NPJ Precision Oncology最新文献_第4页

Feature-interactive Siamese graph encoder-based image analysis to predict STAS from histopathology images in lung cancer 基于特征交互暹罗图编码器的图像分析预测肺癌组织病理学图像中的STAS。

IF 6.8 1区医学

NPJ Precision Oncology Pub Date : 2024-12-20 DOI: 10.1038/s41698-024-00771-y

Liangrui Pan, Qingchun Liang, Wenwu Zeng, Yijun Peng, Zhenyu Zhao, Yiyi Liang, Jiadi Luo, Xiang Wang, Shaoliang Peng

{"title":"Feature-interactive Siamese graph encoder-based image analysis to predict STAS from histopathology images in lung cancer","authors":"Liangrui Pan, Qingchun Liang, Wenwu Zeng, Yijun Peng, Zhenyu Zhao, Yiyi Liang, Jiadi Luo, Xiang Wang, Shaoliang Peng","doi":"10.1038/s41698-024-00771-y","DOIUrl":"10.1038/s41698-024-00771-y","url":null,"abstract":"Spread through air spaces (STAS) is a distinct invasion pattern in lung cancer, crucial for prognosis assessment and guiding surgical decisions. Histopathology is the gold standard for STAS detection, yet traditional methods are subjective, time-consuming, and prone to misdiagnosis, limiting large-scale applications. We present VERN, an image analysis model utilizing a feature-interactive Siamese graph encoder to predict STAS from lung cancer histopathological images. VERN captures spatial topological features with feature sharing and skip connections to enhance model training. Using 1,546 histopathology slides, we built a large single-cohort STAS lung cancer dataset. VERN achieved an AUC of 0.9215 in internal validation and AUCs of 0.8275 and 0.8829 in frozen and paraffin-embedded test sections, respectively, demonstrating clinical-grade performance. Validated on a single-cohort and three external datasets, VERN showed robust predictive performance and generalizability, providing an open platform ( http://plr.20210706.xyz:5000/ ) to enhance STAS diagnosis efficiency and accuracy.","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":" ","pages":"1-12"},"PeriodicalIF":6.8,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41698-024-00771-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142872170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting the tumor microenvironment composition and immunotherapy response in non-small cell lung cancer from digital histopathology images 从数字组织病理学图像预测非小细胞肺癌的肿瘤微环境组成和免疫治疗反应

IF 6.8 1区医学

NPJ Precision Oncology Pub Date : 2024-12-19 DOI: 10.1038/s41698-024-00765-w

Sushant Patkar, Alex Chen, Alina Basnet, Amber Bixby, Rahul Rajendran, Rachel Chernet, Susan Faso, Prashant A. Kumar, Devashish Desai, Ola El-Zammar, Christopher Curtiss, Saverio J. Carello, Michel R. Nasr, Peter Choyke, Stephanie Harmon, Baris Turkbey, Tamara Jamaspishvili

{"title":"Predicting the tumor microenvironment composition and immunotherapy response in non-small cell lung cancer from digital histopathology images","authors":"Sushant Patkar, Alex Chen, Alina Basnet, Amber Bixby, Rahul Rajendran, Rachel Chernet, Susan Faso, Prashant A. Kumar, Devashish Desai, Ola El-Zammar, Christopher Curtiss, Saverio J. Carello, Michel R. Nasr, Peter Choyke, Stephanie Harmon, Baris Turkbey, Tamara Jamaspishvili","doi":"10.1038/s41698-024-00765-w","DOIUrl":"10.1038/s41698-024-00765-w","url":null,"abstract":"Immune checkpoint inhibitors (ICI) have become integral to treatment of non-small cell lung cancer (NSCLC). However, reliable biomarkers predictive of immunotherapy efficacy are limited. Here, we introduce HistoTME, a novel weakly supervised deep learning approach to infer the tumor microenvironment (TME) composition directly from histopathology images of NSCLC patients. We show that HistoTME accurately predicts the expression of 30 distinct cell type-specific molecular signatures directly from whole slide images, achieving an average Pearson correlation of 0.5 with the ground truth on independent tumor cohorts. Furthermore, we find that HistoTME-predicted microenvironment signatures and their underlying interactions improve prognostication of lung cancer patients receiving immunotherapy, achieving an AUROC of 0.75 [95% CI: 0.61-0.88] for predicting treatment responses following first-line ICI treatment, utilizing an external clinical cohort of 652 patients. Collectively, HistoTME presents an effective approach for interrogating the TME and predicting ICI response, complementing PD-L1 expression, and bringing us closer to personalized immuno-oncology.","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":" ","pages":"1-15"},"PeriodicalIF":6.8,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41698-024-00765-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142845193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CAR-T cell therapy for the treatment of adult high-grade gliomas CAR-T细胞疗法治疗成人高级别胶质瘤

IF 6.8 1区医学

NPJ Precision Oncology Pub Date : 2024-12-19 DOI: 10.1038/s41698-024-00753-0

Sangwoo Park, Marcela V. Maus, Bryan D. Choi

引用次数: 0

Pitfalls in interpreting calibration in comparative evaluations of risk models for precision lung cancer screening 在肺癌精确筛查风险模型的比较评估中解释校准的陷阱

IF 6.8 1区医学

NPJ Precision Oncology Pub Date : 2024-12-19 DOI: 10.1038/s41698-024-00785-6

Hermann Brenner, Clara Frick, Teresa Seum, Megha Bhardwaj

引用次数: 0

A universal immunohistochemistry analyzer for generalizing AI-driven assessment of immunohistochemistry across immunostains and cancer types 一种通用的免疫组织化学分析仪，用于推广ai驱动的免疫组织化学评估，跨越免疫染色和癌症类型。

IF 6.8 1区医学

NPJ Precision Oncology Pub Date : 2024-12-03 DOI: 10.1038/s41698-024-00770-z

Biagio Brattoli, Mohammad Mostafavi, Taebum Lee, Wonkyung Jung, Jeongun Ryu, Seonwook Park, Jongchan Park, Sergio Pereira, Seunghwan Shin, Sangjoon Choi, Hyojin Kim, Donggeun Yoo, Siraj M. Ali, Kyunghyun Paeng, Chan-Young Ock, Soo Ick Cho, Seokhwi Kim

{"title":"A universal immunohistochemistry analyzer for generalizing AI-driven assessment of immunohistochemistry across immunostains and cancer types","authors":"Biagio Brattoli, Mohammad Mostafavi, Taebum Lee, Wonkyung Jung, Jeongun Ryu, Seonwook Park, Jongchan Park, Sergio Pereira, Seunghwan Shin, Sangjoon Choi, Hyojin Kim, Donggeun Yoo, Siraj M. Ali, Kyunghyun Paeng, Chan-Young Ock, Soo Ick Cho, Seokhwi Kim","doi":"10.1038/s41698-024-00770-z","DOIUrl":"10.1038/s41698-024-00770-z","url":null,"abstract":"Immunohistochemistry (IHC) is the common companion diagnostics in targeted therapies. However, quantifying protein expressions in IHC images present a significant challenge, due to variability in manual scoring and inherent subjective interpretation. Deep learning (DL) offers a promising approach to address these issues, though current models require extensive training for each cancer and IHC type, limiting the practical application. We developed a Universal IHC (UIHC) analyzer, a DL-based tool that quantifies protein expression across different cancers and IHC types. This multi-cohort trained model outperformed conventional single-cohort models in analyzing unseen IHC images (Kappa score 0.578 vs. up to 0.509) and demonstrated consistent performance across varying positive staining cutoff values. In a discovery application, the UIHC model assigned higher tumor proportion scores to MET amplification cases, but not MET exon 14 splicing or other non-small cell lung cancer cases. This UIHC model represents a novel role for DL that further advances quantitative analysis of IHC.","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":" ","pages":"1-13"},"PeriodicalIF":6.8,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41698-024-00770-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142770993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessing CD36 and CD47 expression levels in solid tumor indications to stratify patients for VT1021 treatment 评估CD36和CD47在实体瘤指征中的表达水平，以对VT1021治疗的患者进行分层。

IF 6.8 1区医学

NPJ Precision Oncology Pub Date : 2024-12-03 DOI: 10.1038/s41698-024-00774-9

Suming Wang, Victor Zota, Melanie Y. Vincent, Donna Clossey, Jian Jenny Chen, Michael Cieslewicz, Randolph S. Watnick, James Mahoney, Jing Watnick

引用次数: 0

Extreme wrinkling of the nuclear lamina is a morphological marker of cancer 核层的极端起皱是癌症的形态学标志。

IF 6.8 1区医学

NPJ Precision Oncology Pub Date : 2024-12-02 DOI: 10.1038/s41698-024-00775-8

Ting-Ching Wang, Christina R. Dollahon, Sneha Mishra, Hailee Patel, Samere Abolghasemzade, Ishita Singh, Vilmos Thomazy, Daniel G. Rosen, Vlad C. Sandulache, Saptarshi Chakraborty, Tanmay P. Lele

{"title":"Extreme wrinkling of the nuclear lamina is a morphological marker of cancer","authors":"Ting-Ching Wang, Christina R. Dollahon, Sneha Mishra, Hailee Patel, Samere Abolghasemzade, Ishita Singh, Vilmos Thomazy, Daniel G. Rosen, Vlad C. Sandulache, Saptarshi Chakraborty, Tanmay P. Lele","doi":"10.1038/s41698-024-00775-8","DOIUrl":"10.1038/s41698-024-00775-8","url":null,"abstract":"Nuclear atypia is a hallmark of cancer. A recent model posits that excess surface area, visible as folds/wrinkles in the lamina of a rounded nucleus, allows the nucleus to take on diverse shapes with little mechanical resistance. Whether this model is applicable to normal and cancer nuclei in human tissues is unclear. We image nuclear lamins in patient tissues and find: (a) nuclear laminar wrinkles are present in control and cancer tissue but are obscured in hematoxylin and eosin (H&E) images, (b) nuclei rarely have a smooth lamina, and (c) wrinkled nuclei assume diverse shapes. Deep learning reveals the presence of extreme nuclear laminar wrinkling in cancer tissues, which is confirmed by Fourier analysis. These data support a model in which excess surface area in the nuclear lamina enables nuclear shape diversity in vivo. Extreme laminar wrinkling is a marker of cancer, and imaging the lamina may benefit cancer diagnosis.","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":" ","pages":"1-12"},"PeriodicalIF":6.8,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41698-024-00775-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142770909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intratumoral heterogeneity drives acquired therapy resistance in a patient with metastatic prostate cancer 肿瘤内异质性驱动转移性前列腺癌患者获得性治疗耐药。

IF 6.8 1区医学

NPJ Precision Oncology Pub Date : 2024-12-02 DOI: 10.1038/s41698-024-00773-w

Dena P. Rhinehart, Jiaying Lai, David E. Sanin, Varsha Vakkala, Adrianna Mendes, Christopher Bailey, Emmanuel S. Antonarakis, Channing J. Paller, Xiaojun Wu, Tamara L. Lotan, Rachel Karchin, Laura A. Sena

引用次数: 0

Deep mutual learning on hybrid amino acid PET predicts H3K27M mutations in midline gliomas 混合氨基酸 PET 深度相互学习预测中线胶质瘤中的 H3K27M 突变

IF 6.8 1区医学

NPJ Precision Oncology Pub Date : 2024-11-25 DOI: 10.1038/s41698-024-00760-1

Yifan Yuan, Guanglei Li, Shuhao Mei, Mingtao Hu, Ying-Hua Chu, Yi-Cheng Hsu, Chaolin Li, Jianping Song, Jie Hu, Danyang Feng, Fang Xie, Yihui Guan, Qi Yue, Mianxin Liu, Ying Mao

{"title":"Deep mutual learning on hybrid amino acid PET predicts H3K27M mutations in midline gliomas","authors":"Yifan Yuan, Guanglei Li, Shuhao Mei, Mingtao Hu, Ying-Hua Chu, Yi-Cheng Hsu, Chaolin Li, Jianping Song, Jie Hu, Danyang Feng, Fang Xie, Yihui Guan, Qi Yue, Mianxin Liu, Ying Mao","doi":"10.1038/s41698-024-00760-1","DOIUrl":"10.1038/s41698-024-00760-1","url":null,"abstract":"Predicting H3K27M mutation status in midline gliomas non-invasively is of considerable interest, particularly using deep learning with 11C-methionine (MET) and 18F-fluoroethyltyrosine (FET) positron emission tomography (PET). To optimise prediction efficiency, we derived an assistance training (AT) scheme to allow mutual benefits between MET and FET learning to boost the predictability but still only require either PET as inputs for predictions. Our method significantly surpassed conventional convolutional neural network (CNN), radiomics-based, and MR-based methods, achieved an area under the curve (AUC) of 0.9343 for MET, and an AUC of 0.8619 for FET during internal cross-validation (n = 90). The performance remained high in hold-out testing (n = 19) and consecutive testing cohorts (n = 21), with AUCs of 0.9205 and 0.7404. The clinical feasibility of the proposed method was confirmed by the agreements to multi-departmental decisions and outcomes in pathology-uncertain cases. The findings positions our method as a promising tool for aiding treatment decisions in midline glioma.","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":" ","pages":"1-13"},"PeriodicalIF":6.8,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11589770/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142716677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

COL8A1 overexpression promotes glioma cell growth by activating focal adhesion kinase signaling cascade COL8A1 过表达通过激活局灶粘附激酶信号级联促进胶质瘤细胞生长

IF 6.8 1区医学

NPJ Precision Oncology Pub Date : 2024-11-22 DOI: 10.1038/s41698-024-00762-z

Jin Qian, Haihui Xing, Yin Wang, Chen Li, Hairong Chen, Jun Rong, Chunfa Qian

{"title":"COL8A1 overexpression promotes glioma cell growth by activating focal adhesion kinase signaling cascade","authors":"Jin Qian, Haihui Xing, Yin Wang, Chen Li, Hairong Chen, Jun Rong, Chunfa Qian","doi":"10.1038/s41698-024-00762-z","DOIUrl":"10.1038/s41698-024-00762-z","url":null,"abstract":"We explored expression and biological roles of collagen type VIII alpha-1 chain (COL8A1) in glioma. Bioinformatics analyses unveiled COL8A1 overexpression within glioma tissues correlates with adverse clinical outcomes of patients. COL8A1 overexpression was also detected in local glioma tissues and various glioma cells. In primary and immortalized glioma cells, COL8A1 shRNA or knockout (KO) reduced cell viability, proliferation and mobility, disrupted cell cycle, and prompted apoptosis. While COL8A1 overexpression augmented the malignant behaviors in glioma cells. COL8A1 shRNA or KO in primary glioma cells decreased phosphorylation of FAK and downstream targets Akt and Erk1/2. Conversely, elevating COL8A1 expression increased their phosphorylations. In vivo experiments confirmed growth inhibition of patient-derived glioma xenografts within the mouse brain following COL8A1 KO. Hindered proliferation, lowered phosphorylation levels of FAK, Akt, and Erk1/2, as well as increased apoptosis were observed within the COL8A1 KO intracranial glioma xenografts. Thus, COL8A1 overexpression promotes glioma cell growth.","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":" ","pages":"1-15"},"PeriodicalIF":6.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41698-024-00762-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0