COAP-Pd Catalyzed Asymmetric Formal [3+2] Cycloaddition for Optically Active Multistereogenic Spiro Cyclopentane-Indandiones Bearing Cyclic N-Sulfonyl Ketimine Skeletons.

We reported a chiral oxamide-phosphine ligand (COAP-Ph)-Pd-catalyzed asymmetric [3+2] cycloaddition reaction between vinyl cyclopropane derived from 1,3-indanedione and 2-vinylcyclopropane-1,1-dicarboxylates with cyclic sulfonyl 1-azadienes. The corresponding reactions provided a series of enantiomerically active spiro cyclopentane-indandione and cyclopentane structures bearing three consecutive stereogenic centers in good yields with good diastereo- and enantioselectivity. The COAP-Pd complex serves not only to promote generation of chiral π-allyl-palladium intermediates and induce the asymmetry of the reaction, but also depress the background reaction.

One-step in vivo gene knock-out in porcine embryos using recombinant adeno-associated viruses.

Introduction: Gene-edited pigs have become prominent models for studying human disease mechanisms, gene therapy, and xenotransplantation. CRISPR (clustered regularly interspaced short palindromic repeats)/CRISPR-associated 9 (CRISPR/Cas9) technology is a widely employed tool for generating gene-edited pigs. Nevertheless, delivering CRISPR/Cas9 to pre-implantation embryos has traditionally posed challenges due to its reliance on intricate micromanipulation equipment and specialized techniques, resulting in high costs and time-consuming procedures. This study aims to introduce a novel one-step approach for generating genetically modified pigs by transducing CRISPR/Cas9 components into pre-implantation porcine embryos through oviductal injection of recombinant adeno-associated viruses (rAAV). Methods: We first used rAAV-1, rAAV-6, rAAV-8, rAAV-9 expressing EGFP to screen for rAAV serotypes that efficiently target porcine embryos, and then, to achieve efficient expression of CRISPR/Cas9 in vivo for a short period, we packaged sgRNAs targeting the GHR genes to self-complementary adeno-associated virus (scAAV), and Cas9 proteins to single-stranded adeno-associated virus (ssAAV). The efficiency of porcine embryos -based editing was then validated in vitro. The feasibility of this one-step method to produce gene-edited pigs using rAAV-CRISPR/Cas9 oviductal injection into sows within 24 h of conception was then validated. Results: Our research firstly establishes the efficient delivery of CRISPR/Cas9 to pig zygotes, both in vivo and in vitro, using rAAV6. Successful gene editing in pigs was achieved through oviductal injection of rAAV-CRISPR/Cas9. Conclusion: This method circumvents the intricate procedures involved in in vitro embryo manipulation and embryo transfers, providing a straightforward and cost-effective approach for the production of gene-edited pigs.

Development of an Imidazopyridazine-Based MNK1/2 Inhibitor for the Treatment of Lymphoma.

The mitogen-activated protein kinase-interacting protein kinases (MNKs) are the only kinases known to phosphorylate eukaryotic translation initiation factor 4E (eIF4E) at Ser209, which plays a significant role in cap-dependent translation. Dysregulation of the MNK/eIF4E axis has been found in various solid tumors and hematological malignancies, including diffuse large B-cell lymphoma (DLBCL). Herein, structure-activity relationship studies and docking models determined that 20j exhibits excellent MNK1/2 inhibitory activity, stability, and hERG safety. 20j exhibits strong and broad antiproliferative activity against different cancer cell lines, especially GCB-DLBCL DOHH2. 20j suppresses the phosphorylation of eIF4E in Hela cells (IC50 = 90.5 nM) and downregulates the phosphorylation of eIF4E and 4E-BP1 in A549 cells. In vivo studies first revealed that ibrutinib enhances the antitumor effect of 20j without side effects in a DOHH2 xenograft model. This study provided a solid foundation for the future development of a MNK inhibitor for GCB-DLBCL treatment.

Design, synthesis and biological evaluation of MNK-PROTACs.

Mitogen-activated protein kinase (MAPK)-interacting kinases (MNKs) can regulate cellular mRNA translation by controlling the phosphorylation of the eukaryotic translation initiation factor 4E (eIF4E), which plays an important role in tumor initiation, development, and metastasis. Although small-molecule MNK inhibitors have made significant breakthroughs in the treatment of various malignancies, their clinical application can be limited by drug resistance, target selectivity and other factors. The strategy of MNK-PROTACs which selectively degrades MNK kinases provides a new approach for developing small-molecule drugs for related diseases. In this study, DS33059, a small-molecule compound modified based on the ongoing clinical trials drug ETC-206, was chosen as the target protein ligand. A series of novel MNK-PROTACs were designed, synthesized and evaluated biological activity. Several compounds showed good inhibitory activities against MNK1/2. Besides, compounds exhibited moderate to excellent anti-proliferative activity in A549 and TMD-8 cells in vitro. In particular, compound II-5 significantly inhibited A549 (IC50 = 1.79 µM) and TMD-8 (IC50 = 1.07 µM) cells. The protein degradation assay showed that compound II-5 had good capability to degrade MNK1. The MNK-PROTACs strategy represents a new direction in treating tumors and deserves further exploration.

DNAJC12 causes breast cancer chemotherapy resistance by repressing doxorubicin-induced ferroptosis and apoptosis via activation of AKT.

BACKGROUND: Chemotherapy is a primary treatment for breast cancer (BC), yet many patients develop resistance over time. This study aims to identify critical factors contributing to chemoresistance and their underlying molecular mechanisms, with a focus on reversing this resistance. METHODS: We utilized samples from the Gene Expression Omnibus (GEO) and West China Hospital to identify and validate genes associated with chemoresistance. Functional studies were conducted using MDA-MB-231 and MCF-7 cell lines, involving gain-of-function and loss-of-function approaches. RNA sequencing (RNA-seq) identified potential mechanisms. We examined interactions between DNAJC12, HSP70, and AKT using co-immunoprecipitation (Co-IP) assays and established cell line-derived xenograft (CDX) models for in vivo validations. RESULTS: Boruta analysis of four GEO datasets identified DNAJC12 as highly significant. Patients with high DNAJC12 expression showed an 8 % pathological complete response (pCR) rate, compared to 38 % in the low expression group. DNAJC12 inhibited doxorubicin (DOX)-induced cell death through both ferroptosis and apoptosis. Combining apoptosis and ferroptosis inhibitors completely reversed DOX resistance caused by DNAJC12 overexpression. RNA-seq suggested that DNAJC12 overexpression activated the PI3K-AKT pathway. Inhibition of AKT reversed the DOX resistance induced by DNAJC12, including reduced apoptosis and ferroptosis, restoration of cleaved caspase 3, and decreased GPX4 and SLC7A11 levels. Additionally, DNAJC12 was found to increase AKT phosphorylation in an HSP70-dependent manner, and inhibiting HSP70 also reversed the DOX resistance. In vivo studies confirmed that AKT inhibition reversed DNAJC12-induced DOX resistance in the CDX model. CONCLUSION: DNAJC12 expression is closely linked to chemoresistance in BC. The DNAJC12-HSP70-AKT signaling axis is crucial in mediating resistance to chemotherapy by suppressing DOX-induced ferroptosis and apoptosis. Our findings suggest that targeting AKT and HSP70 activities may offer new therapeutic strategies to overcome chemoresistance in BC.

Difference-Deformable Convolution with Pseudo Scale Instance Map for Cell Localization.

Cell localization still faces two unresolved challenges: 1) the dramatic variations in cell morphology, coupled with the heterogeneous intensity distribution of lightly stained cells; 2) existing cell location maps lack scale information, resulting in insufficient supervision for point maps and inaccurate supervision for density maps. 1) To address the first challenges, we introduce a novel gradient-aware and shape-adaptive Difference-Deformable Convolution (DDConv), which enhances the model's robustness to color by leveraging gradient information while adaptively adjusting the shape of the convolutional kernel to tackle the substantial variability in cell morphology. 2) To overcome the issue of unreasonable location maps, we propose the Pseudo-Scale Instance (PSI) map, which can adaptively provide the corresponding scale information for each cell to realize accurate supervision. We analyze and evaluate DDConv and the PSI map in three challenging cell localization tasks. In comparison to existing methods, our proposed approach significantly enhances localization performance, setting a new benchmark for the cell localization task. Our code is available at https://github.com/ChyaZhang/DDConv-PSI.

Novel chlorin e6-based conjugates of tyrosine kinase inhibitors: Synthesis and photobiological evaluation as potent photosensitizers for photodynamic therapy.

Since tyrosine kinase inhibitor (TKI) could reverse ABCG2-mediated drug-resistance, novel chlorin e6-based conjugates of Dasatinib and Imatinib as photosensitizer (PS) were designed and synthesized. The results demonstrated that conjugate 10b showed strongest phototoxicity against HepG2 and B16-F10 cells, which was more phototoxic than chlorin e6 and Talaporfin. It could reduce efflux of intracellular PS by inhibiting ABCG2 in HepG2 cells, and localize in mitochondria, lysosomes, golgi and ER, resulting in higher cell apoptosis rate and ROS production than Talaporfin. Moreover, it could induce cell autophagy and block cell cycle in S phase, and significantly inhibit tumor growth and prolong survival time on BALB/c nude mice bearing HepG2 xenograft tumor to a greater extent than chlorin e6. Consequently, compound 10b could be applied as a promising candidate PS due to its good water-solubility and stability, low drug-resistance, high quantum yield of 1O2 and excellent antitumor efficacy in vitro and in vivo.

Clinical outcome and therapeutic impact on neuroendocrine neoplasms of the breast: a national cancer database study.

PURPOSE: Neuroendocrine neoplasms (NENs) of the breast are rare and not well-studied. NEN are subcategorized as well-differentiated neuroendocrine tumor (NET) and poorly differentiated neuroendocrine carcinoma (NEC). The objectives of the current study were to review the clinicopathologic features of NENs, therapeutic efficacy of current systemic therapy and clinical outcomes of NEN of the breast. METHODS: Between 2004 and 2015, 420 NET, 205 NEC, 146 Adenocarcinoma with NE differentiation (ACNED) and 1,479,520 of invasive carcinoma, not otherwise specified (IC-NOS) of the breast were identified in the National Caner Database. Overall survival was compared among groups using Kaplan-Meier method and Log-rank test. Multivariate analyses were performed to identify prognostic factors. RESULTS: After adjusting for other prognostic factors, both NET and NEC of the breast showed significantly worse OS than IC-NOS (HR (95% CI) = 1.41 (1.17, 1.72), p = 0.005 and HR (95% CI) = 2.11 (1.67, 2.67), p < 0.001, respectively). Both NET and NEC benefited from endocrine therapy if the tumors were hormonal receptor positive (median OS for treated with vs without: 125 vs 57 months in NET, not reached vs 29 months in NEC). NEC also benefited from chemotherapy (median OS for treated with vs without: 42 vs 34 months), but not NET. CONCLUSION: NEN is a unique pathologic and clinical entity, which has worse clinical outcome compared to IC-NOS of the breast. Current therapeutics used in the treatment of IC-NOS improve, but do not fully mitigate, the poorer prognosis of NEN patients. More effective therapy for patients with this unique tumor type are needed.

Combining the tumor-stroma ratio with tumor-infiltrating lymphocytes improves the prediction of pathological complete response in breast cancer patients.

PURPOSE: The tumor-stroma ratio (TSR) is a common histological parameter that measures stromal abundance and is prognostic in breast cancer (BC). However, more evidence is needed on the predictive value of the TSR for the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). The purpose of this study was to determine the importance of the TSR in predicting pCR in NAC settings. METHOD: We evaluated the TSR on pretreatment biopsies of 912 BC patients from four independent Chinese hospitals and investigated the potential value of the TSR for predicting pCR. Meanwhile, stromal tumor-infiltrating lymphocytes (sTILs) were assessed, and we evaluated the predictive value of the combination of sTILs and TSR (TSRILs). RESULTS: Patients with low stroma showed a higher pCR rate than those with high stroma among the four independent hospitals, and in multivariate analysis, the TSR was proven to be an independent predictor for pCR to NAC with an odds ratio of 1.945 (95% CI 1.230-3.075, P = 0.004). Moreover, we found that TSRILs could improve the area under the curve (AUC) for predicting pCR from 0.750 to 0.785 (P = 0.039); especially in HER2-negative BCs, the inclusion of TSRILs increased the AUC from 0.801 to 0.835 in the discovery dataset (P = 0.048) and 0.734 to 0.801 in the validation dataset (P = 0.003). CONCLUSION: TSR and sTILs can be easily measured in pathological routines and provide predictive information without additional cost; with more evidence from clinical trials, TSRILs could be a candidate to better stratify patients in NAC settings.

Prediction of coexisting invasive carcinoma on ductal carcinoma in situ (DCIS) lesions by mass spectrometry imaging.

Due to limited biopsy samples, ~20% of DCIS lesions confirmed by biopsy are upgraded to invasive ductal carcinoma (IDC) upon surgical resection. Avoiding underestimation of IDC when diagnosing DCIS has become an urgent challenge in an era discouraging overtreatment of DCIS. In this study, the metabolic profiles of 284 fresh frozen breast samples, including tumor tissues and adjacent benign tissues (ABTs) and distant surrounding tissues (DSTs), were analyzed using desorption electrospray ionization-mass spectrometry (DESI-MS) imaging. Metabolomics analysis using DESI-MS data revealed significant differences in metabolite levels, including small-molecule antioxidants, long-chain polyunsaturated fatty acids (PUFAs) and phospholipids between pure DCIS and IDC. However, the metabolic profile in DCIS with invasive carcinoma components clearly shifts to be closer to adjacent IDC components. For instance, DCIS with invasive carcinoma components showed lower levels of antioxidants and higher levels of free fatty acids compared to pure DCIS. Furthermore, the accumulation of long-chain PUFAs and the phosphatidylinositols (PIs) containing PUFA residues may also be associated with the progression of DCIS. These distinctive metabolic characteristics may offer valuable indications for investigating the malignant potential of DCIS. By combining DESI-MS data with machine learning (ML) methods, various breast lesions were discriminated. Importantly, the pure DCIS components were successfully distinguished from the DCIS components in samples with invasion in postoperative specimens by a Lasso prediction model, achieving an AUC value of 0.851. In addition, pixel-level prediction based on DESI-MS data enabled automatic visualization of tissue properties across whole tissue sections. Summarily, DESI-MS imaging on histopathological sections can provide abundant metabolic information about breast lesions. By analyzing the spatial metabolic characteristics in tissue sections, this technology has the potential to facilitate accurate diagnosis and individualized treatment of DCIS by inferring the presence of IDC components surrounding DCIS lesions. © 2023 The Pathological Society of Great Britain and Ireland.

Near-Infrared II Hyperspectral Imaging Improves the Accuracy of Pathological Sampling of Multiple Cancer Types.

Pathological histology is the "gold standard" for clinical diagnosis of cancer. Incomplete or excessive sampling of the formalin-fixed excised cancer specimen will result in inaccurate histologic assessment or excessive workload. Conventionally, pathologists perform specimen sampling relying on naked-eye observation, which is subjective and limited by human perception. Precise identification of cancer tissue, size, and margin is challenging, especially for lesions with inconspicuous tumors. To overcome the limits of human eye perception (visible: 400-700 nm) and improve the sampling efficiency, in this study, we propose using a second near-infrared window (NIR-II: 900-1700 nm) hyperspectral imaging (HSI) system to assist specimen sampling on the strength of the verified deep anatomical penetration and low scattering characteristics of the NIR-II optical window. We used selected NIR-II HSI narrow bands to synthesize color images for human eye observation and also applied a machine learning-based algorithm on the complete NIR-II HSI data for automatic tissue classification to assist pathologists in specimen sampling. A total of 92 tumor samples were collected, including 7 types. Sixty-two (62/92) samples were used as the validation set. Five experienced pathologists marked the contour of the cancer tissue on conventional color images by using different methods, and compared it with the "gold standard," showing that NIR-II HSI-assisted methods had significant improvements in determining cancer tissue compared with conventional methods (conventional color image with or without X-ray). The proposed system can be easily integrated into the current workflow, with high imaging efficiency and no ionizing radiation. It may also find applications in intraoperative detection of residual lesions and identification of different tissues.

Glutamine synthetase-negative hepatocellular carcinoma has better prognosis and response to sorafenib treatment after hepatectomy.

BACKGROUND: Glutamine synthetase (GS) and arginase 1 (Arg1) are widely used pathological markers that discriminate hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma; however, their clinical significance in HCC remains unclear. METHODS: We retrospectively analyzed 431 HCC patients: 251 received hepatectomy alone, and the other 180 received sorafenib as adjuvant treatment after hepatectomy. Expression of GS and Arg1 in tumor specimens was evaluated using immunostaining. mRNA sequencing and immunostaining to detect progenitor markers (cytokeratin 19 [CK19] and epithelial cell adhesion molecule [EpCAM]) and mutant TP53 were also conducted. RESULTS: Up to 72.4% (312/431) of HCC tumors were GS positive (GS+). Of the patients receiving hepatectomy alone, GS negative (GS-) patients had significantly better overall survival (OS) and recurrence-free survival (RFS) than GS+ patients; negative expression of Arg1, which is exclusively expressed in GS- hepatocytes in the healthy liver, had a negative effect on prognosis. Of the patients with a high risk of recurrence who received additional sorafenib treatment, GS- patients tended to have better RFS than GS+ patients, regardless of the expression status of Arg1. GS+ HCC tumors exhibit many features of the established proliferation molecular stratification subtype, including poor differentiation, high alpha-fetoprotein levels, increased progenitor tumor cells, TP53 mutation, and upregulation of multiple tumor-related signaling pathways. CONCLUSIONS: GS- HCC patients have a better prognosis and are more likely to benefit from sorafenib treatment after hepatectomy. Immunostaining of GS may provide a simple and applicable approach for HCC molecular stratification to predict prognosis and guide targeted therapy.

A deep learning model for lymph node metastasis prediction based on digital histopathological images of primary endometrial cancer.

Background: The current study aimed to develop a deep learning (DL) model for prediction of lymph node metastasis (LNM) based on hematoxylin and eosin (HE)-stained histopathological images of endometrial cancer (EC). The model was validated using external data. Methods: A total of 2,104 whole slide image (WSI) from 564 patients with pathologically confirmed LNM status were collated from West China Second University Hospital. An artificial intelligence (AI) model was built on the multiple instance-learning (MIL) framework for automatic prediction of the probability of LNM and its performance compared with "Mayo criteria". An additional external data source comprising 533 WSI was collected from two independent medical institutions to validate the model's robustness. Heatmaps were generated to demonstrate regions of the WSI that made the greatest contributions to the DL network output to improve understanding of these processes. Results: The proposed MIL model achieved an area under the curve (AUC) of 0.938, a sensitivity of 0.830 and a specificity of 0.911 for LNM prediction to EC. The AUC according to Mayo criteria was 0.666 for the same test dataset. For types I, II and mixed EC, AUCs were 0.927, 0.979 and 0.929, respectively. The predictive performance of the MIL model also achieved an AUC of 0.921 for early staging. In external validation data, the proposed model achieved an AUC of 0.770, a sensitivity of 0.814 and a specificity of 0.520 for LNM prediction. AUCs were 0.783 for type I and 0.818 for early stage EC. Conclusions: The proposed MIL model generated from histopathological images of EC has a much better LNM predictive performance than that of Mayo criteria. A novel DL-based biomarker trained on different histological subtypes of EC slides was revealed to predict metastatic status with improved accuracy, especially for early staging patients. The current study proves the concept of MIL-based prediction of LNM in EC for the first time, and brought a new sight to improve the accuracy of LNM prediction. Multicenter prospective validation data is required to further confirm the clinical utility.

High Dynamic Range Dual-Modal White Light Imaging Improves the Accuracy of Tumor Bed Sampling After Neoadjuvant Therapy for Breast Cancer.

OBJECTIVES: Accurate evaluation of residual cancer burden remains challenging because of the lack of appropriate techniques for tumor bed sampling. This study evaluated the application of a white light imaging system to help pathologists differentiate the components and location of tumor bed in specimens. METHODS: The high dynamic range dual-mode white light imaging (HDR-DWI) system was developed to capture antiglare reflection and multiexposure HDR transmission images. It was tested in 60 specimens of modified radical mastectomy after neoadjuvant therapy. We observed the differential transmittance among tumor tissue, fibrosis tissue, and adipose tissue. RESULTS: The sensitivity and specificity of HDR-DWI were compared with x-ray or visual examination to determine whether HDR-DWI was superior in identifying tumor beds. We found that tumor tissue had lower transmittance (0.12 ± 0.03) than fibers (0.15 ± 0.04) and fats (0.27 ± 0.07) (P < .01). CONCLUSIONS: HDR-DWI was more sensitive in identifying fiber and tumor tissues than cabinet x-ray and visual observation (P < .01). In addition, HDR-DWI could identify more fibrosis areas than the currently used whole slide imaging did in 12 samples (12/60). We have determined that HDR-DWI can provide more in-depth tumor bed information than x-ray and visual examination do, which will help prevent diagnostic errors in tumor bed sampling.

Regulated cell death in cancer: from pathogenesis to treatment.

ABSTRACT: Regulated cell death (RCD), including apoptosis, pyroptosis, necroptosis, and ferroptosis, is regulated by a series of evolutionarily conserved pathways, and is required for development and tissue homeostasis. Based on previous genetic and biochemical explorations of cell death subroutines, the characteristics of each are generally considered distinctive. However, recent in-depth studies noted the presence of crosstalk between the different forms of RCD; hence, the concept of PANoptosis appeared. Cancer, a complex genetic disease, is characterized by stepwise deregulation of cell apoptosis and proliferation, with significant morbidity and mortality globally. At present, studies on the different RCD pathways, as well as the intricate relationships between different cell death subroutines, mainly focus on infectious diseases, and their roles in cancer remain unclear. As cancers are characterized by dysregulated cell death and inflammatory responses, most current treatment strategies aim to selectively induce cell death via different RCD pathways in cancer cells. In this review, we describe five types of RCD pathways in detail with respect to tumorigenesis and cancer progression. The potential value of some of these key effector molecules in tumor diagnosis and therapeutic response has also been raised. We then review and highlight recent progress in cancer treatment based on PANoptosis and ferroptosis induced by small-molecule compounds, immune checkpoint inhibitors, and nanoparticles. Together, these findings may provide meaningful evidence to fill in the gaps between cancer pathogenesis and RCD pathways to develop better cancer therapeutic strategies.

Predicting neoadjuvant chemotherapy benefit using deep learning from stromal histology in breast cancer.

Neoadjuvant chemotherapy (NAC) is a standard treatment option for locally advanced breast cancer. However, not all patients benefit from NAC; some even obtain worse outcomes after therapy. Hence, predictors of treatment benefit are crucial for guiding clinical decision-making. Here, we investigated the predictive potential of breast cancer stromal histology via a deep learning (DL)-based approach and proposed the tumor-associated stroma score (TS-score) for predicting pathological complete response (pCR) to NAC with a multicenter dataset. The TS-score was demonstrated to be an independent predictor of pCR, and it not only outperformed the baseline variables and stromal tumor-infiltrating lymphocytes (sTILs) but also significantly improved the prediction performance of the baseline variable-based model. Furthermore, we discovered that unlike lymphocytes, collagen and fibroblasts in the stroma were likely associated with a poor response to NAC. The TS-score has the potential to better stratify breast cancer patients in NAC settings.

Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer：A multicenter study.

INTRODUCTION: Predicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic information of the tumor, which is important for therapeutic response effectiveness. In this study, we aimed to investigate whether predictive information for pCR could be detected from WSIs. MATERIALS AND METHODS: We retrospectively collected data from four cohorts of 874 patients diagnosed with biopsy-proven breast cancer. A deep learning pathological model (DLPM) was constructed to predict pCR using biopsy WSIs in the primary cohort, and it was then validated in three external cohorts. The DLPM could generate a deep learning pathological score (DLPs) for each patient; stromal tumor-infiltrating lymphocytes (TILs) were selected for comparison with DLPs. RESULTS: The WSI feature-based DLPM showed good predictive performance with the highest area under the curve (AUC) of 0.72 among the cohorts. Alternatively, the combination of the DLPM and clinical characteristics offered a better prediction performance (AUC >0.70) in all cohorts. We also evaluated the performance of DLPM in three different breast subtypes with the best prediction for the triple-negative breast cancer (TNBC) subtype (AUC: 0.73). Moreover, DLPM combined with clinical characteristics and stromal TILs achieved the highest AUC in the primary cohort (AUC: 0.82) and validation cohort 1 (AUC: 0.80). CONCLUSION: Our study suggested that WSIs integrated with deep learning could potentially predict pCR to NAC in breast cancer. The predictive performance will be improved by combining clinical characteristics. DLPs from DLPM can provide more information compared to stromal TILs for pCR prediction.

Apelin-mediated deamidation of HMGA1 promotes tumorigenesis by enhancing SREBP1 activity and lipid synthesis.

Enhanced fatty acid synthesis provides proliferation and survival advantages for tumor cells. Apelin is an adipokine, which serves as a ligand of G protein-coupled receptors that promote tumor growth in malignant cancers. Here, we confirmed that apelin increased sterol regulatory element-binding protein 1 (SREBP1) activity and induced the expression of glutamine amidotransferase for deamidating high-mobility group A 1 (HMGA1) to promote fatty acid synthesis and proliferation of lung cancer cells. This post-translational modification stabilized the HMGA1 expression and enhanced the formation of the apelin-HMGA1-SREBP1 complex to facilitate SREBP1 activity for lipid metabolism and lung cancer cell growth. We uncovered the pivotal role of apelin-mediated deamidation of HMGA1 in lipid metabolism and tumorigenesis of lung cancer cells.

Application of deep learning to identify ductal carcinoma in situ and microinvasion of the breast using ultrasound imaging.

Background: The treatment and prognosis of breast ductal carcinoma in situ (DCIS) with and without microinvasion (MIC) are different. Ultrasound imaging shows that DCIS is a heterogeneous breast tumor with diverse manifestations. DCIS means that the cancer cells are confined in the duct without penetrating the basement membrane, MIC means that the cancer cells penetrate the basement membrane and the maximum diameter of any largest invasive lesion is less than or equal to 1 mm. This study was designed to evaluate how deep learning can be used to identify DCIS with MIC on ultrasound images. Methods: The clinical and ultrasound data of 467 consecutive inpatients diagnosed with DCIS (213 with MIC) in West China Hospital of Sichuan University were collected from January 2013 to April 2019 and randomly apportioned to training and internal validation sets. An external validation set comprised data from Sichuan Provincial People's Hospital with 101 patients (33 with MIC) collected between January 2017 and December 2019. There were 2,492 original images; 66% of these were used to establish a model, and the remaining 34% were used to evaluate the model. Three experienced breast ultrasound clinicians analyzed the ultrasound images to establish a logistic regression model. Finally, the logistic regression model and five deep learning models (ResNet-50, ResNet-101, DenseNet-161, DenseNet-169, and Inception-v3) were compared and evaluated to assess their diagnostic efficiency when identifying MIC based on ultrasound image data. Results: The characteristics of high nuclear grade (P<0.001), necrosis (P=0.006), estrogen receptor negative (ER-; P=0.003), progesterone receptor negative (PR-; P=0.001), human epidermal growth factor receptor 2 positive (HER2+; P=0.034), lymphatic metastasis (P=0.008), and calcification (P<0.001) all showed significant correlations with MIC. The Inception-v3 model achieved the best performance (P<0.05) in MIC identification. The area under the receiver operating curve (AUC) of the Inception-v3 model was 0.803 [95% confidence interval (CI): 0.709 to 0.878], with a classification accuracy of 0.766, a sensitivity of 0.767, and a specificity of 0.765. Conclusions: Deep learning can be used to identify MIC of breast DCIS from ultrasound images. Models based on Inception-v3 can provide automated detection of DCIS with MIC from ultrasound images.