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Neagu AN, Whitham D, Bruno P, Morrissiey H, Darie CA, Darie CC. Omics-Based Investigations of Breast Cancer. Molecules 2023; 28:4768. [PMID: 37375323 DOI: 10.3390/molecules28124768] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 06/08/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
Breast cancer (BC) is characterized by an extensive genotypic and phenotypic heterogeneity. In-depth investigations into the molecular bases of BC phenotypes, carcinogenesis, progression, and metastasis are necessary for accurate diagnoses, prognoses, and therapy assessments in predictive, precision, and personalized oncology. This review discusses both classic as well as several novel omics fields that are involved or should be used in modern BC investigations, which may be integrated as a holistic term, onco-breastomics. Rapid and recent advances in molecular profiling strategies and analytical techniques based on high-throughput sequencing and mass spectrometry (MS) development have generated large-scale multi-omics datasets, mainly emerging from the three "big omics", based on the central dogma of molecular biology: genomics, transcriptomics, and proteomics. Metabolomics-based approaches also reflect the dynamic response of BC cells to genetic modifications. Interactomics promotes a holistic view in BC research by constructing and characterizing protein-protein interaction (PPI) networks that provide a novel hypothesis for the pathophysiological processes involved in BC progression and subtyping. The emergence of new omics- and epiomics-based multidimensional approaches provide opportunities to gain insights into BC heterogeneity and its underlying mechanisms. The three main epiomics fields (epigenomics, epitranscriptomics, and epiproteomics) are focused on the epigenetic DNA changes, RNAs modifications, and posttranslational modifications (PTMs) affecting protein functions for an in-depth understanding of cancer cell proliferation, migration, and invasion. Novel omics fields, such as epichaperomics or epimetabolomics, could investigate the modifications in the interactome induced by stressors and provide PPI changes, as well as in metabolites, as drivers of BC-causing phenotypes. Over the last years, several proteomics-derived omics, such as matrisomics, exosomics, secretomics, kinomics, phosphoproteomics, or immunomics, provided valuable data for a deep understanding of dysregulated pathways in BC cells and their tumor microenvironment (TME) or tumor immune microenvironment (TIMW). Most of these omics datasets are still assessed individually using distinct approches and do not generate the desired and expected global-integrative knowledge with applications in clinical diagnostics. However, several hyphenated omics approaches, such as proteo-genomics, proteo-transcriptomics, and phosphoproteomics-exosomics are useful for the identification of putative BC biomarkers and therapeutic targets. To develop non-invasive diagnostic tests and to discover new biomarkers for BC, classic and novel omics-based strategies allow for significant advances in blood/plasma-based omics. Salivaomics, urinomics, and milkomics appear as integrative omics that may develop a high potential for early and non-invasive diagnoses in BC. Thus, the analysis of the tumor circulome is considered a novel frontier in liquid biopsy. Omics-based investigations have applications in BC modeling, as well as accurate BC classification and subtype characterization. The future in omics-based investigations of BC may be also focused on multi-omics single-cell analyses.
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Affiliation(s)
- Anca-Narcisa Neagu
- Laboratory of Animal Histology, Faculty of Biology, "Alexandru Ioan Cuza" University of Iasi, Carol I Bvd, No. 20A, 700505 Iasi, Romania
| | - Danielle Whitham
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699, USA
| | - Pathea Bruno
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699, USA
| | - Hailey Morrissiey
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699, USA
| | - Celeste A Darie
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699, USA
| | - Costel C Darie
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699, USA
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Wang H, Arulraj T, Kimko H, Popel AS. Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition. NPJ Precis Oncol 2023; 7:55. [PMID: 37291190 DOI: 10.1038/s41698-023-00405-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 05/25/2023] [Indexed: 06/10/2023] Open
Abstract
Generating realistic virtual patients from a limited amount of patient data is one of the major challenges for quantitative systems pharmacology modeling in immuno-oncology. Quantitative systems pharmacology (QSP) is a mathematical modeling methodology that integrates mechanistic knowledge of biological systems to investigate dynamics in a whole system during disease progression and drug treatment. In the present analysis, we parameterized our previously published QSP model of the cancer-immunity cycle to non-small cell lung cancer (NSCLC) and generated a virtual patient cohort to predict clinical response to PD-L1 inhibition in NSCLC. The virtual patient generation was guided by immunogenomic data from iAtlas portal and population pharmacokinetic data of durvalumab, a PD-L1 inhibitor. With virtual patients generated following the immunogenomic data distribution, our model predicted a response rate of 18.6% (95% bootstrap confidence interval: 13.3-24.2%) and identified CD8/Treg ratio as a potential predictive biomarker in addition to PD-L1 expression and tumor mutational burden. We demonstrated that omics data served as a reliable resource for virtual patient generation techniques in immuno-oncology using QSP models.
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Affiliation(s)
- Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
| | - Theinmozhi Arulraj
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Holly Kimko
- Clinical Pharmacology & Quantitative Pharmacology, AstraZeneca, Gaithersburg, MD, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- Department of Oncology, and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
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Wang H, Arulraj T, Kimko H, Popel AS. Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.25.538191. [PMID: 37162938 PMCID: PMC10168221 DOI: 10.1101/2023.04.25.538191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Generating realistic virtual patients from a limited amount of patient data is one of the major challenges for quantitative systems pharmacology modeling in immuno-oncology. Quantitative systems pharmacology (QSP) is a mathematical modeling methodology that integrates mechanistic knowledge of biological systems to investigate dynamics in a whole system during disease progression and drug treatment. In the present analysis, we parameterized our previously published QSP model of the cancer-immunity cycle to non-small cell lung cancer (NSCLC) and generated a virtual patient cohort to predict clinical response to PD-L1 inhibition in NSCLC. The virtual patient generation was guided by immunogenomic data from iAtlas portal and population pharmacokinetic data of durvalumab, a PD-L1 inhibitor. With virtual patients generated following the immunogenomic data distribution, our model predicted a response rate of 18.6% (95% bootstrap confidence interval: 13.3-24.2%) and identified CD8/Treg ratio as a potential predictive biomarker in addition to PD-L1 expression and tumor mutational burden. We demonstrated that omics data served as a reliable resource for virtual patient generation techniques in immuno-oncology using QSP models.
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Wang L, Liu W, Liu K, Wang L, Yin X, Bo L, Xu H, Lin S, Feng K, Zhou X, Lin L, Fei M, Zhang C, Ning S, Zhao H. The dynamic dysregulated network identifies stage-specific markers during lung adenocarcinoma malignant progression and metastasis. MOLECULAR THERAPY. NUCLEIC ACIDS 2022; 30:633-647. [PMID: 36514354 PMCID: PMC9722404 DOI: 10.1016/j.omtn.2022.11.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 11/17/2022] [Indexed: 11/23/2022]
Abstract
Brain metastasis occurs in approximately 30% of patients with lung adenocarcinoma (LUAD) and is closely associated with poor prognosis, recurrence, and death. However, dynamic gene regulation and molecular mechanism driving LUAD progression remain poorly understood. In this study, we performed a comprehensive single-cell transcriptome analysis using data from normal, early stage, advanced stage, and brain metastasis LUAD. Our single-cell-level analysis reveals the cellular composition heterogeneity at different stages during LUAD progression. We identified stage-specific risk genes that could contribute to LUAD progression and metastasis by reprogramming immune-related and metabolic-related functions. We constructed an early advanced metastatic dysregulated network and revealed the dynamic changes in gene regulations during LUAD progression. We identified 6 early advanced (HLA-DRB1, HLA-DQB1, SFTPB, SFTPC, PLA2G1B, and FOLR1), 8 advanced metastasis (RPS15, RPS11, RPL13A, RPS24, HLA-DRB5, LYPLA1, KCNJ15, and PSMA3), and 2 common risk genes in different stages (SFTPD and HLA-DRA) as prognostic markers in LUAD. Particularly, decreased expression of HLA-DRA, HLA-DRB1, HLA-DQB1, and HLA-DRB5 refer poor prognosis in LUAD by controlling antigen processing and presentation and T cell activation. Increased expression of PSMA3 and LYPLA1 refer poor prognosis by reprogramming fatty acid metabolism and RNA catabolic process. Our findings will help further understanding the pathobiology of brain metastases in LUAD.
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Affiliation(s)
- Li Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China,Corresponding author Li Wang, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
| | - Wangyang Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Kailai Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Lixia Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Xiangzhe Yin
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Lin Bo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Haotian Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Shihua Lin
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Ke Feng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Xinyu Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Lin Lin
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Meiting Fei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Caiyu Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Shangwei Ning
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China,Corresponding author Shangwei Ning, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
| | - Hongying Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China,Corresponding author Hongying Zhao, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
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Zhang Z, Hu H, Li Q, Yi F, Liu Y. A Novel Circular RNA circPTCD3 Promotes Breast Cancer Progression Through Sponging miR-198. Cancer Manag Res 2021; 13:8435-8443. [PMID: 34785951 PMCID: PMC8590943 DOI: 10.2147/cmar.s256091] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 08/25/2020] [Indexed: 12/23/2022] Open
Abstract
Introduction Circular RNAs (circRNAs), a new type of non-coding RNA, have been demonstrated to play critical roles in the progression of various of malignant cancers. However, the function of circRNAs in breast cancer is not clearly understood. Methods qPCR was used to evaluate the gene expression. Function studies including MTT, transwell, wound healing and colony formation assay were performed to evaluate the function of circPTCD3 in breast cancer. Luciferase and RNA pull-down assays were used to verify the interaction between circPTCD3 and miR-198. The xenograft model was established to evaluate the function of circPTCD3 in vivo. Results In the present study, we identified a novel circRNA termed as circPTCD3 which was indicated to be significantly up-regulated in breast cancer tissues and cell lines. The results revealed that ectopic expression of circPTCD3 promoted the cell proliferation, migration and colony formation ability of breast cancer cells. Constantly, silencing of circPTCD3 inhibited those of breast cancer cells. Furthermore, we identified that circPTCD3 was able to target miR-198 in breast cancer cell. miR-198 has the function of inhibiting proliferation and migration of breast cancer cells which can be reversed by circPTCD3. Conclusion Taken together, our findings for the first time identified a novel circRNA (circPTCD3) and revealed its oncogenic role in breast cancer. Mechanically, we reported that circPTCD3 served as a competing endogenous RNA (ceRNA) to sponge miR-198. These findings provide insights into breast cancer progression and also potential new targets for diagnosis or treatment of breast cancer.
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Affiliation(s)
- Zhaohui Zhang
- Department of Tumor Chemotherapy and Radiation Sickness, Peking University Third Hospital, Beijing, 100191, People's Republic of China
| | - Hao Hu
- Beijing 100biotech Co., Ltd., Beijing, 100006, People's Republic of China
| | - Qian Li
- Department of Tumor Chemotherapy and Radiation Sickness, Peking University Third Hospital, Beijing, 100191, People's Republic of China
| | - Fumei Yi
- Department of Tumor Chemotherapy and Radiation Sickness, Peking University Third Hospital, Beijing, 100191, People's Republic of China
| | - Yan'e Liu
- Department of Tumor Chemotherapy and Radiation Sickness, Peking University Third Hospital, Beijing, 100191, People's Republic of China
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Li J, Huang G, Ren C, Wang N, Sui S, Zhao Z, Li M. Identification of differentially expressed genes-related prognostic risk model for survival prediction in breast carcinoma patients. Aging (Albany NY) 2021; 13:16577-16599. [PMID: 34175839 PMCID: PMC8266316 DOI: 10.18632/aging.203178] [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: 11/19/2020] [Accepted: 05/31/2021] [Indexed: 11/25/2022]
Abstract
Since the imbalance of gene expression has been demonstrated to tightly related to breast cancer (BRCA) genesis and growth, common genes expressed of BRCA were screened to explore the essence in-between. In current work, most common differentially expressed genes (DEGs) in various subtypes of BRCA were identified. Functional enrichment analysis illustrated the driving factor of deactivation of the cell cycle and the oocyte meiosis, which critically triggers the development of BRCA. Herein, we constructed a 12-gene prognostic risk model relative to differential gene expression. Subsequently, the K-M curves, analysis on time-ROC curve and Cox regression were performed to assess this risk model by determining the respective prognostic value, and the prediction performance were ascertained for both training and validation cohorts. In addition, multivariate Cox regression was analysed to reveal the independence between risk score and prognostic stage, and the accuracy and sensitivity of prognosis are particularly improved after clinical indicators are included into the analysis. In summary, this study offers novel insights into the imbalance of gene expression within BRCA, and highlights 12 selected genes associated with patient prognosis. The risk model can help individualize treatment for patients at different risks, and propose precise strategies and treatments for BRCA therapy.
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Affiliation(s)
- Jinyu Li
- Department of Breast Oncology, The Second Hospital of Dalian Medical University, Dalian, Liaoning 116023, China
| | - Gena Huang
- Department of Breast Oncology, The Second Hospital of Dalian Medical University, Dalian, Liaoning 116023, China
| | - Caixia Ren
- Department of Respiratory Medicine, The Second Hospital of Dalian Medical University, Dalian, Liaoning 116023, China
| | - Ning Wang
- Institute for Genome Engineered Animal Models of Human Diseases, Dalian Medical University, Dalian, Liaoning 116044, China
| | - Silei Sui
- Institute of Cancer Stem Cell, Dalian Medical University, Dalian, Liaoning 116044, China
| | - Zuowei Zhao
- Department of Breast Oncology, The Second Hospital of Dalian Medical University, Dalian, Liaoning 116023, China.,Department of Breast Surgery, The Second Hospital of Dalian Medical University, Dalian, Liaoning 116023, China
| | - Man Li
- Department of Breast Oncology, The Second Hospital of Dalian Medical University, Dalian, Liaoning 116023, China
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Cook DJ, Kallus J, Jörnsten R, Nielsen J. Molecular natural history of breast cancer: Leveraging transcriptomics to predict breast cancer progression and aggressiveness. Cancer Med 2020; 9:3551-3562. [PMID: 32207233 PMCID: PMC7221450 DOI: 10.1002/cam4.2996] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 01/29/2020] [Accepted: 03/01/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Characterizing breast cancer progression and aggressiveness relies on categorical descriptions of tumor stage and grade. Interpreting these categorical descriptions is challenging because stage convolutes the size and spread of the tumor and no consensus exists to define high/low grade tumors. METHODS We address this challenge of heterogeneity in patient-specific cancer samples by adapting and applying several tools originally created for understanding heterogeneity and phenotype development in single cells (specifically, single-cell topological data analysis and Wanderlust) to create a continuous metric describing breast cancer progression using bulk RNA-seq samples from individual patient tumors. We also created a linear regression-based method to predict tumor aggressiveness in vivo from bulk RNA-seq data. RESULTS We found that breast cancer proceeds along three convergent phenotype trajectories: luminal, HER2-enriched, and basal-like. Furthermore, 31 296 genes (for luminal cancers), 17 827 genes (for HER2-enriched), and 18 505 genes (for basal-like) are dynamically differentially expressed during breast cancer progression. Across progression trajectories, our results show that expression of genes related to ADP-ribosylation decreased as tumors progressed (while PARP1 and PARP2 increased or remained stable), suggesting the potential for a differential response to PARP inhibitors based on cancer progression. Additionally, we developed a 132-gene expression regression equation to predict mitotic index and a 23-gene expression regression equation to predict growth rate from a single breast cancer biopsy. CONCLUSION Our results suggest that breast cancer dynamically changes during disease progression, and growth rate of the cancer cells is associated with distinct transcriptional profiles.
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Affiliation(s)
- Daniel J. Cook
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
- Wallenberg Center for Protein ResearchChalmers University of TechnologyGothenburgSweden
| | - Jonatan Kallus
- Department of Mathematical SciencesChalmers University of Technology and University of GothenburgGothenburgSweden
| | - Rebecka Jörnsten
- Department of Mathematical SciencesChalmers University of Technology and University of GothenburgGothenburgSweden
| | - Jens Nielsen
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
- Wallenberg Center for Protein ResearchChalmers University of TechnologyGothenburgSweden
- Novo Nordisk Foundation Center for BiosustainabilityTechnical University of DenmarkLyngbyDenmark
- BioInnovation InstituteCopenhagen NDenmark
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