way toward precision medicine for TNBC.

Association of volatile organic compounds co-exposure with bone health indicators and potential mediators.

Limited evidence was found in the associations of volatile organic compound (VOC) exposure with bone health indicators. This study aimed to explore the associations of individual and combined metabolites of VOCs (mVOCs) in urine, a representative of the internal exposure level of VOCs, with bone mineral density (BMD), osteoporosis (OP) and fracture, and potential mediators. Data of the National Health Examination and Nutrition Survey 2005-2006 and 2013-2014 was used. Multiple linear and logistic regression modeling were performed to analyze the associations of individual mVOC with bone health indicators. The least absolute shrinkage and selection operator (LASSO) regression was adopted to select mVOCs that were more relevant to bone health indicators for further weight quantile sum (WQS) analysis used for analyzing the associations between multiple VOC co-exposure and bone health indicators. Mediation analysis was used to identify potential mediators. Seventeen mVOC members with detection rate of >50% in urine of all 3478 participants aged ≥20 years (1829 females) were involved. Levels of most mVOCs were higher in women than men. Eight mVOCs were negatively associated with BMDs, and two and four mVOCs were positively associated with OP and fracture risks, respectively. WQS regression revealed decreased femoral neck BMD (ß = -0.010 g/cm2, 95% CI: -0.020, -0.0001) and total spine BMD (ß = -0.015 g/cm2, 95% CI: -0.028, -0.002) in response to increasing mVOC mixture levels. And alkaline phosphatase (ALP), body mass index (BMI), fasting insulin (FI) and high-density lipoprotein (HDL), were mediators in the associations with proportions of mediating effect ranging from 4.6% to 10.2%. Individual and combined VOC (co-)exposure were associated with reduced BMDs in American adults. ALP, BMI, FI and HDL were demonstrated to be mediators in the association of multiple VOC co-exposure with BMD.

Radiogenomic analysis reveals tumor heterogeneity of triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a subset of breast cancer with an adverse prognosis and significant tumor heterogeneity. Here, we extract quantitative radiomic features from contrast-enhanced magnetic resonance images to construct a breast cancer radiomic dataset (n = 860) and a TNBC radiogenomic dataset (n = 202). We develop and validate radiomic signatures that can fairly differentiate TNBC from other breast cancer subtypes and distinguish molecular subtypes within TNBC. A radiomic feature that captures peritumoral heterogeneity is determined to be a prognostic factor for recurrence-free survival (p = 0.01) and overall survival (p = 0.004) in TNBC. Combined with the established matching TNBC transcriptomic and metabolomic data, we demonstrate that peritumoral heterogeneity is associated with immune suppression and upregulated fatty acid synthesis in tumor samples. Collectively, this multi-omic dataset serves as a useful public resource to promote precise subtyping of TNBC and helps to understand the biological significance of radiomics.

A Multiomics Signature Highlights Alterations Underlying Homologous Recombination Deficiency in Triple-Negative Breast Cancer.

BACKGROUND: Homologous recombination (HR) is a key pathway in DNA double-strand damage repair. HR deficiency (HRD) occurs more commonly in triple-negative breast cancers (TNBCs) than in other breast cancer subtypes. Several clinical trials have demonstrated the value of HRD in stratifying breast cancer patients into distinct groups based on their responses to poly(ADP ribose) polymerase inhibitors and chemotherapy. METHODS: We retrospectively collected TNBC samples to establish a multiomics cohort (n = 343) and explored the biological and phenotypic mechanisms underlying the better prognosis of patients with high HRD scores. Gene set enrichment analysis was conducted to elucidate the underlying pathways in patients with low HRD scores, and a radiomics model was established to predict the HRD score via a noninvasive method. RESULTS: Multivariable Cox analysis revealed the independent prognostic value of a low HRD score (hazard ratio 2.20, 95% confidence interval 1.05-4.59; p = 0.04). Furthermore, amino acid and lipid metabolism pathways were highly enriched in tumors from patients with low HRD scores, which was also demonstrated by differential abundant metabolite analysis. A noninvasive radiomics method was developed to predict the HRD status and it performed well in the independent validation cohort (support vector machine model: area under the curve [AUC] 0.739, sensitivity 0.571, and specificity 0.824; logistic regression model: AUC 0.695, sensitivity 0.571, and specificity 0.882). CONCLUSIONS: We revealed the prognostic value of the HRD score, predicted the HRD status with noninvasive radiomics features, and preliminarily explored druggable targets for TNBC patients with low HRD scores.

CaMKII inhibition protects against hyperthyroid arrhythmias and adverse myocardial remodeling.

Hyperthyroidism can potentiate arrhythmias and cardiac hypertrophy, whereas Ca2+/calmodulin-dependent kinase II (CaMKII) promotes maladaptive myocardial remodeling. However, it remains unclear whether CaMKII contributes to the progression of hyperthyroid heart disease (HHD). This study demonstrated that CaMKII inhibition can relieve adverse myocardial remodeling and reduce sinus tachycardia, isoproterenol-induced atrial fibrillation, and ventricular arrhythmias in hyperthyroid mice with preserved heart function. Hyperthyroid cardiac hypertrophy was promoted by CaMKII upregulation-induced HDAC4/MEF2a activation. Briefly, CaMKII inhibition benefits HHD management greatly in mice by preventing arrhythmias and maladaptive remodeling.

Systematic Immune-Inflammation Index Predicts Embolic Events in Infective Endocarditis.

Infective endocarditis (IE) is a life-threatening disease with embolisms occurring in 20%-50% of cases. We aimed to evaluate the value of the systemic immune-inflammation index (SII) in predicting embolic events (EEs) in patients with infective endocarditis.A total of 186 patients diagnosed with definite IE, who admitted to the Union Hospital affiliated to Tongji Medical College, Huazhong University of Science and Technology, were retrospectively identified from November 2011 to March 2019.The median (interquartile) age of the patients was 46 (32-57) years. Viridans group streptococci were the most common microorganism identified from blood culture (24.7%). The most frequent complication was heart failure (64.2%), followed by EEs (30.2%). Patients complicated with EEs presented a significantly higher SII than those without EEs (1605.38 versus 1039.61, P = 0.001). SII had an area under the curve (AUC) value for EEs of 0.661 (95% CI: 0.575-0.747, P = 0.001), which predicted the presence of EEs with a sensitivity of 42.6% and specificity of 86.3%. Multivariate logistic regression analysis revealed that SII (OR = 6.925; 95% CI: 1.035-46.318, P = 0.046) was an independent predictor of EEs in IE patients.We demonstrated that a high level of SII is associated with a higher likelihood of EEs. The SII may be a promising predictor for EEs in patients with IE.