Pub Date : 2023-05-03DOI: 10.48550/arXiv.2305.01968
M. Cen, Xingyu Li, Bangwei Guo, J. Jonnagaddala, Hong Zhang, Xuesong Xu
In digital pathology tasks, transformers have achieved state-of-the-art results, surpassing convolutional neural networks (CNNs). However, transformers are usually complex and resource intensive. In this study, we developed a novel and efficient digital pathology classifier called DPSeq, to predict cancer biomarkers through fine-tuning a sequencer architecture integrating horizon and vertical bidirectional long short-term memory (BiLSTM) networks. Using hematoxylin and eosin (H&E)-stained histopathological images of colorectal cancer (CRC) from two international datasets: The Cancer Genome Atlas (TCGA) and Molecular and Cellular Oncology (MCO), the predictive performance of DPSeq was evaluated in series of experiments. DPSeq demonstrated exceptional performance for predicting key biomarkers in CRC (MSI status, Hypermutation, CIMP status, BRAF mutation, TP53 mutation and chromosomal instability [CING]), outperforming most published state-of-the-art classifiers in a within-cohort internal validation and a cross-cohort external validation. Additionally, under the same experimental conditions using the same set of training and testing datasets, DPSeq surpassed 4 CNN (ResNet18, ResNet50, MobileNetV2, and EfficientNet) and 2 transformer (ViT and Swin-T) models, achieving the highest AUROC and AUPRC values in predicting MSI status, BRAF mutation, and CIMP status. Furthermore, DPSeq required less time for both training and prediction due to its simple architecture. Therefore, DPSeq appears to be the preferred choice over transformer and CNN models for predicting cancer biomarkers.
{"title":"DPSeq: A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers using Sequencer Architecture","authors":"M. Cen, Xingyu Li, Bangwei Guo, J. Jonnagaddala, Hong Zhang, Xuesong Xu","doi":"10.48550/arXiv.2305.01968","DOIUrl":"https://doi.org/10.48550/arXiv.2305.01968","url":null,"abstract":"In digital pathology tasks, transformers have achieved state-of-the-art results, surpassing convolutional neural networks (CNNs). However, transformers are usually complex and resource intensive. In this study, we developed a novel and efficient digital pathology classifier called DPSeq, to predict cancer biomarkers through fine-tuning a sequencer architecture integrating horizon and vertical bidirectional long short-term memory (BiLSTM) networks. Using hematoxylin and eosin (H&E)-stained histopathological images of colorectal cancer (CRC) from two international datasets: The Cancer Genome Atlas (TCGA) and Molecular and Cellular Oncology (MCO), the predictive performance of DPSeq was evaluated in series of experiments. DPSeq demonstrated exceptional performance for predicting key biomarkers in CRC (MSI status, Hypermutation, CIMP status, BRAF mutation, TP53 mutation and chromosomal instability [CING]), outperforming most published state-of-the-art classifiers in a within-cohort internal validation and a cross-cohort external validation. Additionally, under the same experimental conditions using the same set of training and testing datasets, DPSeq surpassed 4 CNN (ResNet18, ResNet50, MobileNetV2, and EfficientNet) and 2 transformer (ViT and Swin-T) models, achieving the highest AUROC and AUPRC values in predicting MSI status, BRAF mutation, and CIMP status. Furthermore, DPSeq required less time for both training and prediction due to its simple architecture. Therefore, DPSeq appears to be the preferred choice over transformer and CNN models for predicting cancer biomarkers.","PeriodicalId":77408,"journal":{"name":"The American journal of cardiovascular pathology","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79650690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-10DOI: 10.1101/2023.02.08.23285648
Yunus Saatchi, Parker Schanen, Richard A. Cheung, H. Petty
Although the existence of non-recurrent and recurrent forms of ductal carcinoma in situ (DCIS) of the breast are observed, no evidence-based test can make this distinction. This retrospective case-control study used archival DCIS samples stained with anti-phospho-Ser226-GLUT1 (glucose transporter type 1) and anti-phosphofructokinase type L (PFKL) antibodies. Immunofluorescence micrographs were used to create machine learning (ML) models of recurrent and non-recurrent biomarker patterns, which were evaluated in cross-validation studies. Clinical performance was assessed by holdout studies using patients whose data were not used in training. Micrographs were stratified by the recurrence probability of each image. Recurrent patients were defined by at least one image with a probability of recurrence >98% whereas non-recurrent patients had none. These studies demonstrated no false negatives, identified true positives, and uniquely identified true negatives. Roughly 20% of the microscope fields of recurrent lesions were computationally recurrent. Strong prognostic results were obtained for both Caucasian and African American women. Our machine tool provides the first means to accurately predict recurrent and non-recurrent patient outcomes. We suggest that at least some false positives were true positives that benefitted from surgical intervention. The intracellular locations of phospho-Ser226-GLUT1 and phosphofructokinase type L likely participate in cancer recurrences by accelerating glucose flux, a key feature of the Warburg Effect.
{"title":"Computer Vision Identifies Recurrent and Non-Recurrent Ductal Carcinoma in situ Lesions with Special Emphasis on African American Women","authors":"Yunus Saatchi, Parker Schanen, Richard A. Cheung, H. Petty","doi":"10.1101/2023.02.08.23285648","DOIUrl":"https://doi.org/10.1101/2023.02.08.23285648","url":null,"abstract":"Although the existence of non-recurrent and recurrent forms of ductal carcinoma in situ (DCIS) of the breast are observed, no evidence-based test can make this distinction. This retrospective case-control study used archival DCIS samples stained with anti-phospho-Ser226-GLUT1 (glucose transporter type 1) and anti-phosphofructokinase type L (PFKL) antibodies. Immunofluorescence micrographs were used to create machine learning (ML) models of recurrent and non-recurrent biomarker patterns, which were evaluated in cross-validation studies. Clinical performance was assessed by holdout studies using patients whose data were not used in training. Micrographs were stratified by the recurrence probability of each image. Recurrent patients were defined by at least one image with a probability of recurrence >98% whereas non-recurrent patients had none. These studies demonstrated no false negatives, identified true positives, and uniquely identified true negatives. Roughly 20% of the microscope fields of recurrent lesions were computationally recurrent. Strong prognostic results were obtained for both Caucasian and African American women. Our machine tool provides the first means to accurately predict recurrent and non-recurrent patient outcomes. We suggest that at least some false positives were true positives that benefitted from surgical intervention. The intracellular locations of phospho-Ser226-GLUT1 and phosphofructokinase type L likely participate in cancer recurrences by accelerating glucose flux, a key feature of the Warburg Effect.","PeriodicalId":77408,"journal":{"name":"The American journal of cardiovascular pathology","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87853603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-06DOI: 10.1101/2022.12.04.22283073
Joshua J. Levy, John Zavras, Eren M. Veziroglu, Mustafa Nasir-Moin, Fred W. Kolling, B. Christensen, L. Salas, Rachael E. Barney, Scott M. Palisoul, Bing Ren, Xiaoying Liu, Darcy A. Kerr, K. Pointer, G. Tsongalis, L. Vaickus
Over 150,000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually over 50,000 individuals will die from CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. CRC tumors are removed en bloc with surrounding vasculature and lymphatics. Examination of regional lymph nodes at the time of surgical resection is essential for prognostication. Developing alternative approaches to indirectly assess recurrence risk would have utility in cases where lymph node yield is incomplete or inadequate. Spatially dependent, immune cell-specific (e.g., Tumor Infiltrating Lymphocytes- TILs), proteomic, and transcriptomic expression patterns inside and around the tumor - the tumor immune microenvironment (TIME) - can predict nodal/distant metastasis and probe the coordinated immune response from the primary tumor site. The comprehensive characterization of TILs and other immune infiltrates is possible using highly multiplexed spatial omics technologies, such as the GeoMX Digital Spatial Profiler (DSP). In this study, machine learning and differential co-expression analyses helped identify biomarkers from DSP-assayed protein expression patterns inside, at the invasive margin, and away from the tumor, associated with extracellular matrix remodeling (e.g., GZMB, fibronectin), immune suppression (e.g., FOXP3), exhaustion and cytotoxicity (e.g., CD8), PD-L1 expressing dendritic cells, neutrophil proliferation, amongst other concomitant alterations. Further investigation of these biomarkers may reveal independent risk factors of CRC metastasis that can be formulated into low-cost, widely available assays.
{"title":"Identification of Spatial Proteomic Signatures of Colon Tumor Metastasis: A Digital Spatial Profiling Approach","authors":"Joshua J. Levy, John Zavras, Eren M. Veziroglu, Mustafa Nasir-Moin, Fred W. Kolling, B. Christensen, L. Salas, Rachael E. Barney, Scott M. Palisoul, Bing Ren, Xiaoying Liu, Darcy A. Kerr, K. Pointer, G. Tsongalis, L. Vaickus","doi":"10.1101/2022.12.04.22283073","DOIUrl":"https://doi.org/10.1101/2022.12.04.22283073","url":null,"abstract":"Over 150,000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually over 50,000 individuals will die from CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. CRC tumors are removed en bloc with surrounding vasculature and lymphatics. Examination of regional lymph nodes at the time of surgical resection is essential for prognostication. Developing alternative approaches to indirectly assess recurrence risk would have utility in cases where lymph node yield is incomplete or inadequate. Spatially dependent, immune cell-specific (e.g., Tumor Infiltrating Lymphocytes- TILs), proteomic, and transcriptomic expression patterns inside and around the tumor - the tumor immune microenvironment (TIME) - can predict nodal/distant metastasis and probe the coordinated immune response from the primary tumor site. The comprehensive characterization of TILs and other immune infiltrates is possible using highly multiplexed spatial omics technologies, such as the GeoMX Digital Spatial Profiler (DSP). In this study, machine learning and differential co-expression analyses helped identify biomarkers from DSP-assayed protein expression patterns inside, at the invasive margin, and away from the tumor, associated with extracellular matrix remodeling (e.g., GZMB, fibronectin), immune suppression (e.g., FOXP3), exhaustion and cytotoxicity (e.g., CD8), PD-L1 expressing dendritic cells, neutrophil proliferation, amongst other concomitant alterations. Further investigation of these biomarkers may reveal independent risk factors of CRC metastasis that can be formulated into low-cost, widely available assays.","PeriodicalId":77408,"journal":{"name":"The American journal of cardiovascular pathology","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87084383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-22DOI: 10.1101/2022.09.19.22280112
Junhwi Kim, Naofumi Tomita, A. Suriawinata, S. Hassanpour
Colorectal cancer is one of the most common types of cancer among men and women. The grading of dysplasia and the detection of adenocarcinoma are important clinical tasks in the diagnosis of colorectal cancer and shape the patients' follow-up plans. This study evaluates the feasibility of deep learning models for the classification of colorectal lesions into four classes: benign, low-grade dysplasia, high-grade dysplasia, and adenocarcinoma. To this end, we develop a deep neural network on a training set of 655 whole-slide images of digitized colorectal resection slides from a tertiary medical institution and evaluate it on an internal test set of 234 slides, as well as on an external test set of 606 adenocarcinoma slides from The Cancer Genome Atlas database. Our model achieves an overall accuracy, sensitivity, and specificity of 95.5%, 91.0%, and 97.1% on the internal test set and an accuracy and sensitivity of 98.5% for adenocarcinoma detection task on the external test set. Our results suggest that such deep learning models can potentially assist pathologists in grading colorectal dysplasia, detecting adenocarcinoma, prescreening, and prioritizing the reviewing of suspicious cases to improve the turnaround time for patients with a high risk of colorectal cancer. Furthermore, the high sensitivity on the external test set suggests our model's generalizability in detecting colorectal adenocarcinoma on whole slide images across different institutions.
{"title":"Detection of Colorectal Adenocarcinoma and Grading Dysplasia on Histopathologic Slides Using Deep Learning","authors":"Junhwi Kim, Naofumi Tomita, A. Suriawinata, S. Hassanpour","doi":"10.1101/2022.09.19.22280112","DOIUrl":"https://doi.org/10.1101/2022.09.19.22280112","url":null,"abstract":"Colorectal cancer is one of the most common types of cancer among men and women. The grading of dysplasia and the detection of adenocarcinoma are important clinical tasks in the diagnosis of colorectal cancer and shape the patients' follow-up plans. This study evaluates the feasibility of deep learning models for the classification of colorectal lesions into four classes: benign, low-grade dysplasia, high-grade dysplasia, and adenocarcinoma. To this end, we develop a deep neural network on a training set of 655 whole-slide images of digitized colorectal resection slides from a tertiary medical institution and evaluate it on an internal test set of 234 slides, as well as on an external test set of 606 adenocarcinoma slides from The Cancer Genome Atlas database. Our model achieves an overall accuracy, sensitivity, and specificity of 95.5%, 91.0%, and 97.1% on the internal test set and an accuracy and sensitivity of 98.5% for adenocarcinoma detection task on the external test set. Our results suggest that such deep learning models can potentially assist pathologists in grading colorectal dysplasia, detecting adenocarcinoma, prescreening, and prioritizing the reviewing of suspicious cases to improve the turnaround time for patients with a high risk of colorectal cancer. Furthermore, the high sensitivity on the external test set suggests our model's generalizability in detecting colorectal adenocarcinoma on whole slide images across different institutions.","PeriodicalId":77408,"journal":{"name":"The American journal of cardiovascular pathology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80502481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-09DOI: 10.48550/arXiv.2208.04910
D. Ho, Narasimhan P. Agaram, M. Jean, Stephanie D. Suser, Cynthia Chu, C. Vanderbilt, P. Meyers, L. Wexler, J. Healey, Thomas J. Fuchs, M. Hameed
Osteosarcoma is the most common primary bone cancer, whose standard treatment includes pre-operative chemotherapy followed by resection. Chemotherapy response is used for predicting prognosis and further management of patients. Necrosis is routinely assessed after chemotherapy from histology slides on resection specimens, where necrosis ratio is defined as the ratio of necrotic tumor/overall tumor. Patients with necrosis ratio ≥90% are known to have a better outcome. Manual microscopic review of necrosis ratio from multiple glass slides is semiquantitative and can have intraobserver and interobserver variability. In this study, an objective and reproducible deep learning-based approach is proposed to estimate necrosis ratio with outcome prediction from scanned hematoxylin and eosin whole slide images (WSIs). To conduct the study, 103 osteosarcoma cases with 3134 WSIs were collected. Deep Multi-Magnification Network was trained to segment multiple tissue subtypes, including viable tumor and necrotic tumor in pixel level and to calculate case-level necrosis ratio from multiple WSIs. Necrosis ratio estimated by the segmentation model highly correlates with necrosis ratio from pathology reports manually assessed by experts. Furthermore, patients were successfully stratified to predict overall survival with P = 2.4 × 10-6 and progression-free survival with P = 0.016. This study indicates deep learning can support pathologists as an objective tool to analyze osteosarcoma from histology for assessing treatment response and predicting patient outcome.
{"title":"Deep Learning-Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction","authors":"D. Ho, Narasimhan P. Agaram, M. Jean, Stephanie D. Suser, Cynthia Chu, C. Vanderbilt, P. Meyers, L. Wexler, J. Healey, Thomas J. Fuchs, M. Hameed","doi":"10.48550/arXiv.2208.04910","DOIUrl":"https://doi.org/10.48550/arXiv.2208.04910","url":null,"abstract":"Osteosarcoma is the most common primary bone cancer, whose standard treatment includes pre-operative chemotherapy followed by resection. Chemotherapy response is used for predicting prognosis and further management of patients. Necrosis is routinely assessed after chemotherapy from histology slides on resection specimens, where necrosis ratio is defined as the ratio of necrotic tumor/overall tumor. Patients with necrosis ratio ≥90% are known to have a better outcome. Manual microscopic review of necrosis ratio from multiple glass slides is semiquantitative and can have intraobserver and interobserver variability. In this study, an objective and reproducible deep learning-based approach is proposed to estimate necrosis ratio with outcome prediction from scanned hematoxylin and eosin whole slide images (WSIs). To conduct the study, 103 osteosarcoma cases with 3134 WSIs were collected. Deep Multi-Magnification Network was trained to segment multiple tissue subtypes, including viable tumor and necrotic tumor in pixel level and to calculate case-level necrosis ratio from multiple WSIs. Necrosis ratio estimated by the segmentation model highly correlates with necrosis ratio from pathology reports manually assessed by experts. Furthermore, patients were successfully stratified to predict overall survival with P = 2.4 × 10-6 and progression-free survival with P = 0.016. This study indicates deep learning can support pathologists as an objective tool to analyze osteosarcoma from histology for assessing treatment response and predicting patient outcome.","PeriodicalId":77408,"journal":{"name":"The American journal of cardiovascular pathology","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74424157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vestibular schwannomas (VSs), which develop from Schwann cells (SCs) of the vestibular nerve, are the most prevalent benign tumors of the cerebellopontine angle and internal auditory canal. Despite advances in treatment, the cellular components and mechanisms of VS tumor progression remain unclear. Here, we performed single-cell RNA-seq on clinically surgically isolated VS samples and clarified their cellular composition in a sophisticated manner, including the heterogeneous Schwann cell subtypes. Advanced bioinformatics analysis also revealed the associated biological functions, pseudotime trajectory and transcriptional network of the SC subgroups. Furthermore, we also found that there was tight intercellular communication between Schwann cells and tumor-associated fibroblasts via integrin and growth factor signaling and that the gene expression differences in SCs and fibroblasts determined the heterogeneity of cellular communication in different individuals. Our findings suggested a microenvironmental mechanism underlying the development of VS.
{"title":"Single-cell RNA-Seq Reveals the Heterogeneity of Cell Communications between Schwann Cells and Fibroblasts within the Microenvironment in Vestibular Schwannoma.","authors":"Maoxiang Xu, Sheng Wang, Yu-Chen Jiang, Jingjing Wang, Yuanping Xiong, Wenqi Dong, Q. Yao, Y. Xing, Feng Liu, Zhengnong Chen, Dongzhen Yu","doi":"10.2139/ssrn.4022083","DOIUrl":"https://doi.org/10.2139/ssrn.4022083","url":null,"abstract":"Vestibular schwannomas (VSs), which develop from Schwann cells (SCs) of the vestibular nerve, are the most prevalent benign tumors of the cerebellopontine angle and internal auditory canal. Despite advances in treatment, the cellular components and mechanisms of VS tumor progression remain unclear. Here, we performed single-cell RNA-seq on clinically surgically isolated VS samples and clarified their cellular composition in a sophisticated manner, including the heterogeneous Schwann cell subtypes. Advanced bioinformatics analysis also revealed the associated biological functions, pseudotime trajectory and transcriptional network of the SC subgroups. Furthermore, we also found that there was tight intercellular communication between Schwann cells and tumor-associated fibroblasts via integrin and growth factor signaling and that the gene expression differences in SCs and fibroblasts determined the heterogeneity of cellular communication in different individuals. Our findings suggested a microenvironmental mechanism underlying the development of VS.","PeriodicalId":77408,"journal":{"name":"The American journal of cardiovascular pathology","volume":"49 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89289712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1016/j.ajpath.2022.03.006
H. Kiyokawa, M. Abe, T. Matsui, Masako Kurashige, K. Ohshima, S. Tahara, S. Nojima, T. Ogino, Y. Sekido, T. Mizushima, E. Morii
{"title":"Deep learning analysis of histologic images from intestinal specimen reveal \"adipocyte shrinkage\" and mast cell infiltration to predict post-operative Crohn's disease.","authors":"H. Kiyokawa, M. Abe, T. Matsui, Masako Kurashige, K. Ohshima, S. Tahara, S. Nojima, T. Ogino, Y. Sekido, T. Mizushima, E. Morii","doi":"10.1016/j.ajpath.2022.03.006","DOIUrl":"https://doi.org/10.1016/j.ajpath.2022.03.006","url":null,"abstract":"","PeriodicalId":77408,"journal":{"name":"The American journal of cardiovascular pathology","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75462287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1016/j.ajpath.2022.03.005
Yuesu Zhou, Meijia Chang, Ning Wang, Yuan Zhuang, Fangmei Wang, Xu Zhang, M. Guo, N. Lin, John Zhong Li, Qian Wang
{"title":"Phosphatidylserine-specific phospholipase A1 limits aggressiveness of lung adenocarcinoma by lysophosphatidylserine and PKA-dependent pathway.","authors":"Yuesu Zhou, Meijia Chang, Ning Wang, Yuan Zhuang, Fangmei Wang, Xu Zhang, M. Guo, N. Lin, John Zhong Li, Qian Wang","doi":"10.1016/j.ajpath.2022.03.005","DOIUrl":"https://doi.org/10.1016/j.ajpath.2022.03.005","url":null,"abstract":"","PeriodicalId":77408,"journal":{"name":"The American journal of cardiovascular pathology","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89953176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1016/j.ajpath.2022.03.007
M. Masawa, Hanako Sato-Yazawa, K. Kashiwagi, Jun Ishii, Chie Miyata-Hiramatsu, M. Iwamoto, K. Kohno, Tadasuke Miyazawa, Masato Onozaki, S. Noda, Y. Shimizu, S. Niho, T. Yazawa
{"title":"REST inactivation and coexpression of ASCL1 and POU3F4 are necessary for the complete transformation of RB1/TP53-inactivated lung adenocarcinoma into neuroendocrine carcinoma.","authors":"M. Masawa, Hanako Sato-Yazawa, K. Kashiwagi, Jun Ishii, Chie Miyata-Hiramatsu, M. Iwamoto, K. Kohno, Tadasuke Miyazawa, Masato Onozaki, S. Noda, Y. Shimizu, S. Niho, T. Yazawa","doi":"10.1016/j.ajpath.2022.03.007","DOIUrl":"https://doi.org/10.1016/j.ajpath.2022.03.007","url":null,"abstract":"","PeriodicalId":77408,"journal":{"name":"The American journal of cardiovascular pathology","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75592752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1016/j.ajpath.2022.03.009
J. Hinley, Rosalind Duke, Jessica Jinks, J. Stahlschmidt, D. Keene, R. Cervellione, I. Mushtaq, P. de Coppi, M. Garriboli, J. Southgate
{"title":"Barrier-Forming Potential of Epithelial Cells from the Exstrophic Bladder","authors":"J. Hinley, Rosalind Duke, Jessica Jinks, J. Stahlschmidt, D. Keene, R. Cervellione, I. Mushtaq, P. de Coppi, M. Garriboli, J. Southgate","doi":"10.1016/j.ajpath.2022.03.009","DOIUrl":"https://doi.org/10.1016/j.ajpath.2022.03.009","url":null,"abstract":"","PeriodicalId":77408,"journal":{"name":"The American journal of cardiovascular pathology","volume":"52 1","pages":"943 - 955"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77006207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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