Article
Genetics & Heredity
Kaibin Zhu, An Yan, Fucheng Zhou, Su Zhao, Jinfeng Ning, Lei Yao, Desi Shang, Lantao Chen
Summary: This study investigated the prognostic value of pyroptosis-related genes (PRGs) in lung adenocarcinoma (LUAD). The classification based on PRGs was able to predict the overall survival rate of LUAD patients. The tumor microenvironment characteristics differed between the two subtypes, with subtype 2 showing stronger immunological infiltration. A prognostic model based on differentially expressed genes (DEGs) was developed to predict overall survival and response to immunotherapy in LUAD patients.
FRONTIERS IN GENETICS
(2022)
Article
Oncology
Jun Deng, Xu Lin, Qi Li, Xiao-yu Cai, Lin-wen Wu, Wei Wang, Bo Zhang, Yang-ling Li, Jian Hu, Neng-ming Lin
Summary: The study revealed that INPP5B is decreased in LUAD tissues and its low expression is associated with advanced pathological features. Furthermore, INPP5B was identified as a significant independent prognostic and diagnostic factor for LUAD patients. Experimental findings demonstrated that INPP5B inhibits LUAD cell proliferation and migration by regulating the activity of the PTEN and PI3K/AKT signaling pathways.
CANCER CELL INTERNATIONAL
(2022)
Article
Oncology
Suping Tang, Jun Ni, Bohua Chen, Fei Sun, Jinbo Huang, Songshi Ni, Zhiyuan Tang
Summary: Our study found that PAFAH1B3 is upregulated in LUAD tissues and cells compared with noncancerous tissues and cells. It is positively correlated with distant metastasis, TNM stage, and poor clinical outcome, and serves as an independent prognostic risk factor for LUAD. Silencing PAFAH1B3 suppresses proliferation, invasion, and EMT, while increasing the cell population in the G0-G1 phases. Additionally, knockdown of PAFAH1B3 increases E-cadherin level and decreases N-cadherin level, inhibiting tumorigenesis and neutrophil infiltration in the xenograft tumor model.
Article
Genetics & Heredity
Thinh T. Nguyen, Hyun-Sung Lee, Bryan M. Burt, Jia Wu, Jianjun Zhang, Christopher Amos, Chao Cheng
Summary: The study inferred immune cell infiltration levels using gene expression data and developed two gene signatures to computationally determine the relative abundance of lepidic and solid components in lung adenocarcinoma samples. The results showed significant immunological differences among histological subtypes, with L-scores associated with prolonged survival and S-scores associated with shortened survival. L-scores and S-scores were also correlated with genomic features and responses to targeted therapy and immunotherapy, suggesting potential clinical implications in predicting patient survival and treatment responses.
Article
Immunology
Pulin Li, Xiaojuan Chen, Sijing Zhou, Xingyuan Xia, Enze Wang, Rui Han, Daxiong Zeng, Guanghe Fei, Ran Wang
Summary: The study found that DEPDC1B expression was significantly higher in tumor tissues compared to normal tissues in both LUAD and LUSC, but high DEPDC1B expression was only associated with poor prognosis in LUAD patients. Functional enrichment analysis suggested that DEPDC1B promotes tumor development in LUAD by regulating the cell cycle.
JOURNAL OF INFLAMMATION RESEARCH
(2022)
Article
Biochemistry & Molecular Biology
Chia-Hsin Liu, Yuanpu Peter Di
Summary: Matrix metalloproteinase-gene signature is significantly associated with recurrence and survival outcomes in patients with stage I lung adenocarcinoma, making it a potential predictive and prognostic biomarker for personalized adjuvant therapeutics.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Oncology
Guichuan Huang, Jing Zhang, Ling Gong, Yi Huang, Daishun Liu
Summary: A three-gene signature related to glycolysis was identified as a novel biomarker for predicting the prognosis of patients with LUSC, with HKDC1, ALDH7A1, and MDH1 genes playing key roles. The signature also showed potential to be used as an independent prognostic indicator and provided additional gene targets for treating LUSC patients.
Article
Medicine, General & Internal
Sisi Chang, Yahui Zhu, Yutan Xi, Fuyan Gao, Juanjuan Lu, Liang Dong, Chunzheng Ma, Honglin Li
Summary: The study evaluated the role of DSCC1 in LUAD through analyzing TCGA and GTEx data. Results showed that high expression of DSCC1 was significantly correlated with T stage, pathological stage, TP53 status, disease-specific survival, overall survival, and DSCC1 was suggested as a potential diagnostic molecule for LUAD. Additionally, down-regulation of certain signaling pathways and positive correlation with specific immune cells were observed in the high DSCC1 expression phenotype, suggesting DSCC1 as an important biomarker for LUAD treatment.
INTERNATIONAL JOURNAL OF GENERAL MEDICINE
(2021)
Article
Immunology
Qianhe Ren, Pengpeng Zhang, Haoran Lin, Yanlong Feng, Hao Chi, Xiao Zhang, Zhijia Xia, Huabao Cai, Yue Yu
Summary: By analyzing gene data, this study found that cancer-associated fibroblasts (CAFs) in lung adenocarcinoma (LUAD) can predict disease progression and immunotherapy responsiveness, and established a risk signature. This provides new perspectives for the treatment and management of LUAD patients.
FRONTIERS IN IMMUNOLOGY
(2023)
Article
Multidisciplinary Sciences
Tao Yang, Lizheng Hao, Renyun Cui, Huanyu Liu, Jian Chen, Jiongjun An, Shuo Qi, Zhong Li
Summary: The study aimed to identify a potential signature model to improve prognosis of lung adenocarcinoma. Through analyzing genomic data and immune scores, the study predicted patients' overall survival rates and found that high stromal and immune scores were associated with better survival rates, effectively classifying different risk groups.
Article
Oncology
Fei-Yuan Yu, Qian Xu, Qi-Yao Wei, Hai-Ying Mo, Qiu-Hua Zhong, Xiao-Yun Zhao, Andy T. Y. Lau, Yan-Ming Xu
Summary: ACC2 is under-expressed in cancerous tissue and negatively correlated with tumor size, lymph-node metastases, and clinical stage in lung adenocarcinoma. Knocking down ACC2 promotes cell proliferation and migration, and affects the expression of cell cycle-related genes. Lung adenocarcinoma patients with under-expressed ACC2 have a worse prognosis.
JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY
(2022)
Article
Immunology
Wuguang Chang, Hongmu Li, Leqi Zhong, Tengfei Zhu, Zenghao Chang, Wei Ou, Siyu Wang
Summary: This study reveals the prognostic value of copper metabolism-related genes in lung adenocarcinoma and their potential role in guiding immunotherapy. The low-risk group exhibits longer overall survival and higher immune cell infiltration levels, suggesting potential benefits from immunotherapy.
FRONTIERS IN IMMUNOLOGY
(2022)
Article
Genetics & Heredity
Yuqi Song, Jinming Zhang, Linan Fang, Wei Liu
Summary: This study constructed a prognostic Necroptosis-related gene signature and found the association between necroptosis and LUAD, highlighting its potential use in guiding immunotherapy.
FRONTIERS IN GENETICS
(2022)
Article
Immunology
Minghui Zhang, Jianli Ma, Qiuyue Guo, Shuang Ding, Yan Wang, Haihong Pu
Summary: This study identifies molecular subtypes and a multi-gene signature associated with CD8(+) T cell-related genes in lung adenocarcinoma. The signature can be used to assess prognostic risk and immunotherapy response in patients.
FRONTIERS IN IMMUNOLOGY
(2022)
Article
Respiratory System
Quan Li, Pan Zhang, Huixiao Hu, Hang Huang, Dong Pan, Guangyun Mao, Burong Hu
Summary: In this study, a prognostic model based on 4 DDR-related genes with cell cycle checkpoint function was constructed to predict the survival rate, immune activity, and chemoradiotherapy responsiveness of LUAD patients.
RESPIRATORY RESEARCH
(2022)
Article
Biology
Dawei Sun, Lewis Evans, Francesca Perrone, Vanesa Sokleva, Kyungtae Lim, Saba Rezakhani, Matthias Lutolf, Matthias Zilbauer, Emma L. Rawlins
Summary: This study developed a genetic toolbox for gene manipulation studies in tissue-derived organoids using CRISPR-mediated homologous recombination and flow cytometry to enrich targeted cells. These tools will facilitate human disease modeling, provide functional counterparts, and support ongoing descriptive studies.
Review
Oncology
Samantha Brown, Jessica A. Lavery, Ronglai Shen, Axel S. Martin, Kenneth L. Kehl, Shawn M. Sweeney, Eva M. Lepisto, Hira Rizvi, Caroline G. McCarthy, Nikolaus Schultz, Jeremy L. Warner, Ben Ho Park, Philippe L. Bedard, Gregory J. Riely, Deborah Schrag, Katherine S. Panageas
Summary: Real-world data combining clinical and genomic information may face issues of left truncation, and ignoring this can lead to overestimation of survival rates. Appropriate statistical methods should be applied to ensure valid and meaningful research findings when analyzing clinicogenomic data.
Article
Computer Science, Information Systems
Jason A. Thomas, Randi E. Foraker, Noa Zamstein, Jon D. Morrow, Philip R. O. Payne, Adam B. Wilcox
Summary: The study found that synthetic data closely matched original data for epidemic curves, patient characteristics, and outcomes, but the utility of synthetic data decreased notably in small sample sizes. Analyses on densely tested zip codes were similar between original and synthetic data, while analyses of sparsely tested populations had more data suppression.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
(2022)
Article
Developmental Biology
Naomi Clements-Brod, Leah Holmes, Emma L. Rawlins
Summary: The Human Developmental Biology Initiative aims to improve treatments for fertility, birth defects, and developmental diseases by researching human fetal and embryonic tissues. Ethical and moral questions surrounding the use of these tissues in research and their potential healthcare impacts are of interest to both scientists and the public. The public engagement program 'What makes us human?' seeks to test new ways of engaging the public with fundamental biology.
Article
Medicine, General & Internal
Dongmei Sun, Thanh M. Nguyen, Robert J. Allaway, Jelai Wang, Verena Chung, Thomas Yu, Michael Mason, Isaac Dimitrovsky, Lars Ericson, Hongyang Li, Yuanfang Guan, Ariel Israel, Alex Olar, Balint Armin Pataki, Gustavo Stolovitzky, Justin Guinney, Percio S. Gulko, Mason B. Frazier, Jake Y. Chen, James C. Costello, S. Louis Bridges
Summary: This study describes an international crowdsourcing competition that aims to promote the development of machine learning methods for assessing radiographic damage in patients with rheumatoid arthritis. The competition resulted in the development of feasible, quick, and accurate algorithms for quantifying joint damage, which could potentially assist clinicians in making better treatment decisions.
Editorial Material
Oncology
Nathaniel B. Verhagen, Nicolas K. Koerber, Aniko Szabo, Bradley Taylor, J. Njeri Wainaina, Douglas B. Evans, Anai N. Kothari
ANNALS OF SURGICAL ONCOLOGY
(2023)
Article
Hematology
Jerald P. Radich, Matthew Wall, Susan Branford, Catarina D. Campbell, Shalini Chaturvedi, Daniel J. DeAngelo, Michael W. Deininger, Justin Guinney, Andreas Hochhaus, Timothy P. Hughes, Hagop M. Kantarjian, Richard A. Larson, Sai Li, Rodrigo Maegawa, Kaushal Mishra, Vanessa Obourn, Javier Pinilla-Ibarz, Das Purkayastha, Islam Sadek, Giuseppe Saglio, Alok Shrestha, Brian S. White, Brian J. Druker
Summary: Tyrosine kinase inhibitor therapy revolutionized chronic myeloid leukemia treatment, showing how targeted therapy and molecular monitoring can improve survival outcomes. A study on gene expression identified immune regulation pathways that were overexpressed in good responders, highlighting the importance of pretreatment adaptive immune response in treatment efficacy.
Article
Multidisciplinary Sciences
Chao Yan, Yao Yan, Zhiyu Wan, Ziqi Zhang, Larsson Omberg, Justin Guinney, Sean D. Mooney, Bradley A. Malin
Summary: This article introduces a systematic benchmarking framework to evaluate key characteristics of synthetic health data generation methods in terms of utility and privacy. The framework is applied to evaluate methods for generating synthetic data from electronic health records from two large academic medical centers. The results demonstrate a tradeoff between utility and privacy when sharing synthetic health data and suggest that no method is universally the best for all use cases, highlighting the importance of assessing synthetic data generation methods in context.
NATURE COMMUNICATIONS
(2022)
Article
Medicine, General & Internal
William Hsu, Daniel S. Hippe, Noor Nakhaei, Pin-Chieh Wang, Bing Zhu, Nathan Siu, Mehmet Eren Ahsen, William Lotter, A. Gregory Sorensen, Arash Naeim, Diana S. M. Buist, Thomas Schaffter, Justin Guinney, Joann G. Elmore, Christoph Lee
Summary: This study found that the high performance of an ensemble deep-learning model for automated screening mammography interpretation did not generalize to a more diverse screening cohort, suggesting that the model experienced underspecification. This study suggests the need for model transparency and fine-tuning of AI models for specific target populations prior to their clinical adoption.
Article
Developmental Biology
Shuyu Liu, Dawei Sun, Richard Butler, Emma L. Rawlins
Summary: This study expands multipotent epithelial progenitor cells from human embryonic lungs as organoids and maintains their self-renewing state. The cells exhibit columnar shape, resembling the developing lung tip epithelium. The study identifies the signaling pathways, including FGF7 and integrin signaling, that regulate cell shape and development in human lung progenitors.
Article
Oncology
Lei Sun, Jia Yu, Justin Guinney, Bo Qin, Frank A. Sinicrope
Summary: ZEB1 is a transcription factor that promotes tumor invasion and metastasis through inducing epithelial-to-mesenchymal transition (EMT). In this study, the regulation of ZEB1 by RAS/RAF signaling and its posttranslation modification, including ubiquitination, were investigated. The interaction between ZEB1 and the deubiquitinase USP10 was identified in colorectal cancer cell lines with RAS/RAF/MEK/ERK activation, and it was found that USP10 modifies ZEB1 ubiquitination and promotes its degradation, contributing to the suppression of tumor metastasis.
MOLECULAR CANCER RESEARCH
(2023)
Article
Genetics & Heredity
Jackson Michuda, Alessandra Breschi, Joshuah Kapilivsky, Kabir Manghnani, Calvin McCarter, Adam J. Hockenberry, Brittany Mineo, Catherine Igartua, Joel T. Dudley, Martin C. Stumpe, Nike Beaubier, Maryam Shirazi, Ryan Jones, Elizabeth Morency, Kim Blackwell, Justin Guinney, Kyle A. Beauchamp, Timothy Taxter
Summary: Cancers can have various histologies and origins, making diagnosis and treatment challenging. The Tempus TO assay, a machine learning classifier based on RNA-sequencing, can accurately determine the subtype of cancer, including cancers of unknown primary, providing potential therapeutic options.
MOLECULAR DIAGNOSIS & THERAPY
(2023)
Article
Biochemical Research Methods
Jineta Banerjee, Jaclyn N. Taroni, Robert J. Allaway, Deepashree Venkatesh Prasad, Justin Guinney, Casey Greene
Summary: High-throughput profiling methods have made molecular characterization routine, and machine learning can be used to extract disease-relevant patterns. However, machine learning for rare diseases faces challenges due to limited clinical cases and few samples.
Article
Multidisciplinary Sciences
Debra Van Egeren, Khushi Kohli, Jeremy L. Warner, Philippe L. Bedard, Gregory Riely, Eva Lepisto, Deborah Schrag, Michele LeNoue-Newton, Paul Catalano, Kenneth L. Kehl, Franziska Michor
Summary: The study found a significant association between TP53 mutations and distant metastasis in non-small cell lung cancer (NSCLC) patients, and TP53 mutations were more prevalent in patients with a history of smoking, suggesting a higher risk of distant metastasis in these patients.
SCIENTIFIC REPORTS
(2022)
Article
Cell Biology
Eugene F. Douglass, Robert J. Allaway, Bence Szalai, Wenyu Wang, Tingzhong Tian, Adria Fernandez-Torras, Ron Realubit, Charles Karan, Shuyu Zheng, Alberto Pessia, Ziaurrehman Tanoli, Mohieddin Jafari, Fangping Wan, Shuya Li, Yuanpeng Xiong, Miquel Duran-Frigola, Martino Bertoni, Pau Badia-I-Mompel, Lidia Mateo, Oriol Guitart-Pla, Verena Chung, Jing Tang, Jianyang Zeng, Patrick Aloy, Julio Saez-Rodriguez, Justin Guinney, Daniela S. Gerhard, Andrea Califano
Summary: The Columbia Cancer Target Discovery and Development Center is developing a resource called PANACEA to study tumor-specific drug mechanisms of action. This resource is used as the basis for a DREAM Challenge to assess the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions.
CELL REPORTS MEDICINE
(2022)