Article
Biochemical Research Methods
Jing Hu, Jie Gao, Xiaomin Fang, Zijing Liu, Fan Wang, Weili Huang, Hua Wu, Guodong Zhao
Summary: Drug combination therapies are superior to monotherapy in cancer treatment. Computational methods have been developed to predict drug pairs with potential synergistic functions. We propose a deep neural network model called DTSyn based on a multi-head attention mechanism to identify novel drug combinations. DTSyn achieved high performance and improved interpretability by capturing chemical-gene and gene-gene associations and extracting chemical-chemical and chemical-cell line interactions.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Jinxian Wang, Xuejun Liu, Siyuan Shen, Lei Deng, Hui Liu
Summary: In this paper, we proposed a deep learning model based on graph neural network and attention mechanism to identify drug combinations that can effectively inhibit the viability of specific cancer cells. The model, called DeepDDS, achieved better performance than other methods in predicting drug synergy. Additionally, we explored the interpretability of the model and found important chemical substructures of drugs. DeepDDS is considered an effective tool for prioritizing synergistic drug combinations for further experimental validation.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Multidisciplinary Sciences
Wengong Jin, Jonathan M. Stokes, Richard T. Eastman, Zina Itkin, Alexey Zakharov, James J. Collins, Tommi S. Jaakkola, Regina Barzilay
Summary: This study introduces a new neural network architecture for learning drug-target interactions and drug-drug synergy to aid in the discovery of drug combinations against COVID-19. By incorporating additional biological information, the model significantly outperforms previous methods in synergy prediction accuracy.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Review
Biology
Yichen Pan, Haotian Ren, Liang Lan, Yixue Li, Tao Huang
Summary: The prediction of drug combinations is clinically significant in reducing drug resistance and developing precision therapy. This review summarizes the latest methods and databases used for predicting the effects of drug combinations and introduces five algorithms applied to drug combinatorial prediction.
Article
Biology
Xun Wang, Lele Yang, Chuang Yu, Xinping Ling, Congcong Guo, Ruzhen Chen, Dong Li, Zhongyang Liu
Summary: Drug resistance is a major obstacle in cancer treatment. To address this, drug combination therapy is proposed as a promising strategy. The study presents a novel computational strategy, RSDP, which predicts personalized cancer drug combination A + B by reversing the resistance signature of drug A. The strategy integrates multiple biological features using a robust rank aggregation algorithm and has shown accurate prediction performance in identifying personalized combinational re-sensitizing drug B against different types of resistance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Mathematical & Computational Biology
Jun Ma, Alison Motsinger-Reif
Summary: A new deep learning approach integrating gene expression profiles and chemical structure data was developed to predict synergistic drug combinations, outperforming other machine learning methods. The use of dimension reduction significantly decreased computation time without sacrificing accuracy.
Article
Virology
Aleksandr Ianevski, Eva Zusinaite, Tanel Tenson, Valentyn Oksenych, Wei Wang, Jan Egil Afset, Magnar Bjoras, Denis E. Kainov
Summary: The study tested several antiviral agents against enterovirus 1 in human cells, confirming the anti-enteroviral activities of some drugs and identifying synergistic effects of drug combinations against enterovirus infection.
Article
Biochemistry & Molecular Biology
Piyush More, Joelle Aurelie Mekontso Ngaffo, Ute Goedtel-Armbrust, Patricia S. Haehnel, Udo F. Hartwig, Thomas Kindler, Leszek Wojnowski
Summary: We analyzed gene expression changes in AML cell lines in response to standard AML drugs, identified treatment-relevant genes and pathways, and showed that drug response can be enhanced by pharmacological mimicking of these changes. The synergistic effect was observed in other cell lines and confirmed in public cytotoxicity data.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Cell Biology
Eleonora Ela Hezkiy, Santosh Kumar, Valid Gahramanov, Julia Yaglom, Arkadi Hesin, Suchita Suryakant Jadhav, Ekaterina Guzev, Shivani Patel, Elena Avinery, Michael A. Firer, Michael Y. Sherman
Summary: Finding synergistic drug combinations is crucial in cancer research. In this study, researchers identified that inhibitors of proteasome and mTORC1 could synergize with ibrutinib, an FDA-approved inhibitor, for the treatment of chronic lymphocytic leukemia (CLL), through shRNA screening and bioinformatics analysis.
Article
Multidisciplinary Sciences
Yasaman KalantarMotamedi, Ran Joo Choi, Siang-Boon Koh, Jo L. Bramhall, Tai-Ping Fan, Andreas Bender
Summary: Resistance to current therapies is common in pancreatic cancer, necessitating novel treatment options. A computational method was developed to select synergistic compound combinations based on transcriptomic profiles and pathway scoring system, successfully identifying effective compound combinations against pancreatic cancer cells.
Article
Oncology
Giulia Arosio, Geeta G. Sharma, Matteo Villa, Mario Mauri, Ilaria Crespiatico, Diletta Fontana, Chiara Manfroni, Cristina Mastini, Marina Zappa, Vera Magistroni, Monica Ceccon, Sara Redaelli, Luca Massimino, Anna Garbin, Federica Lovisa, Lara Mussolin, Rocco Piazza, Carlo Gambacorti-Passerini, Luca Mologni
Summary: Combining two drugs for the treatment of ALK+ ALCL can effectively prevent the emergence of resistant cells, showing superior efficacy compared to single drug treatments in controlling the expansion of lymphoma cells in the long term.
Article
Multidisciplinary Sciences
Deepika Vatsa, Sumeet Agarwal
Summary: This paper introduces a novel gene regulatory network inference method using Probabilistic Extended Petri Net, which utilizes transition of discrete gene expression levels as evidence types to understand gene regulation processes, and evaluates the method on two datasets.
Article
Chemistry, Multidisciplinary
Mengdie Xu, Xinwei Zhao, Jingyu Wang, Wei Feng, Naifeng Wen, Chunyu Wang, Junjie Wang, Yun Liu, Lingling Zhao
Summary: In this study, a deep learning model called the Dual Feature Fusion Network for Drug-Drug Synergy prediction (DFFNDDS) was proposed. It utilizes a fine-tuned pretrained language model and dual feature fusion mechanism to predict synergistic drug combinations. The dual feature fusion mechanism fuses the drug features and cell line features at the bit-wise level and the vector-wise level. The results demonstrate that DFFNDDS outperforms competitive methods and can serve as a reliable tool for identifying synergistic drug combinations.
JOURNAL OF CHEMINFORMATICS
(2023)
Article
Multidisciplinary Sciences
Jiaqi Li, Hongyan Xu, Richard A. McIndoe
Summary: Drug combination therapies play an important role in cancer treatment, but current methods to identify synergistic drug combinations are limited. This paper presents a computational algorithm that utilizes gene regulatory networks and single drug data to evaluate all possible drug pairs and find potential synergistic drug combinations.
Article
Biochemical Research Methods
Xiaowen Wang, Hongming Zhu, Danyi Chen, Yongsheng Yu, Qi Liu, Qin Liu
Summary: This article proposes a method called CGMS to address the instability and poor generalization ability of current deep learning models in drug combination prediction. CGMS models the relationship between drug combinations and cell lines, and uses a heterogeneous graph attention network to generate whole-graph embeddings for prediction. The application of multi-task learning further enhances the generalization ability of CGMS. Experimental results show that CGMS outperforms other methods in drug combination prediction.
Article
Oncology
Omeed Moaven, Jing Su, Guangxu Jin, Konstantinos Votanopoulos, Perry Shen, Christopher Mangieri, Stacey S. O'Neill, Kathleen C. Perry, Edward A. Levine, Lance D. Miller
ANNALS OF SURGICAL ONCOLOGY
(2020)
Article
Biochemical Research Methods
Wenzhong Yang, Guangxu Jin
Summary: The study reveals that the uneven distribution of genetic variations of the novel coronavirus SARS-CoV-2 among geographic regions is strongly correlated with the incidence and mortality of COVID-19.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Oncology
Angelina T. Regua, Noah R. Aguayo, Sara Abu Jalboush, Daniel L. Doheny, Sara G. Manore, Dongqin Zhu, Grace L. Wong, Austin Arrigo, Calvin J. Wagner, Yang Yu, Alexandra Thomas, Michael D. Chan, Jimmy Ruiz, Guangxu Jin, Roy Strowd, Peiqing Sun, Jiayuh Lin, Hui-Wen Lo
Summary: The study identified a novel co-activation of TrkA and STAT3 in triple-negative and HER2-enriched breast cancers, enhancing gene transcription and promoting breast cancer stem cells. The findings revealed that TrkA is a new activating kinase of STAT3, leading to increased aggressiveness of breast cancer through enhanced stem cell renewal and gene expression.
Article
Oncology
Chao Gao, Guangxu Jin, Elizabeth Forbes, Lingegowda S. Mangala, Yingmei Wang, Cristian Rodriguez-Aguayo, Paola Amero, Emine Bayraktar, Ye Yan, Gabriel Lopez-Berestein, Russell R. Broaddus, Anil K. Sood, Fengxia Xue, Wei Zhang
Summary: Research found that IK somatic mutations are more common in high-grade and high-stage endometrial cancer patients, who have longer survival periods. These mutations are related to the pathophysiology of EC and have a role in promoting cell cycle progression.
Article
Biochemical Research Methods
Yong Lu, Gang Xue, Ningbo Zheng, Kun Han, Wenzhong Yang, Rui-Sheng Wang, Lingyun Wu, Lance D. Miller, Timothy Pardee, Pierre L. Triozzi, Hui-Wen Lo, Kounosuke Watabe, Stephen T. C. Wong, Boris C. Pasche, Wei Zhang, Guangxu Jin
Summary: A new algorithm called hDirect-MAP has been developed to map T cells into a shared high-dimensional expression space in order to identify T cell phenotypes that respond to immune checkpoint blockade in specific types of cancer. By removing non-contributing cells and grouping T cells based on functional markers, hDirect-MAP can provide a generalizable and accurate predictive biomarker.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Oncology
Gang Xue, Ningbo Zheng, Jing Fang, Guangxu Jin, Xiaoyin Li, Gianpietro Dotti, Qing Yi, Yong Lu
Summary: This research demonstrates that tumor-specific Th9 cells have a unique capacity to eliminate advanced tumors containing ALVs, attributed to their enhanced direct tumor cell killing and interferon alpha/beta-induced bystander antitumor effects. This is due to the accumulation of extracellular ATP within the tumor microenvironment, promoting immune cell infiltration and antitumor cytokine production.
Article
Oncology
Tadas K. Rimkus, Austin B. Arrigo, Dongqin Zhu, Richard L. Carpenter, Sherona Sirkisoon, Daniel Doheny, Angelina T. Regua, Grace L. Wong, Sara Manore, Calvin Wagner, Hui-Kuan Lin, Guangxu Jin, Jimmy Ruiz, Michael Chan, Waldemar Debinski, Hui-Wen Lo
Summary: The study found that TUSC2 protein expression is reduced in glioblastoma compared to normal brain due to protein destabilization. NEDD4-mediated polyubiquitination is a novel mechanism for TUSC2 degradation in glioblastoma. TUSC2 loss promotes glioblastoma progression through upregulation of Bcl-xL and its knockout gene signature predicts poor patient survival.
Correction
Oncology
Fei Xing, Yin Liu, Shih-Ying Wu, Kerui Wu, Sambad Sharma, Yin-Yuan Mo, Jiamei Feng, Stephanie Sanders, Guangxu Jin, Ravi Singh, Pierre-Alexandre Vidi, Abhishek Tyagi, Michael D. Chan, Jimmy Ruiz, Waldemar Debinski, Boris C. Pasche, Hui-Wen Lo, Linda J. Metheny-Barlow, Ralph B. D'Agostino, Kounosuke Watabe
Article
Oncology
Ashok K. Pullikuth, Eric D. Routh, Kip D. Zimmerman, Julia Chifman, Jeff W. Chou, Michael H. Soike, Guangxu Jin, Jing Su, Qianqian Song, Michael A. Black, Cristin Print, Davide Bedognetti, Marissa Howard-McNatt, Stacey S. O'Neill, Alexandra Thomas, Carl D. Langefeld, Alexander B. Sigalov, Yong Lu, Lance D. Miller
Summary: High TREM-1 expression is associated with poor prognosis in breast cancer, potentially contributing to tumor progression and immune suppression through myeloid cells.
FRONTIERS IN ONCOLOGY
(2021)
Article
Engineering, Biomedical
Gang Xue, Ziyu Wang, Ningbo Zheng, Jing Fang, Chengqiong Mao, Xiaoyin Li, Guangxu Jin, Xin Ming, Yong Lu
Summary: Targeting tumor cells can promote resistance to immune checkpoint blockade therapy, but this resistance can be overcome by depleting tumor cells and immunosuppressive cells simultaneously using a monoclonal antibody binding CD73 enzyme. This approach prevents tumors from acquiring resistance to immune checkpoint blockade and leads to the eradication of advanced tumors.
NATURE BIOMEDICAL ENGINEERING
(2021)
Article
Biochemical Research Methods
Hongyu Zhao, Kun Han, Chao Gao, Vithal Madhira, Umit Topaloglu, Yong Lu, Guangxu Jin
Summary: This article introduces a method called VOC-alarm to predict variant of concern (VOCs) and their impact on COVID surges by analyzing mutations in SARS-CoV-2 sequences. The study found that VOCs rely on lineage-level entropy of mutation numbers to compete with other variants, highlighting the importance of population-level mutations in virus evolution. By simulating the mutational process, the authors predicted the mutation patterns of Alpha, Delta, Delta Plus, and Omicron in different stages, and also predicted that Omicron could lead to another COVID surge from January 2022 to March 2022.
Article
Oncology
Sherona R. Sirkisoon, Grace L. Wong, Noah R. Aguayo, Daniel L. Doheny, Dongqin Zhu, Angelina T. Regua, Austin Arrigo, Sara G. Manore, Calvin Wagner, Alexandra Thomas, Ravi Singh, Fei Xing, Guangxu Jin, Kounosuke Watabe, Hui-Wen Lo
Summary: This study demonstrates that miR-1290 and miR-1246 derived from extracellular vesicles (EVs) secreted by breast cancer cells activate astrocytes, promoting brain metastasis. The activation of astrocytes is mediated by the suppression of FOXA2 and subsequent secretion of CNTF cytokine through the EV-miR-1290 -> FOXA2 -> CNTF signaling axis.
Article
Oncology
Ningbo Zheng, Jing Fang, Gang Xue, Ziyu Wang, Xiaoyin Li, Mengshi Zhou, Guangxu Jin, Masmudur M. Rahman, Grant McFadden, Yong Lu
Summary: In this study, tumor-specific T cells infected with myxoma virus were used to deliver the virus into solid tumors, overcoming primary resistance. In addition to inducing classic tumor cell apoptosis and pyroptosis, these infected T cells were found to induce a special type of cell death called tumor cell autosis, which helps to restrain tumor antigen escape.
Meeting Abstract
Oncology
Grace L. Wong, Sherona R. Sirkisoon, Noah R. Aguayo, Daniel L. Doheny, Dongqin Zhu, Angelina T. Regua, Austin Arrigo, Sara G. Manore, Calvin J. Wagner, Alexandra Thomas, Ravi Singh, Fei Xing, Guangxu Jin, Kounosuke Watabe, Hui-Wen Lo
Meeting Abstract
Oncology
Angelina T. Regua, Noah R. Aguayo, Sara Abu Jalboush, Daniel L. Doheny, Sara G. Manore, Dongqin Zhu, Grace L. Wong, Austin Arrigo, Calvin J. Wagner, Yang Yu, Karen Baylon, Alexandra Thomas, Michael D. Chan, Jimmy Ruiz, Guangxu Jin, Roy E. Strowd, Peiqing Sun, Linda J. Metheny-Barlow, Jiayuh Lin, Hui-Wen Lo