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Zhang Y, Chen F, Balic M, Creighton CJ. An essential gene signature of breast cancer metastasis reveals targetable pathways. Breast Cancer Res 2024; 26:98. [PMID: 38867323 PMCID: PMC11167932 DOI: 10.1186/s13058-024-01855-0] [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: 02/02/2024] [Accepted: 06/04/2024] [Indexed: 06/14/2024] Open
Abstract
BACKGROUND The differential gene expression profile of metastatic versus primary breast tumors represents an avenue for discovering new or underappreciated pathways underscoring processes of metastasis. However, as tumor biopsy samples are a mixture of cancer and non-cancer cells, most differentially expressed genes in metastases would represent confounders involving sample biopsy site rather than cancer cell biology. METHODS By paired analysis, we defined a top set of differentially expressed genes in breast cancer metastasis versus primary tumors using an RNA-sequencing dataset of 152 patients from The Breast International Group Aiming to Understand the Molecular Aberrations dataset (BIG-AURORA). To filter the genes higher in metastasis for genes essential for breast cancer proliferation, we incorporated CRISPR-based data from breast cancer cell lines. RESULTS A significant fraction of genes with higher expression in metastasis versus paired primary were essential by CRISPR. These 264 genes represented an essential signature of breast cancer metastasis. In contrast, nonessential metastasis genes largely involved tumor biopsy site. The essential signature predicted breast cancer patient outcome based on primary tumor expression patterns. Pathways underlying the essential signature included proteasome degradation, the electron transport chain, oxidative phosphorylation, and cancer metabolic reprogramming. Transcription factors MYC, MAX, HDAC3, and HCFC1 each bound significant fractions of essential genes. CONCLUSIONS Associations involving the essential gene signature of breast cancer metastasis indicate true biological changes intrinsic to cancer cells, with important implications for applying existing therapies or developing alternate therapeutic approaches.
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Affiliation(s)
- Yiqun Zhang
- Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, One Baylor Plaza, MS305, Houston, TX, 77030, USA
| | - Fengju Chen
- Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, One Baylor Plaza, MS305, Houston, TX, 77030, USA
| | - Marija Balic
- Division of Oncology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
- Unit for Translational Breast Cancer Research, Medical University of Graz, Graz, Austria
- Division of Hematology/Oncology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Chad J Creighton
- Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, One Baylor Plaza, MS305, Houston, TX, 77030, USA.
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030, USA.
- Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
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2
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Zhu C, Liu LY, Yamaguchi TN, Zhu H, Hugh-White R, Livingstone J, Patel Y, Kislinger T, Boutros PC. moPepGen: Rapid and Comprehensive Proteoform Identification. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.28.587261. [PMID: 38585946 PMCID: PMC10996593 DOI: 10.1101/2024.03.28.587261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Gene expression is a multi-step transformation of biological information from its storage form (DNA) into functional forms (protein and some RNAs). Regulatory activities at each step of this transformation multiply a single gene into a myriad of proteoforms. Proteogenomics is the study of how genomic and transcriptomic variation creates this proteoform diversity, and is limited by the challenges of modeling the complexities of gene-expression. We therefore created moPepGen, a graph-based algorithm that comprehensively enumerates proteoforms in linear time. moPepGen works with multiple technologies, in multiple species and on all types of genetic and transcriptomic data. In human cancer proteomes, it detects and quantifies previously unobserved noncanonical peptides arising from germline and somatic genomic variants, noncoding open reading frames, RNA fusions and RNA circularization. By enabling efficient identification and quantitation of previously hidden proteins in both existing and new proteomic data, moPepGen facilitates all proteogenomics applications. It is available at: https://github.com/uclahs-cds/package-moPepGen.
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Affiliation(s)
- Chenghao Zhu
- Department of Human Genetics, University of California, Los Angeles, CA, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA
- Institute for Precision Health, University of California, Los Angeles, CA, USA
- Department of Urology, University of California, Los Angeles, CA, USA
| | - Lydia Y. Liu
- Department of Human Genetics, University of California, Los Angeles, CA, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
- Vector Institute for Artificial Intelligence, Toronto, Canada
| | - Takafumi N. Yamaguchi
- Department of Human Genetics, University of California, Los Angeles, CA, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA
- Institute for Precision Health, University of California, Los Angeles, CA, USA
| | - Helen Zhu
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
- Vector Institute for Artificial Intelligence, Toronto, Canada
| | - Rupert Hugh-White
- Department of Human Genetics, University of California, Los Angeles, CA, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA
- Institute for Precision Health, University of California, Los Angeles, CA, USA
| | - Julie Livingstone
- Department of Human Genetics, University of California, Los Angeles, CA, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA
- Institute for Precision Health, University of California, Los Angeles, CA, USA
| | - Yash Patel
- Department of Human Genetics, University of California, Los Angeles, CA, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA
- Institute for Precision Health, University of California, Los Angeles, CA, USA
| | - Thomas Kislinger
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Paul C. Boutros
- Department of Human Genetics, University of California, Los Angeles, CA, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA
- Institute for Precision Health, University of California, Los Angeles, CA, USA
- Department of Urology, University of California, Los Angeles, CA, USA
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
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Lou R, Shui W. Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023. Mol Cell Proteomics 2024; 23:100712. [PMID: 38182042 PMCID: PMC10847697 DOI: 10.1016/j.mcpro.2024.100712] [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: 10/31/2023] [Revised: 12/27/2023] [Accepted: 01/02/2024] [Indexed: 01/07/2024] Open
Abstract
Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.
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Affiliation(s)
- Ronghui Lou
- iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
| | - Wenqing Shui
- iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
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Dhanushkumar T, M E S, Selvam PK, Rambabu M, Dasegowda KR, Vasudevan K, George Priya Doss C. Advancements and hurdles in the development of a vaccine for triple-negative breast cancer: A comprehensive review of multi-omics and immunomics strategies. Life Sci 2024; 337:122360. [PMID: 38135117 DOI: 10.1016/j.lfs.2023.122360] [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: 10/12/2023] [Revised: 12/15/2023] [Accepted: 12/15/2023] [Indexed: 12/24/2023]
Abstract
Triple-Negative Breast Cancer (TNBC) presents a significant challenge in oncology due to its aggressive behavior and limited therapeutic options. This review explores the potential of immunotherapy, particularly vaccine-based approaches, in addressing TNBC. It delves into the role of immunoinformatics in creating effective vaccines against TNBC. The review first underscores the distinct attributes of TNBC and the importance of tumor antigens in vaccine development. It then elaborates on antigen detection techniques such as exome sequencing, HLA typing, and RNA sequencing, which are instrumental in identifying TNBC-specific antigens and selecting vaccine candidates. The discussion then shifts to the in-silico vaccine development process, encompassing antigen selection, epitope prediction, and rational vaccine design. This process merges computational simulations with immunological insights. The role of Artificial Intelligence (AI) in expediting the prediction of antigens and epitopes is also emphasized. The review concludes by encapsulating how Immunoinformatics can augment the design of TNBC vaccines, integrating tumor antigens, advanced detection methods, in-silico strategies, and AI-driven insights to advance TNBC immunotherapy. This could potentially pave the way for more targeted and efficacious treatments.
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Affiliation(s)
- T Dhanushkumar
- Department of Biotechnology, School of Applied Sciences, REVA University, Bengaluru 560064, India
| | - Santhosh M E
- Department of Biotechnology, School of Applied Sciences, REVA University, Bengaluru 560064, India
| | - Prasanna Kumar Selvam
- Department of Biotechnology, School of Applied Sciences, REVA University, Bengaluru 560064, India
| | - Majji Rambabu
- Department of Biotechnology, School of Applied Sciences, REVA University, Bengaluru 560064, India
| | - K R Dasegowda
- Department of Biotechnology, School of Applied Sciences, REVA University, Bengaluru 560064, India
| | - Karthick Vasudevan
- Department of Biotechnology, School of Applied Sciences, REVA University, Bengaluru 560064, India.
| | - C George Priya Doss
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of BioSciences and Technology, Vellore Institute of Technology (VIT), Vellore, India.
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Chen F, Zhang Y, Chandrashekar DS, Varambally S, Creighton CJ. Global impact of somatic structural variation on the cancer proteome. Nat Commun 2023; 14:5637. [PMID: 37704602 PMCID: PMC10499989 DOI: 10.1038/s41467-023-41374-8] [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: 04/20/2023] [Accepted: 09/01/2023] [Indexed: 09/15/2023] Open
Abstract
Both proteome and transcriptome data can help assess the relevance of non-coding somatic mutations in cancer. Here, we combine mass spectrometry-based proteomics data with whole genome sequencing data across 1307 human tumors spanning various tissues to determine the extent somatic structural variant (SV) breakpoint patterns impact protein expression of nearby genes. We find that about 25% of the hundreds of genes with SV-associated cis-regulatory alterations at the mRNA level are similarly associated at the protein level. SVs associated with enhancer hijacking, retrotransposon translocation, altered DNA methylation, or fusion transcripts are implicated in protein over-expression. SVs combined with altered protein levels considerably extend the numbers of patients with tumors somatically altered for critical pathways. We catalog both SV breakpoint patterns involving patient survival and genes with nearby SV breakpoints associated with increased cell dependency in cancer cell lines. Pan-cancer proteogenomics identifies targetable non-coding alterations, by virtue of the associated deregulated genes.
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Affiliation(s)
- Fengju Chen
- Dan L. Duncan Comprehensive Cancer Center Division of Biostatistics, Baylor College of Medicine, Houston, TX, USA
| | - Yiqun Zhang
- Dan L. Duncan Comprehensive Cancer Center Division of Biostatistics, Baylor College of Medicine, Houston, TX, USA
| | - Darshan S Chandrashekar
- Molecular and Cellular Pathology, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
- O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
- Genomic Diagnostics and Bioinformatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
| | - Sooryanarayana Varambally
- Molecular and Cellular Pathology, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
- O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
- The Informatics Institute, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
| | - Chad J Creighton
- Dan L. Duncan Comprehensive Cancer Center Division of Biostatistics, Baylor College of Medicine, Houston, TX, USA.
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030, USA.
- Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
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Monsivais D, Parks SE, Chandrashekar DS, Varambally S, Creighton CJ. Using cancer proteomics data to identify gene candidates for therapeutic targeting. Oncotarget 2023; 14:399-412. [PMID: 37141409 DOI: 10.18632/oncotarget.28420] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023] Open
Abstract
Gene-level associations obtained from mass-spectrometry-based cancer proteomics datasets represent a resource for identifying gene candidates for functional studies. When recently surveying proteomic correlates of tumor grade across multiple cancer types, we identified specific protein kinases having a functional impact on uterine endometrial cancer cells. This previously published study provides just one template for utilizing public molecular datasets to discover potential novel therapeutic targets and approaches for cancer patients. Proteomic profiling data combined with corresponding multi-omics data on human tumors and cell lines can be analyzed in various ways to prioritize genes of interest for interrogating biology. Across hundreds of cancer cell lines, CRISPR loss of function and drug sensitivity scoring can be readily integrated with protein data to predict any gene's functional impact before bench experiments are carried out. Public data portals make cancer proteomics data more accessible to the research community. Drug discovery platforms can screen hundreds of millions of small molecule inhibitors for those that target a gene or pathway of interest. Here, we discuss some of the available public genomic and proteomic resources while considering approaches to how these could be leveraged for molecular biology insights or drug discovery. We also demonstrate the inhibitory effect of BAY1217389, a TTK inhibitor recently tested in a Phase I clinical trial for the treatment of solid tumors, on uterine cancer cell line viability.
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Affiliation(s)
- Diana Monsivais
- Center for Drug Discovery, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA
- Dan L. Duncan Comprehensive Cancer Center Division of Biostatistics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Sydney E Parks
- Center for Drug Discovery, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA
- Cancer and Cell Biology Program, Baylor College of Medicine, Houston, TX 77030, USA
| | - Darshan S Chandrashekar
- O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL 35233, USA
- Molecular and Cellular Pathology, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL 35233, USA
- Genomic Diagnostics and Bioinformatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL 35233, USA
| | - Sooryanarayana Varambally
- O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL 35233, USA
- Molecular and Cellular Pathology, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL 35233, USA
- The Informatics Institute, University of Alabama at Birmingham, Birmingham, AL 35233, USA
| | - Chad J Creighton
- Dan L. Duncan Comprehensive Cancer Center Division of Biostatistics, Baylor College of Medicine, Houston, TX 77030, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
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7
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Gonzales LISA, Qiao JW, Buffier AW, Rogers LJ, Suchowerska N, McKenzie DR, Kwan AH. An omics approach to delineating the molecular mechanisms that underlie the biological effects of physical plasma. BIOPHYSICS REVIEWS 2023; 4:011312. [PMID: 38510160 PMCID: PMC10903421 DOI: 10.1063/5.0089831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 02/24/2023] [Indexed: 03/22/2024]
Abstract
The use of physical plasma to treat cancer is an emerging field, and interest in its applications in oncology is increasing rapidly. Physical plasma can be used directly by aiming the plasma jet onto cells or tissue, or indirectly, where a plasma-treated solution is applied. A key scientific question is the mechanism by which physical plasma achieves selective killing of cancer over normal cells. Many studies have focused on specific pathways and mechanisms, such as apoptosis and oxidative stress, and the role of redox biology. However, over the past two decades, there has been a rise in omics, the systematic analysis of entire collections of molecules in a biological entity, enabling the discovery of the so-called "unknown unknowns." For example, transcriptomics, epigenomics, proteomics, and metabolomics have helped to uncover molecular mechanisms behind the action of physical plasma, revealing critical pathways beyond those traditionally associated with cancer treatments. This review showcases a selection of omics and then summarizes the insights gained from these studies toward understanding the biological pathways and molecular mechanisms implicated in physical plasma treatment. Omics studies have revealed how reactive species generated by plasma treatment preferentially affect several critical cellular pathways in cancer cells, resulting in epigenetic, transcriptional, and post-translational changes that promote cell death. Finally, this review considers the outlook for omics in uncovering both synergies and antagonisms with other common cancer therapies, as well as in overcoming challenges in the clinical translation of physical plasma.
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Affiliation(s)
- Lou I. S. A. Gonzales
- School of Life and Environmental Sciences, The University of Sydney, NSW 2006, Australia
| | - Jessica W. Qiao
- School of Life and Environmental Sciences, The University of Sydney, NSW 2006, Australia
| | - Aston W. Buffier
- School of Life and Environmental Sciences, The University of Sydney, NSW 2006, Australia
| | | | | | | | - Ann H. Kwan
- Author to whom correspondence should be addressed:
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