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.

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.

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.

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.

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.

KRT13 promotes stemness and drives metastasis in breast cancer through a plakoglobin/c-Myc signaling pathway.

BACKGROUND: Keratins (KRTs) are intermediate filament proteins that interact with multiple regulatory proteins to initiate signaling cascades. Keratin 13 (KRT13) plays an important role in breast cancer progression and metastasis. The objective of this study is to elucidate the mechanism by which KRT13 promotes breast cancer growth and metastasis. METHODS: The function and mechanisms of KRT13 in breast cancer progression and metastasis were assessed by overexpression and knockdown followed by examination of altered behaviors in breast cancer cells and in xenograft tumor formation in mouse mammary fat pad. Human breast cancer specimens were examined by immunohistochemistry and multiplexed quantum dot labeling analysis to correlate KRT13 expression to breast cancer progression and metastasis. RESULTS: KRT13-overexpressing MCF7 cells displayed increased proliferation, invasion, migration and in vivo tumor growth and metastasis to bone and lung. Conversely, KRT13 knockdown inhibited the aggressive behaviors of HCC1954 cells. At the molecular level, KRT13 directly interacted with plakoglobin (PG, γ-catenin) to form complexes with desmoplakin (DSP). This complex interfered with PG expression and nuclear translocation and abrogated PG-mediated suppression of c-Myc expression, while the KRT13/PG/c-Myc signaling pathway increased epithelial to mesenchymal transition and stem cell-like phenotype. KRT13 expression in 58 human breast cancer tissues was up-regulated especially at the invasive front and in metastatic specimens (12/18) (p < 0.05). KRT13 up-regulation in primary breast cancer was associated with decreased overall patient survival. CONCLUSIONS: This study reveals that KRT13 promotes breast cancer cell growth and metastasis via a plakoglobin/c-Myc pathway. Our findings reveal a potential novel pathway for therapeutic targeting of breast cancer progression and metastasis.

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.

Biomarker profile of invasive lobular carcinoma: pleomorphic versus classic subtypes, clinicopathological characteristics and prognosis analyses.

PURPOSE: To compare the clinicopathologic features and prognosis of pleomorphic invasive lobular carcinoma (P-ILC) and classic ILC (C-ILC) according to the biomarker profile. METHODS: A total of 667 C-ILCs and 133 P-ILCs between 2011 and 2021 were included. Clinicopathologic features and stromal tumor-infiltrating lymphocytes (sTILs) status were evaluated. P-ILCs were divided into subtypes based on ER/PR and HER2 expression. The overall survival and disease-free survival (DFS) of patients were compared among matched P-ILCs, C-ILCs, and invasive ductal carcinomas (IDCs) with biomarker subtypes. RESULTS: Compared to C-ILCs, P-ILCs had greater tumor sizes and stages, fewer ER-positive, more HER2-positive, triple negative (TN), and Ki-67 > 20% tumors (P < 0.05). P-ILCs were subdivided into ER+ (63.1%), HER2+ (21.1%) and TN (15.8%). ER+ P-ILCs were mainly showed trabecular and solid growth patterns. Apocrine and solid features were more strongly associated with HER2+ P-ILCs and TN-P-ILCs, respectively. The prognosis of each biomarker group (ER+, HER2+ and TN) differed by subtype. The P-ILC biomarker subtypes had worse prognosis than the same subtypes in the IDC group, while there was no difference between the P-ILC and the C-ILC counterparts. Solid variants of P-ILC had the worst prognosis. Bone was the most common metastatic site in ER+ P-ILCs and TN-P-ILCs. HER2+ P-ILCs tended to metastasize to the brain and liver. DFS of HER2+ P-ILCs and TN-P-ILCs were worse than that of ER+ P-ILCs. Lacking lobular carcinoma in situ and sTILs ≤ 10% were associated with worse survival of ER+ P-ILCs and TN-P-ILCs, respectively. For HER2+ P-ILCs, Ki-67 > 20% and sTILs ≤ 10% were significant factors for lower DFS. CONCLUSION: P-ILCs is an aggressive subtype of ILCs. Analyzing the prognostic factors of P-ILCs with heterogeneous morphological and biomarker characteristics is helpful for creating an individualized treatment.

Clinical and genomic analyses of neuroendocrine neoplasms of the breast.

Breast neuroendocrine neoplasms (NENs) constitute a rare histologic subtype that includes both neuroendocrine tumors (NETs) and neuroendocrine carcinomas (NECs). In this study, we aimed to gain insight into the clinical and molecular characteristics of NENs of the breast. NEN and paired distant normal fresh tissues and clinicopathological data were obtained from 17 patients with NENs, and clinicopathological data were collected from 755 patients with invasive breast carcinomas of no special type (IBCs-NST). We compared the clinicopathological characteristics of NENs and IBCs-NST and performed whole-exome sequencing (WES) of both NEN and paired normal tissues. Compared with the IBC-NST patients, the NEN patients had a higher mean age, lower clinical stage, and lower pathological nodal (pN) stage (P < 0.001, P < 0.001, and P = 0.017, respectively). The most frequently mutated gene in NENs was KMT2C (3/17, 17.6%). NENs had copy number variations (CNVs) of 8q, 11q, and 17q amplification and 17q and 11q deletion and harbored the following specific genes related to tumorigenesis: (i) suppressor genes with loss of heterozygosity (LOH) such as ACE (2/17, 11.8%); (ii) tumor driver genes such as GATA3 (2/17, 11.8%); and (iii) susceptibility genes such as MAP3K4 (17/17, 100%) and PDE4DIP (17/17, 100%). The oncogenic/likely oncogenic mutations of NETs in PI3K pathway genes (50.0%, 18.2%; P < 0.001) and MAPK signaling pathway genes (83.3%, 18.2%; P = 0.035) affected higher proportions than those of NECs. In conclusion, this study provides certain clinical and molecular evidence supporting NENs as a distinct subtype of breast cancer and provides some potential molecular features for distinguishing NETs from NECs.

Brahma-Related Gene 1 Inhibition Prevents Liver Fibrosis and Cholangiocarcinoma by Attenuating Progenitor Expansion.

BACKGROUND AND AIMS: Intrahepatic cholangiocarcinoma (iCCA) is closely correlated with hepatic progenitor cell (HPC) expansion and liver fibrosis. Brahma-related gene 1 (Brg1), an enzymatic subunit of the switch/sucrose nonfermentable complex that is critical in stem cell maintenance and tumor promotion, is prominently up-regulated in both HPCs and iCCA; however, its role in this correlation remains undefined. APPROACH AND RESULTS: A retrospective cohort study indicated that high Brg1 expression suggests poor prognosis in patients with iCCA. In chronically injured livers induced by a 0.1% 3,5-diethoxycarbonyl-1,4-dihydrocollidine diet or bile duct ligation surgery, HPCs were dramatically activated, as indicated by their enhanced expression of Brg1 and a subset of stem cell markers; however, Brg1 ablation in HPCs strongly suppressed HPC expansion and liver fibrosis. Furthermore, in a chemically induced iCCA model, inhibition of Brg1 by a specific inhibitor or inducible gene ablation markedly improved histology and suppressed iCCA growth. Mechanistically, in addition to transcriptionally promoting both Wnt receptor genes and target genes, Brg1 was found to bind to the ß-catenin/transcription factor 4 transcription complex, suggesting a possible approach for regulation of Wnt/ß-catenin signaling. CONCLUSIONS: We have demonstrated the function of Brg1 in promoting HPC expansion, liver cirrhosis, and, ultimately, iCCA development in chronically injured livers, which is largely dependent on Wnt/ß-catenin signaling. Our data suggest that therapies targeting Brg1-expressing HPCs are promising for the treatment of liver cirrhosis and iCCA.

Correlation between CA12 and TFF3 and their prediction value of neoadjuvant chemotherapy response in breast cancer.

WHAT IS KNOWN AND OBJECTIVE: Compared with other molecular subtypes, hormone receptor-positive breast cancer often shows worse neoadjuvant chemotherapy efficacy. This study aims to explore the relationship between the oestrogen receptor (ER)-related genes carbonic anhydrase 12 (CA12) and trefoil factor 3 (TFF3) and their predictive value of neoadjuvant chemotherapy for breast cancer. METHODS: We investigated the relationships between CA12, TFF3 and ER status and their predictive value of anthracycline-taxane neoadjuvant chemotherapy in 115 female breast cancer patients via real-time polymerase chain reaction (RT-PCR) and 4 GEO datasets: GSE41998, GSE25065, GSE20194 and GSE20271. Then, the effects of CA12 and TFF3 on the chemotherapy drugs doxorubicin and docetaxel were verified in vitro in the breast cancer cell lines MCF-7 and BT474. RESULTS AND DISCUSSION: The GEO datasets and RT-PCR results showed that the relative expression of both CA12 and TFF3 was higher in oestrogen receptor-positive samples compared with the other samples (p < 0.05). CA12 was significantly correlated with TFF3 (p < 0.05). In MCF-7 cells, inhibition of TFF3 induced downregulation of CA12 and ESR1 (p < 0.05) at both the mRNA and the protein levels, while inhibition of CA12 also downregulated TFF3 and ESR1 (p < 0.05). In BT474 cells, inhibition of TFF3 downregulated CA12 and ESR1 (p < 0.05) at both the mRNA and the protein levels, while inhibition of CA12 led to slight upregulation of TFF3 and ESR1 (p > 0.05). Moreover, GEO datasets and RT-PCR results showed that CA12 and TFF3 were more highly expressed in nonpathological complete response (non-pCR) samples than in pCR samples (p < 0.05). Cell viability assays of MCF-7 and BT474 cells showed that inhibiting CA12 and TFF3 could enhance sensitivity to doxorubicin and docetaxel (p < 0.05). WHAT IS NEW AND CONCLUSION: CA12 and TFF3 were correlated with each other, and their high expression might explain the worse efficacy of neoadjuvant chemotherapy in oestrogen receptor-positive breast cancer patients.

[Performance Comparison of Two Cryosection Embedding Agents Used for Desorption Electrospray Ionization Mass Spectrometry Imaging].

Objective: To evaluate the potential effect of embedding with carboxylmethyl cellulose (CMC) and embedding with optimal cutting temperature (OCT) compound followed by washing with PBS (OCT-W) on the analysis of breast cancer tissue samples with desorption electrospray ionization mass spectrometry imaging (DESI-MSI). Methods: DESI-MSI of fresh frozen (FF) tissue samples, OCT-embedded samples, CMC-embedded samples, and OCT-W samples from the same breast cancer tumor tissue were performed. The ratio of maximum abundance ion was used to assess the reproducibility of DESI-MSI analysis. In addition, the effects of the treatment of each group were examined by comparing the characteristic ion species and the ion signal intensity detected by DESI-MSI. Results: DESI-MSI of continuous sections of FF samples showed that the coefficient of variation (CV) of the pair-to-pair ratios of m/ z 281.25, m/ z 309.28 and m/ z 279.23 ions, the three ions with the highest intensity in the tumor region, were 19.61%, 20.74% and 10.18%, respectively. The characteristic ion species detected by DESI-MSI of CMC embedded tissue and the OCT-W tissue were almost the same, compared with those of the FF tumor tissue. However, ion species detected in OCT embedded samples were less than 50% of the FF samples. In terms of ion signal intensity, the CMC embedded tissue was not affected overall, while the signal of most of the characteristic ions of the OCT-W group showed decreased intensity (P<0.05). Conclusion: FF tissue sections and CMC-embedded samples can be used for DESI-MSI routine analysis. OCT embedding affects the feasibility of sample analysis whether or not the sample undergoes washing with PBS. CMC embedding agent is recommended if the tissue sections need to be fixated and supported due to small sample size, fragility, or other problems.

Mediator complex subunit 16 is down-regulated in papillary thyroid cancer, leading to increased transforming growth factor-ß signaling and radioiodine resistance.

Mediator complex subunit 16 (MED16) is a component of the mediator complex and functions as a coactivator in transcriptional events at almost all RNA polymerase II-dependent genes. In this study, we report that the expression of MED16 is markedly decreased in papillary thyroid cancer (PTC) tumors compared with normal thyroid tissues. In vitro, MED16 overexpression in PTC cells significantly inhibited cell migration, enhanced sodium/iodide symporter expression and iodine uptake, and decreased resistance to radioactive 131I (RAI). Conversely, PTC cells in which MED16 had been further knocked down (MED16KD) exhibited enhanced cell migration, epithelial-mesenchymal transition, and RAI resistance, accompanied by decreased sodium/iodide symporter levels. Moreover, cell signaling through transforming growth factor ß (TGF-ß) was highly activated after the MED16 knockdown. Similar results were obtained in MED12KD PTC cells, and a co-immunoprecipitation experiment verified interactions between MED16 and MED12 and between MED16 and TGF-ßR2. Of note, the application of LY2157299, a potent inhibitor of TGF-ß signaling, significantly attenuated MED16KD-induced RAI resistance both in vitro and in vivo In conclusion, our findings indicate that MED16 reduction in PTC contributes to tumor progression and RAI resistance via the activation of the TGF-ß pathway.

Validation of prognostic significance of the proposed uniform classification framework in neuroendocrine neoplasms of the breast.

PURPOSE: A uniform classification framework for neuroendocrine neoplasms (NENs) in all the organ systems has been recently proposed by an International Agency for Research on Cancer (IARC) and World Health Organization (WHO) expert panel. Based on the new classification system, the NENs of the breast are divided into well-differentiated neuroendocrine tumors (NETs) and poorly differentiated neuroendocrine carcinomas (NECs). This study is aimed to analysis the prognostic differences between NENs and invasive ductal carcinomas of no special type (IDCs-NST). METHODS: The surveillance, epidemiology, and end results (SEER) database released on November 2018 was used for this study. Between 2003 and 2016, 361 NENs (NET = 239, NEC = 122) of the breast and 491,908 of IDCs-NST were identified. Survival analysis was performed for disease-specific survival (DSS) and overall survival (OS). RESULTS: The 5-year DSS of NET, NEC, and IDC-NST was 63.39%, 46.00%, and 89.17%, respectively. And the 5-year OS of NET, NEC, and IDC-NST was 55.66%, 38.87%, and 83.17%, respectively. Within the same clinical stage or grade, NETs and NECs of the breast had worse DSS and OS than corresponding stage or grade IDCs-NST (all P < 0.050). In univariate and multivariate survival analysis, NENs of the breast had significantly worse DSS and OS than IDCs-NST (P < 0.001). CONCLUSION: The universal classification framework for NEN allowed us to further refine the breast carcinoma with neuroendocrine differentiation as a unique pathologic and clinical entity, which has worse clinical outcome compared to IDC-NST.

Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer.

BACKGROUND: Pathological complete response (pCR) is considered a surrogate endpoint for favorable survival in breast cancer patients treated with neoadjuvant chemotherapy (NAC). Predictive biomarkers of treatment response are crucial for guiding treatment decisions. With the hypothesis that histological information on tumor biopsy images could predict NAC response in breast cancer, we proposed a novel deep learning (DL)-based biomarker that predicts pCR from images of hematoxylin and eosin (H&E)-stained tissue and evaluated its predictive performance. METHODS: In total, 540 breast cancer patients receiving standard NAC were enrolled. Based on H&E-stained images, DL methods were employed to automatically identify tumor epithelium and predict pCR by scoring the identified tumor epithelium to produce a histopathological biomarker, the pCR-score. The predictive performance of the pCR-score was assessed and compared with that of conventional biomarkers including stromal tumor-infiltrating lymphocytes (sTILs) and subtype. RESULTS: The pCR-score derived from H&E staining achieved an area under the curve (AUC) of 0.847 in predicting pCR directly, and achieved accuracy, F1 score, and AUC of 0.853, 0.503, and 0.822 processed by the logistic regression method, respectively, higher than either sTILs or subtype; a prediction model of pCR constructed by integrating sTILs, subtype and pCR-score yielded a mean AUC of 0.890, outperforming the baseline sTIL-subtype model by 0.051 (0.839, P = 0.001). CONCLUSION: The DL-based pCR-score from histological images is predictive of pCR better than sTILs and subtype, and holds the great potentials for a more accurate stratification of patients for NAC.

THE PRESENT AND FUTURE OF THE MASS SPECTROMETRY-BASED INVESTIGATION OF THE EXOSOME LANDSCAPE.

Exosomes are critical intercellular messengers released upon the fusion of multivesicular bodies with the cellular plasma membrane that deliver their cargo in the form of extracellular vesicles. Containing numerous nonrandomly packed functional proteins, lipids, and RNAs, exosomes are vital intercellular messengers that contribute to the physiologic processes of the healthy organism. During the post-genome era, exosome-oriented proteomics have garnered great interest. Since its establishment, mass spectrometry (MS) has been indispensable for the field of proteomics research and has advanced rapidly to interrogate biological samples at a higher resolution and sensitivity. Driven by new methodologies and more advanced instrumentation, MS-based approaches have revolutionized our understanding of protein biology. As the access to online proteomics database platforms has blossomed, experimental data processing occurs with more speed and accuracy. Here, we review recent advances in the technological progress of MS-based proteomics and several new detection strategies for MS-based proteomics research. We also summarize the use of integrated online databases for proteomics research in the era of big data. © 2020 John Wiley & Sons Ltd. Mass Spec Rev.

Prognostic Score-Based Stratification Analysis Reveals Universal Benefits of Radiotherapy on Lowering the Risk of Ipsilateral Breast Event for Ductal Carcinoma In Situ Patients with Different Risk Levels.

BACKGROUND: We aimed to analyze the effects of radiotherapy (RT) on the incidence rate of ipsilateral breast event (IBE) in ductal carcinoma in situ (DCIS) patients with lumpectomy after being stratified by prognostic score. METHODS: We identified DCIS patients who received lumpectomy, from the Surveillance, Epidemiology, and End Results (SEER) database from 1988 to 2015. Cumulative incidence functions for competing risk were used to evaluate the effects of RT on IBE risk over time. Three multivariate regression models (weighted, non-weighted, and Fine-Gray) were applied to compare the IBE risk between the RT and non-RT groups after stratifying patients by prognostic score. RESULTS: Overall, 72,623 DCIS patients were identified from the SEER database and 49,206 (66.8%) patients received RT. During the follow-up period (ranging from 7 to 347 months), the cumulative probability of invasive and in situ IBE was significantly lower in the RT group than in the non-RT group (p < 0.001). After being stratified by prognostic score, the weighted IBE incidence rate increased as the risk level increased (p < 0.050). In multivariate regression models, RT lowered the IBE incidence rate by at least 30% in low-, moderate-, and high-risk DCIS (p < 0.010). In particular, the in situ and invasive IBE incidence rate decreased by over 50% in low-risk DCIS with RT (p < 0.001). CONCLUSIONS: RT is associated with a lowered IBE incidence rate in DCIS patients, regardless of the assigned risk levels for patients. The significant reduction in the IBE incidence rate in low-risk DCIS patients also indicates the potential benefits for recommending RT to such a patient population in clinical practice.

Improving Ki67 assessment concordance by the use of an artificial intelligence-empowered microscope: a multi-institutional ring study.

AIMS: The nuclear proliferation biomarker Ki67 plays potential prognostic and predictive roles in breast cancer treatment. However, the lack of interpathologist consistency in Ki67 assessment limits the clinical use of Ki67. The aim of this article was to report a solution utilising an artificial intelligence (AI)-empowered microscope to improve Ki67 scoring concordance. METHODS AND RESULTS: We developed an AI-empowered microscope in which the conventional microscope was equipped with AI algorithms, and AI results were provided to pathologists in real time through augmented reality. We recruited 30 pathologists with various experience levels from five institutes to assess the Ki67 labelling index on 100 Ki67-stained slides from invasive breast cancer patients. In the first round, pathologists conducted visual assessment on a conventional microscope; in the second round, they were assisted with reference cards; and in the third round, they were assisted with an AI-empowered microscope. Experienced pathologists had better reproducibility and accuracy [intraclass correlation coefficient (ICC) = 0.864, mean error = 8.25%] than inexperienced pathologists (ICC = 0.807, mean error = 11.0%) in visual assessment. Moreover, with reference cards, inexperienced pathologists (ICC = 0.836, mean error = 10.7%) and experienced pathologists (ICC = 0.875, mean error = 7.56%) improved their reproducibility and accuracy. Finally, both experienced pathologists (ICC = 0.937, mean error = 4.36%) and inexperienced pathologists (ICC = 0.923, mean error = 4.71%) improved the reproducibility and accuracy significantly with the AI-empowered microscope. CONCLUSION: The AI-empowered microscope allows seamless integration of the AI solution into the clinical workflow, and helps pathologists to obtain higher consistency and accuracy for Ki67 assessment.

Design, synthesis and biological evaluation of imidazopyridazine derivatives containing isoquinoline group as potent MNK1/2 inhibitors.

Mitogen-activated protein kinase (MAPK)-interacting kinases (MNKs) are located at the meeting-point of ERK and p38 MAPK signaling pathways, which can phosphorylate eukaryotic translation initiation factor 4E (eIF4E) at the conserved serine 209 exclusively. MNKs modulate the translation of mRNA involved in tumor-associated signaling pathways. Consequently, selective inhibitors of MNK1/2 could reduce the level of phosphorylated eIF4E. Series of imidazopyrazines, imidazopyridazines and imidazopyridines derivatives were synthesized and evaluated as MNK1/2 inhibitors. Several compounds exhibited great inhibitory activity against MNK1/2 and selected compounds showed moderate to excellent anti-proliferative potency against diffuse large B-cell lymphoma (DLBCL) cell lines. In particular, compound II-5 (MNK1 IC50 = 2.3 nM; MNK2 IC50 = 3.4 nM) exhibited excellent enzymatic inhibitory potency and proved to be the most potent compound against TMD-8 and DOHH-2 cell lines with IC50 value of 0.3896 µM and 0.4092 µM respectively. These results demonstrated that compound II-5 could be considered as a potential MNK1/2 inhibitor for further investigation.

[New Trends of Development in Precision Pathological Diagnosis Promoted by Artificial Intelligence].

Precision pathological diagnosis plays a vital role in precision medicine. Both the limited resources available to pathologists and the incessant demands for further refinement and quantification of clinical diagnosis are posing new challenges for pathologists to meet the needs for precision pathological diagnosis. It is expected that artificial intelligence (AI) will be the powerful tool that will help find solutions to this problem from different angles. The author of this article elaborated on a number of ways in which AI can help promote precision pathological diagnosis, including AI-assisted precision extraction of tissue samples, AI-assisted precision histopathologic diagnosis, AI-assisted histological grading and quantitative scoring, AI-assisted precision assessment of tumor biomarkers, AI-assisted prediction of molecular features and precision interpretation of biological information based on hematoxylin-eosin (HE) stained images, the realization of in-depth precision diagnosis based on AI-assisted information integration, and AI-assisted accurate prediction of patient survival and prognosis based on HE-stained images. The paper presents to the readers the future of smart pathology that AI will help usher in.