NPJ Precision Oncology最新文献_第5页

ACSL3 is an unfavorable prognostic marker in cholangiocarcinoma patients and confers ferroptosis resistance in cholangiocarcinoma cells ACSL3在胆管癌患者中是一个不利的预后标志物，并赋予胆管癌细胞对铁下垂的抗性。

IF 6.8 1区医学

NPJ Precision Oncology Pub Date : 2024-12-20 DOI: 10.1038/s41698-024-00783-8

Apiwit Sae-Fung, Nawaporn Vinayavekhin, Bengt Fadeel, Siriporn Jitkaew

{"title":"ACSL3 is an unfavorable prognostic marker in cholangiocarcinoma patients and confers ferroptosis resistance in cholangiocarcinoma cells","authors":"Apiwit Sae-Fung, Nawaporn Vinayavekhin, Bengt Fadeel, Siriporn Jitkaew","doi":"10.1038/s41698-024-00783-8","DOIUrl":"10.1038/s41698-024-00783-8","url":null,"abstract":"Cholangiocarcinoma (CCA) is a bile duct malignancy. Our previous comprehensive analysis showed that ferroptosis-related genes can stratify CCA patients into low-risk and high-risk groups based on survival time. Here, we explored the role of ferroptosis in CCA by analyzing mRNA expression in CCA patients from public databases. We identified acyl-CoA synthetase long chain family member 3 (ACSL3) as a potential ferroptosis suppressor in high-risk CCA patients. Using a panel of CCA cell lines, we confirmed ACSL3 upregulation in CCA cell lines associated with high-risk CCA, correlating this with resistance to the ferroptosis inducer RSL3. Lipidomic analysis revealed increased monounsaturated fatty acid (MUFA)-containing phospholipids in resistant cell lines. ACSL3 silencing sensitized these cells to RSL3. Resistance to ferroptosis was also dependent on exogenous MUFAs and was enhanced by lipid droplet biogenesis inhibition. These findings highlight ACSL3 as a promising target for therapeutic strategies aimed at overcoming ferroptosis resistance in CCA.","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":" ","pages":"1-14"},"PeriodicalIF":6.8,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41698-024-00783-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142872684","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

Characterization of shared neoantigens landscape in Mismatch Repair Deficient Endometrial Cancer 错配修复缺陷子宫内膜癌中共享新抗原景观的特征。

IF 6.8 1区医学

NPJ Precision Oncology Pub Date : 2024-12-20 DOI: 10.1038/s41698-024-00779-4

Elisa De Paolis, Camilla Nero, Elisa Micarelli, Guido Leoni, Alessia Piermattei, Rita Trozzi, Elisa Scarselli, Anna Morena D’Alise, Luciano Giacò, Maria De Bonis, Alessia Preziosi, Gennaro Daniele, Diletta Piana, Tina Pasciuto, Gianfranco Zannoni, Angelo Minucci, Giovanni Scambia, Andrea Urbani, Francesco Fanfani

{"title":"Characterization of shared neoantigens landscape in Mismatch Repair Deficient Endometrial Cancer","authors":"Elisa De Paolis, Camilla Nero, Elisa Micarelli, Guido Leoni, Alessia Piermattei, Rita Trozzi, Elisa Scarselli, Anna Morena D’Alise, Luciano Giacò, Maria De Bonis, Alessia Preziosi, Gennaro Daniele, Diletta Piana, Tina Pasciuto, Gianfranco Zannoni, Angelo Minucci, Giovanni Scambia, Andrea Urbani, Francesco Fanfani","doi":"10.1038/s41698-024-00779-4","DOIUrl":"10.1038/s41698-024-00779-4","url":null,"abstract":"Endometrial cancer (EC) with Mismatch Repair deficiency (MMRd) is characterized by the accumulation of insertions/deletions at microsatellite sites. These mutations lead to the synthesis of frameshift peptides (FSPs) that represent tumor-specific neoantigens (nAg) proved to be shared across patients/tumors with MMRd. In this study, we explored the feasibility of a nAg-based cancer vaccination design in EC with MMRd. We adopted a whole exome sequencing approach and ad hoc bioinformatics pipelines to characterize FSPs in 35 patients with EC. A mean of 146 mutated mononucleotide repeats (MNRs) was identified with enrichment in the patients’ group with MLH1 impairment. A high coverage emerged from the comparative analysis of the EC FSPs with the content of the previously validated NOUS-209 vaccine. We obtained pieces of evidence of FSPs translation as expressed proteins from Ribo-seq, supporting the potential as the target of vaccination. The development of a nAgs-based vaccine strategy in MMRd EC may be further explored.","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":" ","pages":"1-9"},"PeriodicalIF":6.8,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41698-024-00779-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142871805","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

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