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Zou J, Li Z, Carleton N, Oesterreich S, Lee AV, Tseng GC. Mutual information for detecting multi-class biomarkers when integrating multiple bulk or single-cell transcriptomic studies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.11.598484. [PMID: 38915481 PMCID: PMC11195192 DOI: 10.1101/2024.06.11.598484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Motivation Biomarker detection plays a pivotal role in biomedical research. Integrating omics studies from multiple cohorts can enhance statistical power, accuracy and robustness of the detection results. However, existing methods for horizontally combining omics studies are mostly designed for two-class scenarios (e.g., cases versus controls) and are not directly applicable for studies with multi-class design (e.g., samples from multiple disease subtypes, treatments, tissues, or cell types). Results We propose a statistical framework, namely Mutual Information Concordance Analysis (MICA), to detect biomarkers with concordant multi-class expression pattern across multiple omics studies from an information theoretic perspective. Our approach first detects biomarkers with concordant multi-class patterns across partial or all of the omics studies using a global test by mutual information. A post hoc analysis is then performed for each detected biomarkers and identify studies with concordant pattern. Extensive simulations demonstrate improved accuracy and successful false discovery rate control of MICA compared to an existing MCC method. The method is then applied to two practical scenarios: four tissues of mouse metabolism-related transcriptomic studies, and three sources of estrogen treatment expression profiles. Detected biomarkers by MICA show intriguing biological insights and functional annotations. Additionally, we implemented MICA for single-cell RNA-Seq data for tumor progression biomarkers, highlighting critical roles of ribosomal function in the tumor microenvironment of triple-negative breast cancer and underscoring the potential of MICA for detecting novel therapeutic targets. Availability https://github.com/jianzou75/MICA.
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Affiliation(s)
- Jian Zou
- Department of Statistics, School of Public Health, Chongqing Medical University, Chongqing, 400016, Chongqing, China
| | - Zheqi Li
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, 02215, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, 02215, Massachusetts, USA
| | - Neil Carleton
- Women’s Cancer Research Center, UPMC Hillman Cancer Center (HCC), Pittsburgh, 15232, Pennsylvania, USA
- Magee-Womens Research Institute, Pittsburgh, 15213, Pennsylvania, USA
- Medical Scientist Training Program, School of Medicine, University of Pittsburgh, Pittsburgh, 15213, Pennsylvania, USA
| | - Steffi Oesterreich
- Women’s Cancer Research Center, UPMC Hillman Cancer Center (HCC), Pittsburgh, 15232, Pennsylvania, USA
- Magee-Womens Research Institute, Pittsburgh, 15213, Pennsylvania, USA
- Department of Pharmacology & Chemical Biology, University of Pittsburgh, Pittsburgh, 15213, Pennsylvania, USA
| | - Adrian V. Lee
- Women’s Cancer Research Center, UPMC Hillman Cancer Center (HCC), Pittsburgh, 15232, Pennsylvania, USA
- Magee-Womens Research Institute, Pittsburgh, 15213, Pennsylvania, USA
- Department of Pharmacology & Chemical Biology, University of Pittsburgh, Pittsburgh, 15213, Pennsylvania, USA
| | - George C. Tseng
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, 15213, Pennsylvania, USA
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Tian J, Bai X, Quek C. Single-Cell Informatics for Tumor Microenvironment and Immunotherapy. Int J Mol Sci 2024; 25:4485. [PMID: 38674070 PMCID: PMC11050520 DOI: 10.3390/ijms25084485] [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: 03/08/2024] [Revised: 04/12/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
Cancer comprises malignant cells surrounded by the tumor microenvironment (TME), a dynamic ecosystem composed of heterogeneous cell populations that exert unique influences on tumor development. The immune community within the TME plays a substantial role in tumorigenesis and tumor evolution. The innate and adaptive immune cells "talk" to the tumor through ligand-receptor interactions and signaling molecules, forming a complex communication network to influence the cellular and molecular basis of cancer. Such intricate intratumoral immune composition and interactions foster the application of immunotherapies, which empower the immune system against cancer to elicit durable long-term responses in cancer patients. Single-cell technologies have allowed for the dissection and characterization of the TME to an unprecedented level, while recent advancements in bioinformatics tools have expanded the horizon and depth of high-dimensional single-cell data analysis. This review will unravel the intertwined networks between malignancy and immunity, explore the utilization of computational tools for a deeper understanding of tumor-immune communications, and discuss the application of these approaches to aid in diagnosis or treatment decision making in the clinical setting, as well as the current challenges faced by the researchers with their potential future improvements.
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Affiliation(s)
| | | | - Camelia Quek
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; (J.T.); (X.B.)
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Ferri-Borgogno S, Burks JK, Seeley EH, McKee TD, Stolley DL, Basi AV, Gomez JA, Gamal BT, Ayyadhury S, Lawson BC, Yates MS, Birrer MJ, Lu KH, Mok SC. Molecular, Metabolic, and Subcellular Mapping of the Tumor Immune Microenvironment via 3D Targeted and Non-Targeted Multiplex Multi-Omics Analyses. Cancers (Basel) 2024; 16:846. [PMID: 38473208 PMCID: PMC10930466 DOI: 10.3390/cancers16050846] [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: 02/07/2024] [Revised: 02/16/2024] [Accepted: 02/17/2024] [Indexed: 03/14/2024] Open
Abstract
Most platforms used for the molecular reconstruction of the tumor-immune microenvironment (TIME) of a solid tumor fail to explore the spatial context of the three-dimensional (3D) space of the tumor at a single-cell resolution, and thus lack information about cell-cell or cell-extracellular matrix (ECM) interactions. To address this issue, a pipeline which integrated multiplex spatially resolved multi-omics platforms was developed to identify crosstalk signaling networks among various cell types and the ECM in the 3D TIME of two FFPE (formalin-fixed paraffin embedded) gynecologic tumor samples. These platforms include non-targeted mass spectrometry imaging (glycans, metabolites, and peptides) and Stereo-seq (spatial transcriptomics) and targeted seqIF (IHC proteomics). The spatially resolved imaging data in a two- and three-dimensional space demonstrated various cellular neighborhoods in both samples. The collection of spatially resolved analytes in a voxel (3D pixel) across serial sections of the tissue was also demonstrated. Data collected from this analytical pipeline were used to construct spatial 3D maps with single-cell resolution, which revealed cell identity, activation, and energized status. These maps will provide not only insights into the molecular basis of spatial cell heterogeneity in the TIME, but also novel predictive biomarkers and therapeutic targets, which can improve patient survival rates.
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Affiliation(s)
- Sammy Ferri-Borgogno
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA (K.H.L.)
| | - Jared K. Burks
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (D.L.S.); (A.V.B.); (J.A.G.)
| | - Erin H. Seeley
- Department of Chemistry, The University of Texas at Austin, Austin, TX 78712, USA;
| | - Trevor D. McKee
- Pathomics, Inc., Toronto, ON M4C 3K2, Canada; (T.D.M.); (S.A.)
| | - Danielle L. Stolley
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (D.L.S.); (A.V.B.); (J.A.G.)
| | - Akshay V. Basi
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (D.L.S.); (A.V.B.); (J.A.G.)
| | - Javier A. Gomez
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (D.L.S.); (A.V.B.); (J.A.G.)
| | - Basant T. Gamal
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA (K.H.L.)
| | | | - Barrett C. Lawson
- Department of Anatomical Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Melinda S. Yates
- Department of Pathology and Laboratory Medicine, The University of North Carolina, Chapel Hill, NC 27599, USA
| | - Michael J. Birrer
- Winthrop P. Rockefelle Cancer Institute, The University of Arkanasas for Medical Sciences, Little Rock, AR 72205, USA
| | - Karen H. Lu
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA (K.H.L.)
| | - Samuel C. Mok
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA (K.H.L.)
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Maeser D, Zhang W, Huang Y, Huang RS. A review of computational methods for predicting cancer drug response at the single-cell level through integration with bulk RNAseq data. Curr Opin Struct Biol 2024; 84:102745. [PMID: 38109840 PMCID: PMC10922290 DOI: 10.1016/j.sbi.2023.102745] [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: 09/14/2023] [Revised: 11/06/2023] [Accepted: 11/24/2023] [Indexed: 12/20/2023]
Abstract
Cancer treatment failure is often attributed to tumor heterogeneity, where diverse malignant cell clones exist within a patient. Despite a growing understanding of heterogeneous tumor cells depicted by single-cell RNA sequencing (scRNA-seq), there is still a gap in the translation of such knowledge into treatment strategies tackling the pervasive issue of therapy resistance. In this review, we survey methods leveraging large-scale drug screens to generate cellular sensitivities to various therapeutics. These methods enable efficient drug screens in scRNA-seq data and serve as the bedrock of drug discovery for specific cancer cell groups. We envision that they will become an indispensable tool for tailoring patient care in the era of heterogeneity-aware precision medicine.
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Affiliation(s)
- Danielle Maeser
- Department of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, United States; Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, United States
| | - Weijie Zhang
- Department of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, United States; Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, United States
| | - Yingbo Huang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, United States
| | - R Stephanie Huang
- Department of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, United States; Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, United States.
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Ghaddar B, De S. Hierarchical and automated cell-type annotation and inference of cancer cell of origin with Census. Bioinformatics 2023; 39:btad714. [PMID: 38011649 PMCID: PMC10713118 DOI: 10.1093/bioinformatics/btad714] [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: 02/21/2023] [Revised: 10/26/2023] [Accepted: 11/25/2023] [Indexed: 11/29/2023] Open
Abstract
MOTIVATION Cell-type annotation is a time-consuming yet critical first step in the analysis of single-cell RNA-seq data, especially when multiple similar cell subtypes with overlapping marker genes are present. Existing automated annotation methods have a number of limitations, including requiring large reference datasets, high computation time, shallow annotation resolution, and difficulty in identifying cancer cells or their most likely cell of origin. RESULTS We developed Census, a biologically intuitive and fully automated cell-type identification method for single-cell RNA-seq data that can deeply annotate normal cells in mammalian tissues and identify malignant cells and their likely cell of origin. Motivated by the inherently stratified developmental programs of cellular differentiation, Census infers hierarchical cell-type relationships and uses gradient-boosted \decision trees that capitalize on nodal cell-type relationships to achieve high prediction speed and accuracy. When benchmarked on 44 atlas-scale normal and cancer, human and mouse tissues, Census significantly outperforms state-of-the-art methods across multiple metrics and naturally predicts the cell-of-origin of different cancers. Census is pretrained on the Tabula Sapiens to classify 175 cell-types from 24 organs; however, users can seamlessly train their own models for customized applications. AVAILABILITY AND IMPLEMENTATION Census is available at Zenodo https://zenodo.org/records/7017103 and on our Github https://github.com/sjdlabgroup/Census.
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Affiliation(s)
- Bassel Ghaddar
- Center for Systems and Computational Biology, Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ 08901, United States
| | - Subhajyoti De
- Center for Systems and Computational Biology, Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ 08901, United States
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Yu X, Hu J, Tan Y, Pan M, Zhang H, Li B. MitoTracer facilitates the identification of informative mitochondrial mutations for precise lineage reconstruction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.22.568285. [PMID: 38045409 PMCID: PMC10690277 DOI: 10.1101/2023.11.22.568285] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Mitochondrial (MT) mutations serve as natural genetic markers for inferring clonal relationships using single cell sequencing data. However, the fundamental challenge of MT mutation-based lineage tracing is automated identification of informative MT mutations. Here, we introduced an open-source computational algorithm called "MitoTracer", which accurately identified clonally informative MT mutations and inferred evolutionary lineage from scRNA-seq or scATAC-seq samples. We benchmarked MitoTracer using the ground-truth experimental lineage sequencing data and demonstrated its superior performance over the existing methods measured by high sensitivity and specificity. MitoTracer is compatible with multiple single cell sequencing platforms. Its application to a cancer evolution dataset revealed the genes related to primary BRAF-inhibitor resistance from scRNA-seq data of BRAF-mutated cancer cells. Overall, our work provided a valuable tool for capturing real informative MT mutations and tracing the lineages among cells. Teaser MitoTracer enables automatically and accurately discover informative mitochondrial mutations for lineage tracing.
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