Metaplastic breast carcinoma (MBC) is a rare heterogeneous group of primary breast malignancies, with low hormone receptor expression and poor outcomes. To date, no prognostic markers for this tumor have been validated. The current study was undertaken to evaluate the clinicopathologic characteristics, the response to various therapeutic regimens and the prognosis of MBCs in a large cohort of patients from Tianjin Medical University Cancer Hospital in China. Ninety cases of MBCs diagnosed in our hospital between January 2000 and September 2014 were retrieved from the archives. In general, MBCs presented with larger size, a lower rate of lymph node metastasis, and demonstrated more frequent local recurrence/distant metastasis than 1,090 stage-matched cases of invasive carcinoma of no specific type (IDC-NST), independent of the status of estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 expressions. The five-year disease-free survival (DFS) of MBC was significantly worse than IDC-NST. Using univariate analysis, lymph node metastasis, advanced clinical stage at diagnosis, high tumor proliferation rate assessed by Ki-67 labeling, and epidermal growth factor receptor (EGFR) overexpression/gene amplification were associated significantly with reduced DFS, while decreased OS was associated significantly with lymph node metastasis and EGFR overexpression/gene amplification. With multivariate analysis, lymph node status was an independent predictor for DFS, and lymph node status and EGFR overexpression/gene amplification were independent predictors for OS. Histologic subtyping and molecular subgrouping of MBCs were not significant factors in prognosis. We also found that MBCs were insensitive to neoadjuvant chemotherapy, routine chemotherapy, and radiation therapy. This study indicates that MBC is an aggressive type of breast cancer with poor prognosis, and that identification and optimization of an effective comprehensive therapeutic regimen is needed.

BackgroundN6-methyladensine (m6A) is a common and abundant RNA methylation modification found in various species. As a type of post-transcriptional methylation, m6A plays an important role in diverse RNA activities such as alternative splicing, an interplay with microRNAs and translation efficiency. Although existing tools can predict m6A at single-base resolution, it is still challenging to extract the biological information surrounding m6A sites.ResultsWe implemented a deep learning framework, named DeepM6ASeq, to predict m6A-containing sequences and characterize surrounding biological features based on miCLIP-Seq data, which detects m6A sites at single-base resolution. DeepM6ASeq showed better performance as compared to other machine learning classifiers. Moreover, an independent test on m6A-Seq data, which identifies m6A-containing genomic regions, revealed that our model is competitive in predicting m6A-containing sequences. The learned motifs from DeepM6ASeq correspond to known m6A readers. Notably, DeepM6ASeq also identifies a newly recognized m6A reader: FMR1. Besides, we found that a saliency map in the deep learning model could be utilized to visualize locations of m6A sites.ConculsionWe developed a deep-learning-based framework to predict and characterize m6A-containing sequences and hope to help investigators to gain more insights for m6A research. The source code is available at https://github.com/rreybeyb/DeepM6ASeq.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2516-4) contains supplementary material, which is available to authorized users.

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Background: Due to different treatment method and prognosis of different subtypes of lung adenocarcinomas appearing as ground-glass nodules (GGNs) on computed tomography (CT) scan, it is important to classify invasive adenocarcinomas from non-invasive adenocarcinomas. The purpose of this paper is to build and evaluate the performance of deep learning networks on the differentiation the invasiveness of lung adenocarcinoma appearing as GGNs. Methods: This retrospective study included 886 GGNs from 794 pathological confirmed patients with lung adenocarcinoma for training and testing the proposed networks. Three deep learning networks, namely XimaNet (deep learning-based classification model), XimaSharp (classification and nodule segmentation model), and Deep-RadNet (deep learning and radiomics combined classification model, i.e., deep radiomics) were built. Three classification tasks, namely task 1: classification of AAH/AIS and MIA, task 2: classification of MIA and IAC, and task 3: classification of non-invasive adenocarcinomas and invasive adenocarcinomas (AAH/AIS&MIA and IAC) were conducted to evaluate the model performance. The Z-test was used to compare the model performance. Results: The AUC for classification of AAH/AIS with MIA were 0.891, 0.841 and 0.779 for Deep-RadNet, XimaNet and XimaSharp respectively. The AUC for classification of MIA with IAC were 0.889, 0.785 and 0.778 for three networks and AUC for classification of AAH/AIS&MIA with IAC were 0.941, 0.892 and 0.827 respectively. The performance of deep_RadNet was better than the other two models with the Z-test (P<0.05). Conclusions: Deep-RadNet with the visual heat map could evaluate the invasiveness of GGNs accurately and intuitively, providing a theoretical basis for individualized and accurate medical treatment of patients with GGNs.