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Zhang S, Xiao X, Yi Y, Wang X, Zhu L, Shen Y, Lin D, Wu C. Tumor initiation and early tumorigenesis: molecular mechanisms and interventional targets. Signal Transduct Target Ther 2024; 9:149. [PMID: 38890350 PMCID: PMC11189549 DOI: 10.1038/s41392-024-01848-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 04/23/2024] [Accepted: 04/27/2024] [Indexed: 06/20/2024] Open
Abstract
Tumorigenesis is a multistep process, with oncogenic mutations in a normal cell conferring clonal advantage as the initial event. However, despite pervasive somatic mutations and clonal expansion in normal tissues, their transformation into cancer remains a rare event, indicating the presence of additional driver events for progression to an irreversible, highly heterogeneous, and invasive lesion. Recently, researchers are emphasizing the mechanisms of environmental tumor risk factors and epigenetic alterations that are profoundly influencing early clonal expansion and malignant evolution, independently of inducing mutations. Additionally, clonal evolution in tumorigenesis reflects a multifaceted interplay between cell-intrinsic identities and various cell-extrinsic factors that exert selective pressures to either restrain uncontrolled proliferation or allow specific clones to progress into tumors. However, the mechanisms by which driver events induce both intrinsic cellular competency and remodel environmental stress to facilitate malignant transformation are not fully understood. In this review, we summarize the genetic, epigenetic, and external driver events, and their effects on the co-evolution of the transformed cells and their ecosystem during tumor initiation and early malignant evolution. A deeper understanding of the earliest molecular events holds promise for translational applications, predicting individuals at high-risk of tumor and developing strategies to intercept malignant transformation.
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Affiliation(s)
- Shaosen Zhang
- Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
- Key Laboratory of Cancer Genomic Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
| | - Xinyi Xiao
- Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
- Key Laboratory of Cancer Genomic Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
| | - Yonglin Yi
- Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
- Key Laboratory of Cancer Genomic Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
| | - Xinyu Wang
- Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
- Key Laboratory of Cancer Genomic Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
| | - Lingxuan Zhu
- Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
- Key Laboratory of Cancer Genomic Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
- Changping Laboratory, 100021, Beijing, China
| | - Yanrong Shen
- Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
- Key Laboratory of Cancer Genomic Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
| | - Dongxin Lin
- Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China.
- Key Laboratory of Cancer Genomic Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China.
- Changping Laboratory, 100021, Beijing, China.
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, 211166, China.
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, 510060, China.
| | - Chen Wu
- Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China.
- Key Laboratory of Cancer Genomic Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China.
- Changping Laboratory, 100021, Beijing, China.
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, 211166, China.
- CAMS Oxford Institute, Chinese Academy of Medical Sciences, 100006, Beijing, China.
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2
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Li Y, Yang R. PxBLAT: an efficient python binding library for BLAT. BMC Bioinformatics 2024; 25:219. [PMID: 38898394 DOI: 10.1186/s12859-024-05844-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 06/13/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND With the surge in genomic data driven by advancements in sequencing technologies, the demand for efficient bioinformatics tools for sequence analysis has become paramount. BLAST-like alignment tool (BLAT), a sequence alignment tool, faces limitations in performance efficiency and integration with modern programming environments, particularly Python. This study introduces PxBLAT, a Python-based framework designed to enhance the capabilities of BLAT, focusing on usability, computational efficiency, and seamless integration within the Python ecosystem. RESULTS PxBLAT demonstrates significant improvements over BLAT in execution speed and data handling, as evidenced by comprehensive benchmarks conducted across various sample groups ranging from 50 to 600 samples. These experiments highlight a notable speedup, reducing execution time compared to BLAT. The framework also introduces user-friendly features such as improved server management, data conversion utilities, and shell completion, enhancing the overall user experience. Additionally, the provision of extensive documentation and comprehensive testing supports community engagement and facilitates the adoption of PxBLAT. CONCLUSIONS PxBLAT stands out as a robust alternative to BLAT, offering performance and user interaction enhancements. Its development underscores the potential for modern programming languages to improve bioinformatics tools, aligning with the needs of contemporary genomic research. By providing a more efficient, user-friendly tool, PxBLAT has the potential to impact genomic data analysis workflows, supporting faster and more accurate sequence analysis in a Python environment.
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Affiliation(s)
- Yangyang Li
- Department of Urology, Northwestern University Feinberg School of Medicine, 303 E Superior St, Chicago, IL, 60611, USA
| | - Rendong Yang
- Department of Urology, Northwestern University Feinberg School of Medicine, 303 E Superior St, Chicago, IL, 60611, USA.
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, 675 N St Clair St, Chicago, IL, 60611, USA.
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3
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Wang L, Zhou J, Wang X, Wang Y, Li J. MCDHGN: heterogeneous network-based cancer driver gene prediction and interpretability analysis. Bioinformatics 2024; 40:btae362. [PMID: 38867699 PMCID: PMC11256962 DOI: 10.1093/bioinformatics/btae362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 05/09/2024] [Accepted: 06/11/2024] [Indexed: 06/14/2024] Open
Abstract
MOTIVATION Accurately predicting the driver genes of cancer is of great significance for carcinogenesis progress research and cancer treatment. In recent years, more and more deep-learning-based methods have been used for predicting cancer driver genes. However, deep-learning algorithms often have black box properties and cannot interpret the output results. Here, we propose a novel cancer driver gene mining method based on heterogeneous network meta-paths (MCDHGN), which uses meta-path aggregation to enhance the interpretability of predictions. RESULTS MCDHGN constructs a heterogeneous network by using several types of multi-omics data that are biologically linked to genes. And the differential probabilities of SNV, DNA methylation, and gene expression data between cancerous tissues and normal tissues are extracted as initial features of genes. Nine meta-paths are manually selected, and the representation vectors obtained by aggregating information within and across meta-path nodes are used as new features for subsequent classification and prediction tasks. By comparing with eight homogeneous and heterogeneous network models on two pan-cancer datasets, MCDHGN has better performance on AUC and AUPR values. Additionally, MCDHGN provides interpretability of predicted cancer driver genes through the varying weights of biologically meaningful meta-paths. AVAILABILITY AND IMPLEMENTATION https://github.com/1160300611/MCDHGN.
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Affiliation(s)
- Lexiang Wang
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
| | - Jingli Zhou
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
| | - Xuan Wang
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
- Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
| | - Yadong Wang
- Center for Bioinformatics, Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
- Ministry of Education, Key Laboratory of Biological Bigdata, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Junyi Li
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
- Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
- Ministry of Education, Key Laboratory of Biological Bigdata, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
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4
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Jung S, Wang S, Lee D. CancerGATE: Prediction of cancer-driver genes using graph attention autoencoders. Comput Biol Med 2024; 176:108568. [PMID: 38744009 DOI: 10.1016/j.compbiomed.2024.108568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 04/13/2024] [Accepted: 05/05/2024] [Indexed: 05/16/2024]
Abstract
Discovery of the cancer type specific-driver genes is important for understanding the molecular mechanisms of each cancer type and for providing proper treatment. Recently, graph deep learning methods became widely used in finding cancer-driver genes. However, previous methods had limited performance in individual cancer types due to a small number of cancer-driver genes used in training and biases toward the cancer-driver genes used in training the models. Here, we introduce a novel pipeline, CancerGATE that predicts the cancer-driver genes using graph attention autoencoder (GATE) to learn in a self-supervised manner and can be applied to each of the cancer types. CancerGATE utilizes biological network topology and multi-omics data from 15 types of cancer of 20,079 samples from the cancer genome atlas (TCGA). Attention coefficients calculated in the model are used to prioritize cancer-driver genes by comparing coefficients of cancer and normal contexts. CancerGATE shows a higher AUPRC with a difference ranging from 1.5 % to 36.5 % compared to the previous graph deep learning models in each cancer type. We also show that CancerGATE is free from the bias toward cancer-driver genes used in training, revealing mechanisms of the cancer-driver genes in specific cancer types. Finally, we propose novel cancer-driver gene candidates that could be therapeutic targets for specific cancer types.
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Affiliation(s)
- Seunghwan Jung
- Department of Bio and Brain Engineering, KAIST, Daejeon 34141, Republic of Korea.
| | - Seunghyun Wang
- Department of Bio and Brain Engineering, KAIST, Daejeon 34141, Republic of Korea.
| | - Doheon Lee
- Department of Bio and Brain Engineering, KAIST, Daejeon 34141, Republic of Korea.
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5
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Qiao L, Li C, Lin W, He X, Mi J, Tong Y, Gao J. ViroISDC: a method for calling integration sites of hepatitis B virus based on feature encoding. BMC Bioinformatics 2024; 25:177. [PMID: 38704528 PMCID: PMC11070082 DOI: 10.1186/s12859-024-05763-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 03/26/2024] [Indexed: 05/06/2024] Open
Abstract
BACKGROUND Hepatitis B virus (HBV) integrates into human chromosomes and can lead to genomic instability and hepatocarcinogenesis. Current tools for HBV integration site detection lack accuracy and stability. RESULTS This study proposes a deep learning-based method, named ViroISDC, for detecting integration sites. ViroISDC generates corresponding grammar rules and encodes the characteristics of the language data to predict integration sites accurately. Compared with Lumpy, Pindel, Seeksv, and SurVirus, ViroISDC exhibits better overall performance and is less sensitive to sequencing depth and integration sequence length, displaying good reliability, stability, and generality. Further downstream analysis of integrated sites detected by ViroISDC reveals the integration patterns and features of HBV. It is observed that HBV integration exhibits specific chromosomal preferences and tends to integrate into cancerous tissue. Moreover, HBV integration frequency was higher in males than females, and high-frequency integration sites were more likely to be present on hepatocarcinogenesis- and anti-cancer-related genes, validating the reliability of the ViroISDC. CONCLUSIONS ViroISDC pipeline exhibits superior precision, stability, and reliability across various datasets when compared to similar software. It is invaluable in exploring HBV infection in the human body, holding significant implications for the diagnosis, treatment, and prognosis assessment of HCC.
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Affiliation(s)
- Lei Qiao
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Chang Li
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Wei Lin
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Xiaoqi He
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Jia Mi
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Yigang Tong
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China.
| | - Jingyang Gao
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China.
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6
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Fűr GM, Nemes K, Magó É, Benő AÁ, Topolcsányi P, Moldvay J, Pongor LS. Applied models and molecular characteristics of small cell lung cancer. Pathol Oncol Res 2024; 30:1611743. [PMID: 38711976 PMCID: PMC11070512 DOI: 10.3389/pore.2024.1611743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 04/03/2024] [Indexed: 05/08/2024]
Abstract
Small cell lung cancer (SCLC) is a highly aggressive type of cancer frequently diagnosed with metastatic spread, rendering it surgically unresectable for the majority of patients. Although initial responses to platinum-based therapies are often observed, SCLC invariably relapses within months, frequently developing drug-resistance ultimately contributing to short overall survival rates. Recently, SCLC research aimed to elucidate the dynamic changes in the genetic and epigenetic landscape. These have revealed distinct subtypes of SCLC, each characterized by unique molecular signatures. The recent understanding of the molecular heterogeneity of SCLC has opened up potential avenues for precision medicine, enabling the development of targeted therapeutic strategies. In this review, we delve into the applied models and computational approaches that have been instrumental in the identification of promising drug candidates. We also explore the emerging molecular diagnostic tools that hold the potential to transform clinical practice and patient care.
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Affiliation(s)
- Gabriella Mihalekné Fűr
- Cancer Genomics and Epigenetics Core Group, Hungarian Centre of Excellence for Molecular Medicine (HCEMM), Szeged, Hungary
| | - Kolos Nemes
- Cancer Genomics and Epigenetics Core Group, Hungarian Centre of Excellence for Molecular Medicine (HCEMM), Szeged, Hungary
| | - Éva Magó
- Cancer Genomics and Epigenetics Core Group, Hungarian Centre of Excellence for Molecular Medicine (HCEMM), Szeged, Hungary
- Genome Integrity and DNA Repair Core Group, Hungarian Centre of Excellence for Molecular Medicine (HCEMM), Szeged, Hungary
| | - Alexandra Á. Benő
- Cancer Genomics and Epigenetics Core Group, Hungarian Centre of Excellence for Molecular Medicine (HCEMM), Szeged, Hungary
| | - Petronella Topolcsányi
- Cancer Genomics and Epigenetics Core Group, Hungarian Centre of Excellence for Molecular Medicine (HCEMM), Szeged, Hungary
| | - Judit Moldvay
- Department of Pulmonology, Szeged University Szent-Gyorgyi Albert Medical School, Szeged, Hungary
- 1st Department of Pulmonology, National Koranyi Institute of Pulmonology, Budapest, Hungary
| | - Lőrinc S. Pongor
- Cancer Genomics and Epigenetics Core Group, Hungarian Centre of Excellence for Molecular Medicine (HCEMM), Szeged, Hungary
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7
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Niu R, Guo Y, Shang X. GLIMS: A two-stage gradual-learning method for cancer genes prediction using multi-omics data and co-splicing network. iScience 2024; 27:109387. [PMID: 38510118 PMCID: PMC10951990 DOI: 10.1016/j.isci.2024.109387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/30/2023] [Accepted: 02/27/2024] [Indexed: 03/22/2024] Open
Abstract
Identifying cancer genes is vital for cancer diagnosis and treatment. However, because of the complexity of cancer occurrence and limited cancer genes knowledge, it is hard to identify cancer genes accurately using only a few omics data, and the overall performance of existing methods is being called for further improvement. Here, we introduce a two-stage gradual-learning strategy GLIMS to predict cancer genes using integrative features from multi-omics data. Firstly, it uses a semi-supervised hierarchical graph neural network to predict the initial candidate cancer genes by integrating multi-omics data and protein-protein interaction (PPI) network. Then, it uses an unsupervised approach to further optimize the initial prediction by integrating the co-splicing network in post-transcriptional regulation, which plays an important role in cancer development. Systematic experiments on multi-omics cancer data demonstrated that GLIMS outperforms the state-of-the-art methods for the identification of cancer genes and it could be a useful tool to help advance cancer analysis.
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Affiliation(s)
- Rui Niu
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
| | - Yang Guo
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
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8
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Kacar Z, Slud E, Levy D, Candia J, Budhu A, Forgues M, Wu X, Raziuddin A, Tran B, Shetty J, Pomyen Y, Chaisaingmongkol J, Rabibhadana S, Pupacdi B, Bhudhisawasdi V, Lertprasertsuke N, Auewarakul C, Sangrajrang S, Mahidol C, Ruchirawat M, Wang XW. Characterization of tumor evolution by functional clonality and phylogenetics in hepatocellular carcinoma. Commun Biol 2024; 7:383. [PMID: 38553628 PMCID: PMC11245610 DOI: 10.1038/s42003-024-06040-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 03/11/2024] [Indexed: 04/02/2024] Open
Abstract
Hepatocellular carcinoma (HCC) is a molecularly heterogeneous solid malignancy, and its fitness may be shaped by how its tumor cells evolve. However, ability to monitor tumor cell evolution is hampered by the presence of numerous passenger mutations that do not provide any biological consequences. Here we develop a strategy to determine the tumor clonality of three independent HCC cohorts of 524 patients with diverse etiologies and race/ethnicity by utilizing somatic mutations in cancer driver genes. We identify two main types of tumor evolution, i.e., linear, and non-linear models where non-linear type could be further divided into classes, which we call shallow branching and deep branching. We find that linear evolving HCC is less aggressive than other types. GTF2IRD2B mutations are enriched in HCC with linear evolution, while TP53 mutations are the most frequent genetic alterations in HCC with non-linear models. Furthermore, we observe significant B cell enrichment in linear trees compared to non-linear trees suggesting the need for further research to uncover potential variations in immune cell types within genomically determined phylogeny types. These results hint at the possibility that tumor cells and their microenvironment may collectively influence the tumor evolution process.
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Affiliation(s)
- Zeynep Kacar
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA
- Department of Mathematics, University of Maryland, College Park, MD, 20742, USA
| | - Eric Slud
- Department of Mathematics, University of Maryland, College Park, MD, 20742, USA
| | - Doron Levy
- Department of Mathematics, University of Maryland, College Park, MD, 20742, USA
| | - Julián Candia
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, 21224, USA
| | - Anuradha Budhu
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA
- Liver Cancer Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA
| | - Marshonna Forgues
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA
| | - Xiaolin Wu
- Cancer Research Technology Program, Frederick, MD, 21702, USA
| | - Arati Raziuddin
- Cancer Research Technology Program, Frederick, MD, 21702, USA
| | - Bao Tran
- Cancer Research Technology Program, Frederick, MD, 21702, USA
| | - Jyoti Shetty
- Cancer Research Technology Program, Frederick, MD, 21702, USA
| | - Yotsawat Pomyen
- Laboratory of Chemical Carcinogenesis, Chulabhorn Research Institute, Bangkok, 10210, Thailand
| | | | - Siritida Rabibhadana
- Laboratory of Chemical Carcinogenesis, Chulabhorn Research Institute, Bangkok, 10210, Thailand
| | - Benjarath Pupacdi
- Laboratory of Chemical Carcinogenesis, Chulabhorn Research Institute, Bangkok, 10210, Thailand
| | | | | | - Chirayu Auewarakul
- Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy, Bangkok, 10210, Thailand
| | | | - Chulabhorn Mahidol
- Laboratory of Chemical Carcinogenesis, Chulabhorn Research Institute, Bangkok, 10210, Thailand
| | - Mathuros Ruchirawat
- Laboratory of Chemical Carcinogenesis, Chulabhorn Research Institute, Bangkok, 10210, Thailand
- Center of Excellence on Environmental Health and Toxicology (EHT), OPS, MHESI, Bangkok, Thailand
| | - Xin Wei Wang
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA.
- Liver Cancer Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA.
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9
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Perea J, García JL, Corchete LA, Martí M, Hernández-Villafranca S, Alcázar JA, Álvaro E, Hurtado E, Jiménez-Toscano M, Balaguer F, Ballestero A, López-Rojo I, Jiménez F, Sanz G, Melone S, Brandáriz L, Vivas A, Alvarellos A, González-Sarmiento R. Chromosomal breaks: another differential gap between early-onset and late-onset colorectal cancers. Br J Surg 2024; 111:znae041. [PMID: 38430195 DOI: 10.1093/bjs/znae041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/16/2024] [Accepted: 01/22/2024] [Indexed: 03/03/2024]
Affiliation(s)
- José Perea
- Molecular Medicine Unit-Department of Medicine, Institute of Biomedical Research of Salamanca (IBSAL) and Institute of Molecular and Cellular Biology of Cancer (IBMCC), University of Salamanca-SACYL-CSIC, Salamanca, Spain
- Surgery Department, Vithas Arturo Soria Hospital, Madrid, Spain
| | - Juan Luis García
- Molecular Medicine Unit-Department of Medicine, Institute of Biomedical Research of Salamanca (IBSAL) and Institute of Molecular and Cellular Biology of Cancer (IBMCC), University of Salamanca-SACYL-CSIC, Salamanca, Spain
| | - Luis A Corchete
- Haematology Department, University Hospital of Salamanca, Institute of Biomedical Research of Salamanca (IBSAL), Salamanca, Spain
- Cancer Research Centre-IBMCC (USAL-CSIC), Salamanca, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
| | - Marc Martí
- Colorectal Unit, Vall d'Hebrón University Hospital, Barcelona, Spain
| | | | - José A Alcázar
- Surgery Department, Clinic University Hospital, Salamanca, Spain
| | - Edurne Álvaro
- Surgery Department, Infanta Leonor University Hospital, Madrid, Spain
| | - Elena Hurtado
- Surgery Department, Gregorio Marañon University Hospital, Madrid, Spain
| | | | - Francesc Balaguer
- Gastroenterology Department, Clinic University Hospital, Barcelona, Spain
| | | | - Irene López-Rojo
- Surgery Department, MD Anderson Cancer Center Madrid, Madrid, Spain
| | | | - Gonzalo Sanz
- Surgery Department, San Carlos University Hospital, Madrid, Spain
| | - Sirio Melone
- Surgery Department, Alcorcon Foundation Hospital, Madrid, Spain
| | | | - Alfredo Vivas
- Surgery Department, 12 de Octubre University Hospital, Madrid, Spain
| | - Alicia Alvarellos
- Surgery Department, University Clinic of Navarra-Madrid, Madrid, Spain
| | - Rogelio González-Sarmiento
- Molecular Medicine Unit-Department of Medicine, Institute of Biomedical Research of Salamanca (IBSAL) and Institute of Molecular and Cellular Biology of Cancer (IBMCC), University of Salamanca-SACYL-CSIC, Salamanca, Spain
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10
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Adine C, Fernando K, Ho NCW, Quah HS, Ho SSW, Wu KZ, Teng KWW, Arcinas C, Li L, Ha K, Chew JWL, Wang C, Too NSH, Yeong JPS, Tan DSW, Tan IBH, Nagadia R, Chia CS, Macalinao D, Bhuvaneswari H, Iyer NG, Fong ELS. Bioengineered hydrogels enhance ex vivo preservation of patient-derived tumor explants for drug evaluation. Biomaterials 2024; 305:122460. [PMID: 38246018 DOI: 10.1016/j.biomaterials.2023.122460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 12/31/2023] [Indexed: 01/23/2024]
Abstract
Ex vivo patient-derived tumor slices (PDTS) are currently limited by short-term viability in culture. Here, we show how bioengineered hydrogels enable the identification of key matrix parameters that significantly enhance PDTS viability compared to conventional culture systems. As demonstrated using single-cell RNA sequencing and high-dimensional flow cytometry, hydrogel-embedded PDTS tightly preserved cancer, cancer-associated fibroblast, and various immune cell populations and subpopulations in the corresponding original tumor. Cell-cell communication networks within the tumor microenvironment, including immune checkpoint ligand-receptor interactions, were also maintained. Remarkably, our results from a co-clinical trial suggest hydrogel-embedded PDTS may predict sensitivity to immune checkpoint inhibitors (ICIs) in head and neck cancer patients. Further, we show how these longer term-cultured tumor explants uniquely enable the sampling and detection of temporal evolution in molecular readouts when treated with ICIs. By preserving the compositional heterogeneity and complexity of patient tumors, hydrogel-embedded PDTS provide a valuable tool to facilitate experiments targeting the tumor microenvironment.
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Affiliation(s)
- Christabella Adine
- The N.1 Institute for Health, National University of Singapore, Singapore
| | - Kanishka Fernando
- Department of Biomedical Engineering, National University of Singapore, Singapore
| | | | - Hong Sheng Quah
- National Cancer Centre Singapore, Singapore; Duke-NUS Medical School, National University of Singapore, Singapore
| | | | - Kenny Zhuoran Wu
- Department of Biomedical Engineering, National University of Singapore, Singapore
| | | | - Camille Arcinas
- National Cancer Centre Singapore, Singapore; Duke-NUS Medical School, National University of Singapore, Singapore
| | - Ling Li
- Translational Medicine Research Centre, MSD, Singapore
| | - Kelly Ha
- Department of Biomedical Engineering, National University of Singapore, Singapore
| | - Joey Wei Ling Chew
- Department of Biomedical Engineering, National University of Singapore, Singapore
| | - Chenhui Wang
- Department of Biomedical Engineering, National University of Singapore, Singapore
| | | | - Joe Poh Sheng Yeong
- Institute for Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore
| | | | | | - Rahul Nagadia
- Department of Head and Neck Surgery, National Cancer Centre Singapore, Singapore; Department of Oral and Maxillofacial Surgery, National Dental Centre Singapore, Singapore; Department of Dental Medicine, Karolinska Institute, Stockholm, Sweden
| | | | | | | | - N Gopalakrishna Iyer
- National Cancer Centre Singapore, Singapore; Duke-NUS Medical School, National University of Singapore, Singapore.
| | - Eliza Li Shan Fong
- The N.1 Institute for Health, National University of Singapore, Singapore; Department of Biomedical Engineering, National University of Singapore, Singapore; Cancer Science Institute, National University of Singapore, Singapore.
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11
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Song J, Song Z, Zhang J, Gong Y. Privacy-Preserving Identification of Cancer Subtype-Specific Driver Genes Based on Multigenomics Data with Privatedriver. J Comput Biol 2024; 31:99-116. [PMID: 38271572 DOI: 10.1089/cmb.2023.0115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2024] Open
Abstract
Identifying cancer subtype-specific driver genes from a large number of irrelevant passengers is crucial for targeted therapy in cancer treatment. Recently, the rapid accumulation of large-scale cancer genomics data from multiple institutions has presented remarkable opportunities for identification of cancer subtype-specific driver genes. However, the insufficient subtype samples, privacy issues, and heterogenous of aberration events pose great challenges in precisely identifying cancer subtype-specific driver genes. To address this, we introduce privatedriver, the first model for identifying subtype-specific driver genes that integrates genomics data from multiple institutions in a data privacy-preserving collaboration manner. The process of identifying subtype-specific cancer driver genes using privatedriver involves the following two steps: genomics data integration and collaborative training. In the integration process, the aberration events from multiple genomics data sources are combined for each institution using the forward and backward propagation method of NetICS. In the collaborative training process, each institution utilizes the federated learning framework to upload encrypted model parameters instead of raw data of all institutions to train a global model by using the non-negative matrix factorization algorithm. We applied privatedriver on head and neck squamous cell and colon cancer from The Cancer Genome Atlas website and evaluated it with two benchmarks using macro-Fscore. The comparison analysis demonstrates that privatedriver achieves comparable results to centralized learning models and outperforms most other nonprivacy preserving models, all while ensuring the confidentiality of patient information. We also demonstrate that, for varying predicted driver gene distributions in subtype, our model fully considers the heterogeneity of subtype and identifies subtype-specific driver genes corresponding to the given prognosis and therapeutic effect. The success of privatedriver reveals the feasibility and effectiveness of identifying cancer subtype-specific driver genes in a data protection manner, providing new insights for future privacy-preserving driver gene identification studies.
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Affiliation(s)
- Junrong Song
- School of Information; Kunming, P.R. China
- Yunnan Key Laboratory of Service Computing; Yunnan University of Finance and Economics, Kunming, P.R. China
| | - Zhiming Song
- School of Information; Kunming, P.R. China
- Yunnan Key Laboratory of Service Computing; Yunnan University of Finance and Economics, Kunming, P.R. China
| | - Jinpeng Zhang
- School of Information; Kunming, P.R. China
- Yunnan Key Laboratory of Service Computing; Yunnan University of Finance and Economics, Kunming, P.R. China
- The School of Computer Science and Engineering, Yunnan University, Kunming, P.R. China
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12
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Sudhakaran M, Navarrete TG, Mejía-Guerra K, Mukundi E, Eubank TD, Grotewold E, Arango D, Doseff AI. Transcriptome reprogramming through alternative splicing triggered by apigenin drives cell death in triple-negative breast cancer. Cell Death Dis 2023; 14:824. [PMID: 38092740 PMCID: PMC10719380 DOI: 10.1038/s41419-023-06342-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 11/19/2023] [Accepted: 11/27/2023] [Indexed: 12/17/2023]
Abstract
Triple-negative breast cancer (TNBC) is characterized by its aggressiveness and resistance to cancer-specific transcriptome alterations. Alternative splicing (AS) is a major contributor to the diversification of cancer-specific transcriptomes. The TNBC transcriptome landscape is characterized by aberrantly spliced isoforms that promote tumor growth and resistance, underscoring the need to identify approaches that reprogram AS circuitry towards transcriptomes, favoring a delay in tumorigenesis or responsiveness to therapy. We have previously shown that flavonoid apigenin is associated with splicing factors, including heterogeneous nuclear ribonucleoprotein A2 (hnRNPA2). Here, we showed that apigenin reprograms TNBC-associated AS transcriptome-wide. The AS events affected by apigenin were statistically enriched in hnRNPA2 substrates. Comparative transcriptomic analyses of human TNBC tumors and non-tumor tissues showed that apigenin can switch cancer-associated alternative spliced isoforms (ASI) to those found in non-tumor tissues. Apigenin preferentially affects the splicing of anti-apoptotic and proliferation factors, which are uniquely observed in cancer cells, but not in non-tumor cells. Apigenin switches cancer-associated aberrant ASI in vivo in TNBC xenograft mice by diminishing proliferation and increasing pro-apoptotic ASI. In accordance with these findings, apigenin increased apoptosis and reduced tumor proliferation, thereby halting TNBC growth in vivo. Our results revealed that apigenin reprograms transcriptome-wide TNBC-specific AS, thereby inducing apoptosis and hindering tumor growth. These findings underscore the impactful effects of nutraceuticals in altering cancer transcriptomes, offering new options to influence outcomes in TNBC treatments.
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Affiliation(s)
- Meenakshi Sudhakaran
- Molecular, Cellular, and Integrative Physiology Graduate Program, Michigan State University, East Lansing, MI, USA
| | - Tatiana García Navarrete
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
| | | | - Eric Mukundi
- Department of Molecular Genetics, The Ohio State University, Columbus, OH, USA
| | - Timothy D Eubank
- Department of Microbiology, Immunology & Cell Biology, West Virginia University, Morgantown, WV, USA
| | - Erich Grotewold
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
| | - Daniel Arango
- Department of Pharmacology and Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
| | - Andrea I Doseff
- Department of Physiology and Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI, USA.
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13
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Brandt VP, Holland H, Wallenborn M, Koschny R, Frydrychowicz C, Richter M, Holland L, Nestler U, Sander C. SNP array genomic analysis of matched pairs of brain and liver metastases in primary colorectal cancer. J Cancer Res Clin Oncol 2023; 149:18173-18183. [PMID: 38010391 PMCID: PMC10725338 DOI: 10.1007/s00432-023-05505-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 10/31/2023] [Indexed: 11/29/2023]
Abstract
PURPOSE Brain metastasis formation is a rare and late event in colorectal cancer (CRC) patients and associated with poor survival. In contrast to other metastatic sites, the knowledge on chromosomal aberrations in brain metastases is very limited. METHODS Therefore, we carried out single nucleotide polymorphism (SNP) array analyses on matched primary CRC and brain metastases of four patients as well as on liver metastases of three patients. RESULTS Brain metastases showed more chromosomal aberrations than primary tumors or liver metastases. Commonly occurring aberrations were gain of 8q11.1-q24.3 (primary CRC), gain of 13q12.13-q12.3 (liver metastases), and gain of 20q11.1-q13.33 (brain metastases). Furthermore, we found one copy-neutral loss of heterozygosity (cn-LOH) region on chromosome 3 in primary CRC, three cn-LOH regions in liver metastases and 23 cn-LOH regions in brain metastases, comprising 26 previously undescribed sites. CONCLUSION The more frequent occurrence of cn-LOHs and subsequently affected genes in brain metastases shed light on the pathophysiology of brain metastasis formation. Further pairwise genetic analyses between primary tumors and their metastases will help to define the role of affected genes in cn-LOH regions.
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Affiliation(s)
- Vivian-Pascal Brandt
- Department of Neurosurgery, University Hospital Leipzig, Leipzig, Saxony, Germany.
- Saxonian Incubator for Clinical Translation (SIKT), University of Leipzig, Leipzig, Saxony, Germany.
| | - Heidrun Holland
- Saxonian Incubator for Clinical Translation (SIKT), University of Leipzig, Leipzig, Saxony, Germany
| | - Marco Wallenborn
- Department of Neurosurgery, University Hospital Leipzig, Leipzig, Saxony, Germany
- Saxonian Incubator for Clinical Translation (SIKT), University of Leipzig, Leipzig, Saxony, Germany
| | - Ronald Koschny
- Interdisciplinary Endoscopy Center (IEZ), Department of Gastroenterology and Hepatology, University Hospital Heidelberg, Heidelberg, Baden-Wuerttemberg, Germany
| | - Clara Frydrychowicz
- Paul Flechsig Institute of Neuropathology, University Medicine Leipzig, Leipzig, Saxony, Germany
| | - Mandy Richter
- Saxonian Incubator for Clinical Translation (SIKT), University of Leipzig, Leipzig, Saxony, Germany
| | - Lydia Holland
- Saxonian Incubator for Clinical Translation (SIKT), University of Leipzig, Leipzig, Saxony, Germany
| | - Ulf Nestler
- Department of Neurosurgery, University Hospital Leipzig, Leipzig, Saxony, Germany
| | - Caroline Sander
- Department of Neurosurgery, University Hospital Leipzig, Leipzig, Saxony, Germany
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14
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Liu J, Ma F, Zhu Y, Zhang N, Kong L, Mi J, Cong H, Gao R, Wang M, Zhang Y. MaxCLK: discovery of cancer driver genes via maximal clique and information entropy of modules. Bioinformatics 2023; 39:btad737. [PMID: 38065693 PMCID: PMC10739565 DOI: 10.1093/bioinformatics/btad737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 11/10/2023] [Accepted: 12/07/2023] [Indexed: 12/23/2023] Open
Abstract
MOTIVATION Cancer is caused by the accumulation of somatic mutations in multiple pathways, in which driver mutations are typically of the properties of high coverage and high exclusivity in patients. Identifying cancer driver genes has a pivotal role in understanding the mechanisms of oncogenesis and treatment. RESULTS Here, we introduced MaxCLK, an algorithm for identifying cancer driver genes, which was developed by an integrated analysis of somatic mutation data and protein-protein interaction (PPI) networks and further improved by an information entropy index. Tested on pancancer and single cancers, MaxCLK outperformed other existing methods with higher accuracy. About pancancer, we predicted 154 driver genes and 787 driver modules. The analysis of co-occurrence and exclusivity between modules and pathways reveals the correlation of their combinations. Overall, our study has deepened the understanding of driver mechanism in PPI topology and found novel driver genes. AVAILABILITY AND IMPLEMENTATION The source codes for MaxCLK are freely available at https://github.com/ShandongUniversityMasterMa/MaxCLK-main.
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Affiliation(s)
- Jian Liu
- School of Mathematics and Statistics, Shandong University at Weihai, Weihai, Shandong 264209, China
| | - Fubin Ma
- School of Mathematics and Statistics, Shandong University at Weihai, Weihai, Shandong 264209, China
| | - Yongdi Zhu
- School of Mathematics and Statistics, Shandong University at Weihai, Weihai, Shandong 264209, China
| | - Naiqian Zhang
- School of Mathematics and Statistics, Shandong University at Weihai, Weihai, Shandong 264209, China
| | - Lingming Kong
- Marine College, Shandong University at Weihai, Weihai, Shandong 264209, China
| | - Jia Mi
- Precision Medicine Research Center, School of Pharmacy, Binzhou Medical University, Yantai, Shandong 264003, China
| | - Haiyan Cong
- Department of Central Lab, Weihai Municipal Hospital, Weihai, Shandong 264209, China
| | - Rui Gao
- School of Control Science and Engineering, Shandong University, Jinan, Shandong 250100, China
| | - Mingyi Wang
- Department of Central Lab, Weihai Municipal Hospital, Weihai, Shandong 264209, China
- Department of Central Lab, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, Shandong 264200, China
| | - Yusen Zhang
- School of Mathematics and Statistics, Shandong University at Weihai, Weihai, Shandong 264209, China
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15
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Becchi T, Beltrame L, Mannarino L, Calura E, Marchini S, Romualdi C. A pan-cancer landscape of pathogenic somatic copy number variations. J Biomed Inform 2023; 147:104529. [PMID: 37858853 DOI: 10.1016/j.jbi.2023.104529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 09/13/2023] [Accepted: 10/16/2023] [Indexed: 10/21/2023]
Abstract
OBJECTIVE Copy number variations (CNVs) play crucial roles in physiological and pathological processes, including cancer. However, the functional implications of somatic CNVs in tumor progression and evolution remain unclear. This study focuses on identifying CNV alterations with high pathogenic potential that drive and sustain tumorigenesis, distinguishing them from passenger alterations that accumulate during tumor growth. Our goal is to explore the variability of CNVs across different tumor types and infer their impact on tumor cell functions. METHODS Starting from 7352 copy number profiles across 33 different cancer types, we infer the pathogenicity of each CNV and perform both intra- and inter-tumor analyses to predict the functional impact of different genomic patterns. We evaluate the actionability of genes belonging to altered regions and we correlate the presence of pathogenic regions with genome instability patterns and patients' survival. RESULTS Our analysis uncovered large heterogeneity among different tumors suggesting in many cases distinct genetic drivers of tumorigenesis. Recurrent genomic alterations frequently coincide with dysfunctional homologous recombination pathways and negative regulation of the immune system. In certain tumors, the number of pathogenic CNVs emerged as a prognostic biomarker, highlighting their significance in cancer progression. CONCLUSION This study contributes to elucidate the functional impact of pathogenic CNVs in tumor progression and sheds light on their potential as prognostic markers in specific cancer types.
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Affiliation(s)
- Tommaso Becchi
- Department of Biology, University of Padova, 35131 Padova, Italy
| | - Luca Beltrame
- Laboratory of Cancer Pharmacology, IRCCS Humanitas Research Hospital, via Manzoni 56, 20089 Rozzano - Milan, Italy
| | - Laura Mannarino
- Laboratory of Cancer Pharmacology, IRCCS Humanitas Research Hospital, via Manzoni 56, 20089 Rozzano - Milan, Italy; Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, 20072 Pieve Emanuele - Milan, Italy
| | - Enrica Calura
- Department of Biology, University of Padova, 35131 Padova, Italy
| | - Sergio Marchini
- Laboratory of Cancer Pharmacology, IRCCS Humanitas Research Hospital, via Manzoni 56, 20089 Rozzano - Milan, Italy
| | - Chiara Romualdi
- Department of Biology, University of Padova, 35131 Padova, Italy.
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16
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Kazachenka A, Loong JH, Attig J, Young GR, Ganguli P, Devonshire G, Grehan N, Ciccarelli FD, Fitzgerald RC, Kassiotis G. The transcriptional landscape of endogenous retroelements delineates esophageal adenocarcinoma subtypes. NAR Cancer 2023; 5:zcad040. [PMID: 37502711 PMCID: PMC10370457 DOI: 10.1093/narcan/zcad040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 07/01/2023] [Accepted: 07/14/2023] [Indexed: 07/29/2023] Open
Abstract
Most cancer types exhibit aberrant transcriptional activity, including derepression of retrotransposable elements (RTEs). However, the degree, specificity and potential consequences of RTE transcriptional activation may differ substantially among cancer types and subtypes. Representing one extreme of the spectrum, we characterize the transcriptional activity of RTEs in cohorts of esophageal adenocarcinoma (EAC) and its precursor Barrett's esophagus (BE) from the OCCAMS (Oesophageal Cancer Clinical and Molecular Stratification) consortium, and from TCGA (The Cancer Genome Atlas). We found exceptionally high RTE inclusion in the EAC transcriptome, driven primarily by transcription of genes incorporating intronic or adjacent RTEs, rather than by autonomous RTE transcription. Nevertheless, numerous chimeric transcripts straddling RTEs and genes, and transcripts from stand-alone RTEs, particularly KLF5- and SOX9-controlled HERVH proviruses, were overexpressed specifically in EAC. Notably, incomplete mRNA splicing and EAC-characteristic intronic RTE inclusion was mirrored by relative loss of the respective fully-spliced, functional mRNA isoforms, consistent with compromised cellular fitness. Defective RNA splicing was linked with strong transcriptional activation of a HERVH provirus on Chr Xp22.32 and defined EAC subtypes with distinct molecular features and prognosis. Our study defines distinguishable RTE transcriptional profiles of EAC, reflecting distinct underlying processes and prognosis, thus providing a framework for targeted studies.
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Affiliation(s)
| | - Jane Hc Loong
- Retroviral Immunology Laboratory, The Francis Crick Institute, London, UK
| | - Jan Attig
- Retroviral Immunology Laboratory, The Francis Crick Institute, London, UK
| | - George R Young
- Bioinformatics and Biostatistics Facility, The Francis Crick Institute, London, UK
| | - Piyali Ganguli
- Cancer Systems Biology Laboratory, The Francis Crick Institute, London, UK
- School of Cancer and Pharmaceutical Sciences, King's College London, London, UK
| | - Ginny Devonshire
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Nicola Grehan
- Early Cancer Institute, Hutchison Research Centre, University of Cambridge, Cambridge, UK
| | - Francesca D Ciccarelli
- Cancer Systems Biology Laboratory, The Francis Crick Institute, London, UK
- School of Cancer and Pharmaceutical Sciences, King's College London, London, UK
| | - Rebecca C Fitzgerald
- Early Cancer Institute, Hutchison Research Centre, University of Cambridge, Cambridge, UK
| | - George Kassiotis
- Retroviral Immunology Laboratory, The Francis Crick Institute, London, UK
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, UK
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17
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Maya-González C, Wessman S, Lagerstedt-Robinson K, Taylan F, Tesi B, Kuchinskaya E, McCluggage WG, Poluha A, Holm S, Nergårdh R, Díaz De Ståhl T, Höybye C, Tettamanti G, Delgado-Vega AM, Skarin Nordenvall A, Nordgren A. Register-based and genetic studies of Prader-Willi syndrome show a high frequency of gonadal tumors and a possible mechanism for tumorigenesis through imprinting relaxation. Front Med (Lausanne) 2023; 10:1172565. [PMID: 37575996 PMCID: PMC10419300 DOI: 10.3389/fmed.2023.1172565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 07/04/2023] [Indexed: 08/15/2023] Open
Abstract
Prader-Willi syndrome (PWS) is a rare disease caused by a lack of expression of inherited imprinted genes in the paternally derived Prader-Willi critical region on chromosome 15q11.2-q13. It is characterized by poor feeding and hypotonia in infancy, intellectual disability, behavioral abnormalities, dysmorphic features, short stature, obesity, and hypogonadism. PWS is not a known cancer predisposition syndrome, but previous investigations regarding the prevalence of cancer in these patients suggest an increased risk of developing specific cancer types such as myeloid leukemia and testicular cancer. We present the results from a Swedish national population-based cohort study of 360 individuals with PWS and 18,000 matched comparisons. The overall frequency of cancer was not increased in our PWS cohort, but we found a high frequency of pediatric cancers. We also performed whole-genome sequencing of blood- and tumor-derived DNAs from a unilateral dysgerminoma in a 13-year-old girl with PWS who also developed bilateral ovarian sex cord tumors with annular tubules. In germline analysis, there were no additional findings apart from the 15q11.2-q13 deletion of the paternal allele, while a pathogenic activating KIT mutation was identified in the tumor. Additionally, methylation-specific multiplex ligation-dependent probe amplification revealed reduced methylation at the PWS locus in the dysgerminoma but not in the blood. In conclusion, our register-based study suggests an increased risk of cancer at a young age, especially testicular and ovarian tumors. We found no evidence of a general increase in cancer risk in patients with PWS. However, given our limited observational time, further studies with longer follow-up times are needed to clarify the lifetime cancer risk in PWS. We have also described the second case of locus-specific loss-of-imprinting in a germ cell tumor in PWS, suggesting a possible mechanism of carcinogenesis.
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Affiliation(s)
- Carolina Maya-González
- Department of Molecular Medicine and Surgery, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Sandra Wessman
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden
| | - Kristina Lagerstedt-Robinson
- Department of Molecular Medicine and Surgery, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Fulya Taylan
- Department of Molecular Medicine and Surgery, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Bianca Tesi
- Department of Molecular Medicine and Surgery, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
- Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden
| | - Ekaterina Kuchinskaya
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
- Department of Clinical Experimental Medicine, Linköping University, Linköping, Sweden
| | - W. Glenn McCluggage
- Department of Pathology, Belfast Health and Social Care Trust, Belfast, United Kingdom
| | - Anna Poluha
- Clinical Genetics, Uppsala University Hospital, Uppsala, Sweden
- Department of Immunology, Genetics and Pathology, Faculty of Medicine, Uppsala University, Uppsala, Sweden
| | - Stefan Holm
- Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | - Ricard Nergårdh
- Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | - Teresita Díaz De Ståhl
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden
| | - Charlotte Höybye
- Department of Molecular Medicine and Surgery, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Endocrinology, Karolinska University Hospital, Stockholm, Sweden
| | - Giorgio Tettamanti
- Department of Molecular Medicine and Surgery, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Angelica Maria Delgado-Vega
- Department of Molecular Medicine and Surgery, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Anna Skarin Nordenvall
- Department of Molecular Medicine and Surgery, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Radiology, Karolinska University Hospital, Stockholm, Sweden
| | - Ann Nordgren
- Department of Molecular Medicine and Surgery, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
- Department of Laboratory Medicine, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, Gothenburg, Sweden
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García-Cárdenas JM, Armendáriz-Castillo I, García-Cárdenas N, Pesantez-Coronel D, López-Cortés A, Indacochea A, Guerrero S. Data mining identifies novel RNA-binding proteins involved in colon and rectal carcinomas. Front Cell Dev Biol 2023; 11:1088057. [PMID: 37384253 PMCID: PMC10293682 DOI: 10.3389/fcell.2023.1088057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 02/13/2023] [Indexed: 06/30/2023] Open
Abstract
Colorectal adenocarcinoma (COREAD) is the second most deadly cancer and third most frequently encountered malignancy worldwide. Despite efforts in molecular subtyping and subsequent personalized COREAD treatments, multidisciplinary evidence suggests separating COREAD into colon cancer (COAD) and rectal cancer (READ). This new perspective could improve diagnosis and treatment of both carcinomas. RNA-binding proteins (RBPs), as critical regulators of every hallmark of cancer, could fulfill the need to identify sensitive biomarkers for COAD and READ separately. To detect new RBPs involved in COAD and READ progression, here we used a multidata integration strategy to prioritize tumorigenic RBPs. We analyzed and integrated 1) RBPs genomic and transcriptomic alterations from 488 COAD and 155 READ patients, 2) ∼ 10,000 raw associations between RBPs and cancer genes, 3) ∼ 15,000 immunostainings, and 4) loss-of-function screens performed in 102 COREAD cell lines. Thus, we unraveled new putative roles of NOP56, RBM12, NAT10, FKBP1A, EMG1, and CSE1L in COAD and READ progression. Interestingly, FKBP1A and EMG1 have never been related with any of these carcinomas but presented tumorigenic features in other cancer types. Subsequent survival analyses highlighted the clinical relevance of FKBP1A, NOP56, and NAT10 mRNA expression to predict poor prognosis in COREAD and COAD patients. Further research should be performed to validate their clinical potential and to elucidate their molecular mechanisms underlying these malignancies.
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Affiliation(s)
- Jennyfer M. García-Cárdenas
- Laboratorio de Ciencia de Datos Biomédicos, Escuela de Medicina, Facultad de Ciencias Médicas de la Salud y de la Vida, Universidad Internacional del Ecuador, Quito, Ecuador
- Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Madrid, Spain
| | - Isaac Armendáriz-Castillo
- Laboratorio de Ciencia de Datos Biomédicos, Escuela de Medicina, Facultad de Ciencias Médicas de la Salud y de la Vida, Universidad Internacional del Ecuador, Quito, Ecuador
- Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Madrid, Spain
- Facultad de Ingenierías y Ciencias Aplicadas, Universidad Internacional SEK, Quito, Ecuador
| | | | - David Pesantez-Coronel
- Medical Oncology Department Hospital Clinic and Translational Genomics and Targeted Therapies in Solid Tumors, IDIBAPS, Barcelona, Spain
| | - Andrés López-Cortés
- Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Madrid, Spain
- Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador
| | - Alberto Indacochea
- Medical Oncology Department Hospital Clinic and Translational Genomics and Targeted Therapies in Solid Tumors, IDIBAPS, Barcelona, Spain
| | - Santiago Guerrero
- Laboratorio de Ciencia de Datos Biomédicos, Escuela de Medicina, Facultad de Ciencias Médicas de la Salud y de la Vida, Universidad Internacional del Ecuador, Quito, Ecuador
- Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Madrid, Spain
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19
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Misetic H, Keddar MR, Jeannon JP, Ciccarelli FD. Mechanistic insights into the interactions between cancer drivers and the tumour immune microenvironment. Genome Med 2023; 15:40. [PMID: 37277866 DOI: 10.1186/s13073-023-01197-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 05/25/2023] [Indexed: 06/07/2023] Open
Abstract
BACKGROUND The crosstalk between cancer and the tumour immune microenvironment (TIME) has attracted significant interest in the latest years because of its impact on cancer evolution and response to treatment. Despite this, cancer-specific tumour-TIME interactions and their mechanistic insights are still poorly understood. METHODS Here, we compute the significant interactions occurring between cancer-specific genetic drivers and five anti- and pro-tumour TIME features in 32 cancer types using Lasso regularised ordinal regression. Focusing on head and neck squamous cancer (HNSC), we rebuild the functional networks linking specific TIME driver alterations to the TIME state they associate with. RESULTS The 477 TIME drivers that we identify are multifunctional genes whose alterations are selected early in cancer evolution and recur across and within cancer types. Tumour suppressors and oncogenes have an opposite effect on the TIME and the overall anti-tumour TIME driver burden is predictive of response to immunotherapy. TIME driver alterations predict the immune profiles of HNSC molecular subtypes, and perturbations in keratinization, apoptosis and interferon signalling underpin specific driver-TIME interactions. CONCLUSIONS Overall, our study delivers a comprehensive resource of TIME drivers, gives mechanistic insights into their immune-regulatory role, and provides an additional framework for patient prioritisation to immunotherapy. The full list of TIME drivers and associated properties are available at http://www.network-cancer-genes.org .
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Affiliation(s)
- Hrvoje Misetic
- Cancer Systems Biology Laboratory, The Francis Crick Institute, London, NW1 1AT, UK
- School of Cancer and Pharmaceutical Sciences, King's College London, London, SE11UL, UK
| | - Mohamed Reda Keddar
- Cancer Systems Biology Laboratory, The Francis Crick Institute, London, NW1 1AT, UK
- School of Cancer and Pharmaceutical Sciences, King's College London, London, SE11UL, UK
| | - Jean-Pierre Jeannon
- Department of Head & Neck Surgery, Great Maze Pond, Guy's Hospital, London, SE1 9RT, UK
| | - Francesca D Ciccarelli
- Cancer Systems Biology Laboratory, The Francis Crick Institute, London, NW1 1AT, UK.
- School of Cancer and Pharmaceutical Sciences, King's College London, London, SE11UL, UK.
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Helfer H, Skaff Y, Happe F, Djennaoui S, Chidiac J, Poénou G, Righini M, Mahé I. Diagnostic Approach for Venous Thromboembolism in Cancer Patients. Cancers (Basel) 2023; 15:cancers15113031. [PMID: 37296993 DOI: 10.3390/cancers15113031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 05/16/2023] [Accepted: 05/27/2023] [Indexed: 06/12/2023] Open
Abstract
Venous thromboembolic disease (VTE) is a common complication in cancer patients. The currently recommended VTE diagnostic approach involves a step-by-step algorithm, which is based on the assessment of clinical probability, D-dimer measurement, and/or diagnostic imaging. While this diagnostic strategy is well validated and efficient in the noncancer population, its use in cancer patients is less satisfactory. Cancer patients often present nonspecific VTE symptoms resulting in less discriminatory power of the proposed clinical prediction rules. Furthermore, D-dimer levels are often increased because of a hypercoagulable state associated with the tumor process. Consequently, the vast majority of patients require imaging tests. In order to improve VTE exclusion in cancer patients, several approaches have been developed. The first approach consists of ordering imaging tests to all patients, despite overexposing a population known to have mostly multiple comorbidities to radiations and contrast products. The second approach consists of new diagnostic algorithms based on clinical probability assessment with different D-dimer thresholds, e.g., the YEARS algorithm, which shows promise in improving the diagnosis of PE in cancer patients. The third approach uses an adjusted D-dimer threshold, to age, pretest probability, clinical criteria, or other criteria. These different diagnostic strategies have not been compared head-to-head. In conclusion, despite having several proposed diagnostic approaches to diagnose VTE in cancer patients, we still lack a dedicated diagnostic algorithm specific for this population.
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Affiliation(s)
- Hélène Helfer
- Service de Médecine Interne, Hôpital Louis Mourier, Assistance Publique des Hôpitaux de Paris (AP-HP), 92700 Colombes, France
- Université Paris Cité, 75006 Paris, France
- INSERM UMR-S-1140, 75006 Paris, France
| | - Yara Skaff
- Service de Médecine Interne, Hôpital Louis Mourier, Assistance Publique des Hôpitaux de Paris (AP-HP), 92700 Colombes, France
| | - Florent Happe
- Service de Médecine Interne, Hôpital Louis Mourier, Assistance Publique des Hôpitaux de Paris (AP-HP), 92700 Colombes, France
| | - Sadji Djennaoui
- Service de Médecine Interne, Hôpital Louis Mourier, Assistance Publique des Hôpitaux de Paris (AP-HP), 92700 Colombes, France
| | - Jean Chidiac
- Service de Médecine Interne, Hôpital Louis Mourier, Assistance Publique des Hôpitaux de Paris (AP-HP), 92700 Colombes, France
| | - Géraldine Poénou
- Service de Médecine Interne, Hôpital Louis Mourier, Assistance Publique des Hôpitaux de Paris (AP-HP), 92700 Colombes, France
| | - Marc Righini
- FCRIN INNOVTE, 42055 Saint-Étienne CEDEX 2, France
- Service d'Angiologie et Hémostase, HUG-Hôpitaux Universitaires de Genève, 1205 Genève, Switzerland
| | - Isabelle Mahé
- Service de Médecine Interne, Hôpital Louis Mourier, Assistance Publique des Hôpitaux de Paris (AP-HP), 92700 Colombes, France
- Université Paris Cité, 75006 Paris, France
- INSERM UMR-S-1140, 75006 Paris, France
- FCRIN INNOVTE, 42055 Saint-Étienne CEDEX 2, France
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21
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Liu Y, Li G. Empowering biologists to decode omics data: the Genekitr R package and web server. BMC Bioinformatics 2023; 24:214. [PMID: 37221491 DOI: 10.1186/s12859-023-05342-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 05/16/2023] [Indexed: 05/25/2023] Open
Abstract
BACKGROUND A variety of high-throughput analyses, such as transcriptome, proteome, and metabolome analysis, have been developed, producing unprecedented amounts of omics data. These studies generate large gene lists, of which the biological significance shall be deeply understood. However, manually interpreting these lists is difficult, especially for non-bioinformatics-savvy scientists. RESULTS We developed an R package and a corresponding web server-Genekitr, to assist biologists in exploring large gene sets. Genekitr comprises four modules: gene information retrieval, ID (identifier) conversion, enrichment analysis and publication-ready plotting. Currently, the information retrieval module can retrieve information on up to 23 attributes for genes of 317 organisms. The ID conversion module assists in ID-mapping of genes, probes, proteins, and aliases. The enrichment analysis module organizes 315 gene set libraries in different biological contexts by over-representation analysis and gene set enrichment analysis. The plotting module performs customizable and high-quality illustrations that can be used directly in presentations or publications. CONCLUSIONS This web server tool will make bioinformatics more accessible to scientists who might not have programming expertise, allowing them to perform bioinformatics tasks without coding.
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Affiliation(s)
- Yunze Liu
- Ministry of Education Frontiers Science Center for Precision Oncology, Faculty of Health Sciences, University of Macau, Macau SAR, China
- Cancer Centre, Faculty of Health Sciences, University of Macau, Macau SAR, China
- Department of Biomedical Science, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Gang Li
- Ministry of Education Frontiers Science Center for Precision Oncology, Faculty of Health Sciences, University of Macau, Macau SAR, China.
- Cancer Centre, Faculty of Health Sciences, University of Macau, Macau SAR, China.
- Department of Biomedical Science, Faculty of Health Sciences, University of Macau, Macau SAR, China.
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22
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Goldman JW, Sholl LM, Dacic S, Fishbein MC, Murciano-Goroff YR, Rajaram R, Szymczak S, Szpurka AM, Chao BH, Drilon A. Case Report: Complete pathologic response to neoadjuvant selpercatinib in a patient with resectable early-stage RET fusion-positive non-small cell lung cancer. Front Oncol 2023; 13:1178313. [PMID: 37274265 PMCID: PMC10232990 DOI: 10.3389/fonc.2023.1178313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 05/03/2023] [Indexed: 06/06/2023] Open
Abstract
The LIBRETTO-001 trial demonstrated the activity of the selective rearrangement during transfection (RET) inhibitor selpercatinib in advanced RET fusion-positive non-small cell lung cancer (NSCLC) and resulted in the drug's approval for this indication. A cohort that included neoadjuvant and adjuvant selpercatinib was opened on LIBRETTO-001 for early-stage RET fusion-positive NSCLC with the primary endpoint of major pathologic response. A patient with a stage IB (cT2aN0M0) KIF5B-RET fusion-positive NSCLC received 8 weeks of neoadjuvant selpercatinib at 160 mg twice daily followed by surgery. While moderate regression in the primary tumor (stable disease, Response Evaluation Criteria in Solid Tumors (RECIST) guidelines version 1.1) was observed radiologically, assessment via an Independent Pathologic Review Committee revealed a pathologic complete response (0% viable tumor). This consensus assessment by three independent pathologists was aided by RET fluorescence in situ hybridization testing of a reactive pneumocyte proliferation showing no rearrangement. Neoadjuvant selpercatinib was well-tolerated with only low-grade treatment-emergent adverse events. The activity of prospective preoperative selpercatinib in this case establishes proof of concept of the potential utility of RET inhibitor therapy in early-stage RET fusion-positive NSCLC.
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Affiliation(s)
- Jonathan W. Goldman
- David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Lynette M. Sholl
- Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, United States
| | - Sanja Dacic
- Department of Pathology, Yale School of Medicine, New Haven, CT, United States
| | - Michael C. Fishbein
- David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | | | - Ravi Rajaram
- Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | | | - Anna M. Szpurka
- Loxo@Lilly, Eli Lilly and Company, Indianapolis, IN, United States
| | - Bo H. Chao
- Loxo@Lilly, Eli Lilly and Company, New York, NY, United States
| | - Alexander Drilon
- Department of Medicine, Memorial Sloan-Kettering Cancer CenterNew York, NY, United States
- Department of Medicine, Weill Cornell Medical College, New York, NY, United States
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Campos Segura AV, Velásquez Sotomayor MB, Gutiérrez Román AIF, Ortiz Rojas CA, Murillo Carrasco AG. Impact of mini-driver genes in the prognosis and tumor features of colorectal cancer samples: a novel perspective to support current biomarkers. PeerJ 2023; 11:e15410. [PMID: 37214090 PMCID: PMC10198153 DOI: 10.7717/peerj.15410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 04/23/2023] [Indexed: 05/24/2023] Open
Abstract
Background Colorectal cancer (CRC) is the second leading cause of cancer-related deaths, and its development is associated with the gains and/or losses of genetic material, which leads to the emergence of main driver genes with higher mutational frequency. In addition, there are other genes with mutations that have weak tumor-promoting effects, known as mini-drivers, which could aggravate the development of oncogenesis when they occur together. The aim of our work was to use computer analysis to explore the survival impact, frequency, and incidence of mutations of possible mini-driver genes to be used for the prognosis of CRC. Methods We retrieved data from three sources of CRC samples using the cBioPortal platform and analyzed the mutational frequency to exclude genes with driver features and those mutated in less than 5% of the original cohort. We also observed that the mutational profile of these mini-driver candidates is associated with variations in the expression levels. The candidate genes obtained were subjected to Kaplan-Meier curve analysis, making a comparison between mutated and wild-type samples for each gene using a p-value threshold of 0.01. Results After gene filtering by mutational frequency, we obtained 159 genes of which 60 were associated with a high accumulation of total somatic mutations with Log2 (fold change) > 2 and p values < 10-5. In addition, these genes were enriched to oncogenic pathways such as epithelium-mesenchymal transition, hsa-miR-218-5p downregulation, and extracellular matrix organization. Our analysis identified five genes with possible implications as mini-drivers: DOCK3, FN1, PAPPA2, DNAH11, and FBN2. Furthermore, we evaluated a combined classification where CRC patients with at least one mutation in any of these genes were separated from the main cohort obtaining a p-value < 0.001 in the evaluation of CRC prognosis. Conclusion Our study suggests that the identification and incorporation of mini-driver genes in addition to known driver genes could enhance the accuracy of prognostic biomarkers for CRC.
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Affiliation(s)
- Anthony Vladimir Campos Segura
- Biochemistry and Molecular Biology Research Laboratory. Faculty of Natural Sciences and Mathematics, Universidad Nacional Federico Villarreal, Lima, Peru
- Research Group in Biochemistry and Synthetic Biology (GIBBS-UNFV), Lima, Peru
- Research Group in Immunology and Cancer (IMMUCA), Lima, Peru
| | - Mariana Belén Velásquez Sotomayor
- Research Group in Immunology and Cancer (IMMUCA), Lima, Peru
- School of Human Medicine, Faculty of Health Sciences, Universidad Cientifica del Sur, Lima, Peru
| | - Ana Isabel Flor Gutiérrez Román
- Biochemistry and Molecular Biology Research Laboratory. Faculty of Natural Sciences and Mathematics, Universidad Nacional Federico Villarreal, Lima, Peru
- Research Group in Biochemistry and Synthetic Biology (GIBBS-UNFV), Lima, Peru
| | - César Alexander Ortiz Rojas
- Research Group in Immunology and Cancer (IMMUCA), Lima, Peru
- Hematology Division, LIM31, Medical School, Universidade de São Paulo, Sao Paulo, Brazil
| | - Alexis Germán Murillo Carrasco
- Research Group in Immunology and Cancer (IMMUCA), Lima, Peru
- Centro de Investigação Translacional em Oncologia (LIM24), Departamento de Radiologia e Oncologia, Faculdade de Medicina, Universidade de São Paulo, Sao Paulo, Brazil
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Alotaibi A, Gadekar VP, Gundla PS, Mandarthi S, Ravi S, Mallya D, Tungekar A, Lavanya BV, Bhagavath AK, Cordero MW, Pitkaniemi J, Seetharam RN, Bepari A, Hebbar P. A comprehensive analysis of mRNA expression profiles of Esophageal Squamous Cell Carcinoma reveals downregulation of Desmoglein 1 and crucial genomic targets. Cancer Biomark 2023; 38:465-487. [PMID: 38073377 DOI: 10.3233/cbm-230145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2023]
Abstract
AIM Esophageal Squamous Cell Carcinoma (ESCC) is a histological subtype of esophageal cancer that begins in the squamous cells in the esophagus. In only 19% of the ESCC-diagnosed patients, a five-year survival rate has been seen. This necessitates the identification of high-confidence biomarkers for early diagnosis, prognosis, and potential therapeutic targets for the mitigation of ESCC. METHOD We performed a meta-analysis of 10 mRNA datasets and identified consistently perturbed genes across the studies. Then, integrated with ESCC ATLAS to segregate 'core' genes to identify consequences of primary gene perturbation events leading to gene-gene interactions and dysregulated molecular signaling pathways. Further, by integrating with toxicogenomics data, inferences were drawn for gene interaction with environmental exposures, trace elements, chemical carcinogens, and drug chemicals. We also deduce the clinical outcomes of candidate genes based on survival analysis using the ESCC related dataset in The Cancer Genome Atlas. RESULT We identified 237 known and 18 novel perturbed candidate genes. Desmoglein 1 (DSG1) is one such gene that we found significantly downregulated (Fold Change =-1.89, p-value = 8.2e-06) in ESCC across six different datasets. Further, we identified 31 'core' genes (that either harbor genetic variants or are regulated by epigenetic modifications) and found regulating key biological pathways via adjoining genes in gene-gene interaction networks. Functional enrichment analysis showed dysregulated biological processes and pathways including "Extracellular matrix", "Collagen trimmer" and "HPV infection" are significantly overrepresented in our candidate genes. Based on the toxicogenomic inferences from Comparative Toxicogenomics Database we report the key genes that interacted with risk factors such as tobacco smoking, zinc, nitroso benzylmethylamine, and drug chemicals such as cisplatin, Fluorouracil, and Mitomycin in relation to ESCC. We also point to the STC2 gene that shows a high risk for mortality in ESCC patients. CONCLUSION We identified novel perturbed genes in relation to ESCC and explored their interaction network. DSG1 is one such gene, its association with microbiota and a clinical presentation seen commonly with ESCC hints that it is a good candidate for early diagnostic marker. Besides, in this study we highlight candidate genes and their molecular connections to risk factors, biological pathways, drug chemicals, and the survival probability of ESCC patients.
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Affiliation(s)
- Amal Alotaibi
- Basic Science Department, College of Medicine, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
- Basic Science Department, College of Medicine, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Veerendra P Gadekar
- Mbiomics LLC, Lewes DE, USA
- Basic Science Department, College of Medicine, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | | | | | | | | | | | | | - Ashok Kumar Bhagavath
- Department of Cellular and Molecular Biology, University of Texas Health Science Center, Tyler, Texas, TX, USA
| | - MaryAnne Wong Cordero
- Basic Science Department, College of Medicine, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Janne Pitkaniemi
- Finnish Cancer Registry, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Raviraja N Seetharam
- Manipal Center for Biotherapeutics Research, Manipal Academy of Higher Education, Manipal, India
| | - Asmatanzeem Bepari
- Basic Science Department, College of Medicine, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Prashantha Hebbar
- Mbiomics LLC, Lewes DE, USA
- Manipal Center for Biotherapeutics Research, Manipal Academy of Higher Education, Manipal, India
- Meta Biosciences Pvt Ltd., Manipal - GOK Bioincubator, Advanced Research Center, Manipal, India
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25
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Wang X, Huang H, Liu X, Li J, Wang L, Li L, Li Y, Han T. Immunogenic cell death-related classifications in breast cancer identify precise immunotherapy biomarkers and enable prognostic stratification. Front Genet 2022; 13:1052720. [PMID: 36437951 PMCID: PMC9685311 DOI: 10.3389/fgene.2022.1052720] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 10/27/2022] [Indexed: 12/01/2023] Open
Abstract
Background: Immunogenic cell death (ICD) remodels the tumor immune microenvironment, plays an inherent role in tumor cell apoptosis, and promotes durable protective antitumor immunity. Currently, appropriate biomarker-based ICD immunotherapy for breast cancer (BC) is under active exploration. Methods: To determine the potential link between ICD genes and the clinical risk of BC, TCGA-BC was used as the training set and GSE58812 was used as the validation set. Gene expression, consistent clustering, enrichment analysis, and mutation omics analyses were performed to analyze the potential biological pathways of ICD genes involved in BC. Furthermore, a risk and prognosis model of ICD was constructed to evaluate the correlation between risk grade and immune infiltration, clinical stage, and survival prognosis. Results: We identified two ICD-related subtypes by consistent clustering and found that the C2 subtype was associated with good survival prognosis, abundant immune cell infiltration, and high activity of immune biological processes. Based on this, we constructed and validated an ICD risk and prognosis model of BC, including ATG5, HSP90AA1, PIK3CA, EIF2AK3, MYD88, IL1R1, and CD8A. This model can effectively predict the survival rate of patients with BC and is negatively correlated with the immune microenvironment and clinical stage. Conclusion: This study provides new insights into the role of ICD in BC. The novel classification risk model based on ICD in BC established in this study can aid in estimating the potential prognosis of patients with BC and the clinical outcomes of immunotherapy and postulates targets that are more useful in comprehensive treatment strategies.
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Affiliation(s)
- Xue Wang
- Pharmacology of Traditional Chinese Medical Formulae, College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Hailiang Huang
- College of Rehabilitation Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Xijian Liu
- Pharmacology of Traditional Chinese Medical Formulae, College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Jiuwei Li
- College of Medical, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Lu Wang
- Office of Academic Research, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Ling Li
- Pharmacology of Traditional Chinese Medical Formulae, College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yaxing Li
- Pharmacology of Traditional Chinese Medical Formulae, College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Tao Han
- Pharmacology of Traditional Chinese Medical Formulae, College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
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26
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Ramezankhani R, Ghavidel AA, Rashidi S, Rojhannezhad M, Abolkheir HR, Mirhosseini M, Taleahmad S, Vosough M. Gender-related differentially expressed genes in pancreatic cancer: possible culprits or accomplices? Front Genet 2022; 13:966941. [DOI: 10.3389/fgene.2022.966941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 10/10/2022] [Indexed: 11/13/2022] Open
Abstract
Pancreatic cancer (PC) is one of the leading causes of cancer mortality worldwide, and its incidence and mortality rate in several regions is higher in male patients. Although numerous efforts have been made to enhance the clinical outcomes of existing therapeutic regimens, their efficiency is still low, and drug resistance usually occurs in many patients. In addition, the exact underlying molecular basis that makes PC slightly more prevalent among males remains unknown. Providing information regarding the possible association between gender and PC tumorigenesis may offer important clues for how certain molecular cross-talks can affect PC initiation and/or progression. In this study, we used several microarray expression data to identify the common up- and downregulated genes within one specific gender, which were also specified to have binding sites for androgen and/or estrogen receptors. Using functional enrichment analysis among the others, for all the gene sets found in this study, we have shed light on the plausible importance of the androgenic effectors in tumorigenesis, such as the androgen-regulated expression of the GLI transcription factor and the potential role of testosterone in the extracellular matrix (ECM)–cell interaction, which are known for their importance in tumorigenesis. Moreover, we demonstrated that the biological process axon guidance was highlighted regarding the upregulated genes in male patients. Overall, identification of gene candidates as the possible link between gender and PC progression or survival rates may help in developing strategies to reduce the incidence of this cancer.
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27
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Wang D, Wu X, Jiang G, Yang J, Yu Z, Yang Y, Yang W, Niu X, Tang K, Gong J. Systematic analysis of the effects of genetic variants on chromatin accessibility to decipher functional variants in non-coding regions. Front Oncol 2022; 12:1035855. [PMID: 36330496 PMCID: PMC9623183 DOI: 10.3389/fonc.2022.1035855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 10/03/2022] [Indexed: 11/18/2022] Open
Abstract
Genome-wide association study (GWAS) has identified thousands of single nucleotide polymorphisms (SNPs) associated with complex diseases and traits. However, deciphering the functions of these SNPs still faces challenges. Recent studies have shown that SNPs could alter chromatin accessibility and result in differences in tumor susceptibility between individuals. Therefore, systematically analyzing the effects of SNPs on chromatin accessibility could help decipher the functions of SNPs, especially those in non-coding regions. Using data from The Cancer Genome Atlas (TCGA), chromatin accessibility quantitative trait locus (caQTL) analysis was conducted to estimate the associations between genetic variants and chromatin accessibility. We analyzed caQTLs in 23 human cancer types and identified 9,478 caQTLs in breast carcinoma (BRCA). In BRCA, these caQTLs tend to alter the binding affinity of transcription factors, and open chromatin regions regulated by these caQTLs are enriched in regulatory elements. By integrating with eQTL data, we identified 141 caQTLs showing a strong signal for colocalization with eQTLs. We also identified 173 caQTLs in genome-wide association studies (GWAS) loci and inferred several possible target genes of these caQTLs. By performing survival analysis, we found that ~10% caQTLs potentially influence the prognosis of patients. To facilitate access to relevant data, we developed a user-friendly data portal, BCaQTL (http://gong_lab.hzau.edu.cn/caqtl_database), for data searching and downloading. Our work may facilitate fine-map regulatory mechanisms underlying risk loci of cancer and discover the biomarkers or therapeutic targets for cancer prognosis. The BCaQTL database will be an important resource for genetic and epigenetic studies.
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Affiliation(s)
- Dongyang Wang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Xiaohong Wu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Guanghui Jiang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Jianye Yang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Zhanhui Yu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Yanbo Yang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Wenqian Yang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Xiaohui Niu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Ke Tang
- Department of Biochemistry and Molecular Biology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Jing Gong, ; Ke Tang,
| | - Jing Gong
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
- College of Biomedicine and Health, Huazhong Agricultural University, Wuhan, China
- *Correspondence: Jing Gong, ; Ke Tang,
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Somatic variation in normal tissues: friend or foe of cancer early detection? Ann Oncol 2022; 33:1239-1249. [PMID: 36162751 DOI: 10.1016/j.annonc.2022.09.156] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 09/03/2022] [Accepted: 09/10/2022] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Seemingly normal tissues progressively become populated by mutant clones over time. Most of these clones bear mutations in well-known cancer genes but only rarely do they transform into cancer. This poses questions on what triggers cancer initiation and what implications somatic variation has for cancer early detection. DESIGN We analysed recent mutational screens of healthy and cancer-free diseased tissues to compare somatic drivers and the causes of somatic variation across tissues. We then reviewed the mechanisms of clonal expansion and their relationships with age and diseases other than cancer. We finally discussed the relevance of somatic variation for cancer initiation and how it can help or hinder cancer detection and prevention. RESULTS The extent of somatic variation is highly variable across tissues and depends on intrinsic features, such as tissue architecture and turnover, as well as the exposure to endogenous and exogenous insults. Most somatic mutations driving clonal expansion are tissue-specific and inactivate tumor suppressor genes involved in chromatin modification and cell growth signaling. Some of these genes are more frequently mutated in normal tissues than cancer, indicating a context-dependent cancer promoting or protective role. Mutant clones can persist over a long time or disappear rapidly, suggesting that their fitness depends on the dynamic equilibrium with the environment. The disruption of this equilibrium is likely responsible for their transformation into malignant clones and knowing what triggers this process is key for cancer prevention and early detection. Somatic variation should be considered in liquid biopsy, where it may contribute cancer-independent mutations, and in the identification of cancer drivers, since not all mutated genes favoring clonal expansion also drive tumorigenesis. CONCLUSIONS Somatic variation and the factors governing homeostasis of normal tissues should be taken into account when devising strategies for cancer prevention and early detection.
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29
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Chen Z, Liang B, Wu Y, Zhou H, Wang Y, Wu H. Identifying driver modules based on multi-omics biological networks in prostate cancer. IET Syst Biol 2022; 16:187-200. [PMID: 36039671 PMCID: PMC9675413 DOI: 10.1049/syb2.12050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 07/31/2022] [Accepted: 08/13/2022] [Indexed: 01/11/2023] Open
Abstract
The development of sequencing technology has promoted the expansion of cancer genome data. It is necessary to identify the pathogenesis of cancer at the molecular level and explore reliable treatment methods and precise drug targets in cancer by identifying carcinogenic functional modules in massive multi-omics data. However, there are still limitations to identifying carcinogenic driver modules by utilising genetic characteristics simply. Therefore, this study proposes a computational method, NetAP, to identify driver modules in prostate cancer. Firstly, high mutual exclusivity, high coverage, and high topological similarity between genes are integrated to construct a weight function, which calculates the weight of gene pairs in a biological network. Secondly, the random walk method is utilised to reevaluate the strength of interaction among genes. Finally, the optimal driver modules are identified by utilising the affinity propagation algorithm. According to the results, the authors' method identifies more validated driver genes and driver modules compared with the other previous methods. Thus, the proposed NetAP method can identify carcinogenic driver modules effectively and reliably, and the experimental results provide a powerful basis for cancer diagnosis, treatment and drug targets.
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Affiliation(s)
- Zhongli Chen
- Tibet Center for Disease Control and PreventionLhasaChina,School of SoftwareShandong UniversityJinanChina,School of Information EngineeringNorthwest A&F UniversityYanglingChina
| | - Biting Liang
- School of Information EngineeringNorthwest A&F UniversityYanglingChina
| | - Yingfu Wu
- School of Information EngineeringNorthwest A&F UniversityYanglingChina
| | - Haoru Zhou
- School of Information EngineeringNorthwest A&F UniversityYanglingChina
| | - Yuchen Wang
- School of SoftwareShandong UniversityJinanChina
| | - Hao Wu
- School of SoftwareShandong UniversityJinanChina
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30
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Solary E, Abou-Zeid N, Calvo F. Ageing and cancer: a research gap to fill. Mol Oncol 2022; 16:3220-3237. [PMID: 35503718 PMCID: PMC9490141 DOI: 10.1002/1878-0261.13222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 04/01/2022] [Accepted: 05/02/2022] [Indexed: 12/03/2022] Open
Abstract
The complex mechanisms of ageing biology are increasingly understood. Interventions to reduce or delay ageing‐associated diseases are emerging. Cancer is one of the diseases promoted by tissue ageing. A clockwise mutational signature is identified in many tumours. Ageing might be a modifiable cancer risk factor. To reduce the incidence of ageing‐related cancer and to detect the disease at earlier stages, we need to understand better the links between ageing and tumours. When a cancer is established, geriatric assessment and measures of biological age might help to generate evidence‐based therapeutic recommendations. In this approach, patients and caregivers would include the respective weight to give to the quality of life and survival in the therapeutic choices. The increasing burden of cancer in older patients requires new generations of researchers and geriatric oncologists to be trained, to properly address disease complexity in a multidisciplinary manner, and to reduce health inequities in this population of patients. In this review, we propose a series of research challenges to tackle in the next few years to better prevent, detect and treat cancer in older patients while preserving their quality of life.
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Affiliation(s)
- Eric Solary
- Fondation « Association pour la Recherche sur le Cancer », Villejuif, France.,Université Paris Saclay, Faculté de Médecine, Le Kremlin-Bicêtre, France.,Gustave Roussy Cancer Center, INSERM U1287, Villejuif, France
| | - Nancy Abou-Zeid
- Fondation « Association pour la Recherche sur le Cancer », Villejuif, France
| | - Fabien Calvo
- Fondation « Association pour la Recherche sur le Cancer », Villejuif, France.,Université de Paris, Paris, France
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Pun FW, Leung GHD, Leung HW, Liu BHM, Long X, Ozerov IV, Wang J, Ren F, Aliper A, Izumchenko E, Moskalev A, de Magalhães JP, Zhavoronkov A. Hallmarks of aging-based dual-purpose disease and age-associated targets predicted using PandaOmics AI-powered discovery engine. Aging (Albany NY) 2022; 14:2475-2506. [PMID: 35347083 PMCID: PMC9004567 DOI: 10.18632/aging.203960] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 03/06/2022] [Indexed: 11/25/2022]
Abstract
Aging biology is a promising and burgeoning research area that can yield dual-purpose pathways and protein targets that may impact multiple diseases, while retarding or possibly even reversing age-associated processes. One widely used approach to classify a multiplicity of mechanisms driving the aging process is the hallmarks of aging. In addition to the classic nine hallmarks of aging, processes such as extracellular matrix stiffness, chronic inflammation and activation of retrotransposons are also often considered, given their strong association with aging. In this study, we used a variety of target identification and prioritization techniques offered by the AI-powered PandaOmics platform, to propose a list of promising novel aging-associated targets that may be used for drug discovery. We also propose a list of more classical targets that may be used for drug repurposing within each hallmark of aging. Most of the top targets generated by this comprehensive analysis play a role in inflammation and extracellular matrix stiffness, highlighting the relevance of these processes as therapeutic targets in aging and age-related diseases. Overall, our study reveals both high confidence and novel targets associated with multiple hallmarks of aging and demonstrates application of the PandaOmics platform to target discovery across multiple disease areas.
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Affiliation(s)
- Frank W. Pun
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Geoffrey Ho Duen Leung
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Hoi Wing Leung
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Bonnie Hei Man Liu
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Xi Long
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Ivan V. Ozerov
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Ju Wang
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Feng Ren
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Alexander Aliper
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Evgeny Izumchenko
- Department of Medicine, Section of Hematology and Oncology, University of Chicago, Chicago, IL 60637, USA
| | - Alexey Moskalev
- School of Systems Biology, George Mason University (GMU), Fairfax, VA 22030, USA
| | - João Pedro de Magalhães
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool L7 8TX, UK
| | - Alex Zhavoronkov
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
- Buck Institute for Research on Aging, Novato, CA 94945, USA
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