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Babu E, Sen S. Explore & actuate: the future of personalized medicine in oncology through emerging technologies. Curr Opin Oncol 2024; 36:93-101. [PMID: 38441149 DOI: 10.1097/cco.0000000000001016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
PURPOSE OF REVIEW The future of medicine is aimed to equip the physician with tools to assess the individual health of the patient for the uniqueness of the disease that separates it from the rest. The integration of omics technologies into clinical practice, reviewed here, would open new avenues for addressing the spatial and temporal heterogeneity of cancer. The rising cancer burden patiently awaits the advent of such an approach to personalized medicine for routine clinical settings. RECENT FINDINGS To weigh the translational potential, multiple technologies were categorized based on the extractable information from the different types of samples used, to the various omic-levels of molecular information that each technology has been able to advance over the last 2 years. This review uses a multifaceted classification that helps to assess translational potential in a meaningful way toward clinical adaptation. SUMMARY The importance of distinguishing technologies based on the flow of information from exploration to actuation puts forth a framework that allows the clinicians to better adapt a chosen technology or use them in combination to enhance their goals toward personalized medicine.
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Affiliation(s)
- Erald Babu
- UM-DAE Centre for Excellence in Basic Sciences, School of Biological Sciences, University of Mumbai, Kalina Campus, Mumbai, Maharashtra, India
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2
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Bai X, Duren Z, Wan L, Xia LC. Joint inference of clonal structure using single-cell genome and transcriptome sequencing data. NAR Genom Bioinform 2024; 6:lqae017. [PMID: 38486887 PMCID: PMC10939367 DOI: 10.1093/nargab/lqae017] [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: 06/21/2023] [Revised: 11/19/2023] [Accepted: 01/29/2024] [Indexed: 03/17/2024] Open
Abstract
Latest advancements in the high-throughput single-cell genome (scDNA) and transcriptome (scRNA) sequencing technologies enabled cell-resolved investigation of tissue clones. However, it remains challenging to cluster and couple single cells for heterogeneous scRNA and scDNA data generated from the same specimen. In this study, we present a computational framework called CCNMF, which employs a novel Coupled-Clone Non-negative Matrix Factorization technique to jointly infer clonal structure for matched scDNA and scRNA data. CCNMF couples multi-omics single cells by linking copy number and gene expression profiles through their general concordance. It successfully resolved the underlying coexisting clones with high correlations between the clonal genome and transcriptome from the same specimen. We validated that CCNMF can achieve high accuracy and robustness using both simulated benchmarks and real-world applications, including an ovarian cancer cell lines mixture, a gastric cancer cell line, and a primary gastric cancer. In summary, CCNMF provides a powerful tool for integrating multi-omics single-cell data, enabling simultaneous resolution of genomic and transcriptomic clonal architecture. This computational framework facilitates the understanding of how cellular gene expression changes in conjunction with clonal genome alternations, shedding light on the cellular genomic difference of subclones that contributes to tumor evolution.
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Affiliation(s)
- Xiangqi Bai
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Zhana Duren
- Center for Human Genetics and Department of Genetics and Biochemistry, Clemson University, Greenwood, SC 29646, USA
| | - Lin Wan
- NCMIS, LSC, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Li C Xia
- Department of Statistics and Financial Mathematics, School of Mathematics, South China University of Technology, Guangzhou, 510006, China
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Antonello A, Bergamin R, Calonaci N, Househam J, Milite S, Williams MJ, Anselmi F, d'Onofrio A, Sundaram V, Sosinsky A, Cross WCH, Caravagna G. Computational validation of clonal and subclonal copy number alterations from bulk tumor sequencing using CNAqc. Genome Biol 2024; 25:38. [PMID: 38297376 PMCID: PMC10832148 DOI: 10.1186/s13059-024-03170-5] [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: 05/02/2023] [Accepted: 01/10/2024] [Indexed: 02/02/2024] Open
Abstract
Copy number alterations (CNAs) are among the most important genetic events in cancer, but their detection from sequencing data is challenging because of unknown sample purity, tumor ploidy, and general intra-tumor heterogeneity. Here, we present CNAqc, an evolution-inspired method to perform the computational validation of clonal and subclonal CNAs detected from bulk DNA sequencing. CNAqc is validated using single-cell data and simulations, is applied to over 4000 TCGA and PCAWG samples, and is incorporated into the validation process for the clinically accredited bioinformatics pipeline at Genomics England. CNAqc is designed to support automated quality control procedures for tumor somatic data validation.
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Affiliation(s)
- Alice Antonello
- Department of Mathematics, Informatics and Geosciences (MIGe), University of Trieste, Trieste, Italy
| | - Riccardo Bergamin
- Department of Mathematics, Informatics and Geosciences (MIGe), University of Trieste, Trieste, Italy
| | - Nicola Calonaci
- Department of Mathematics, Informatics and Geosciences (MIGe), University of Trieste, Trieste, Italy
| | - Jacob Househam
- Evolution and Cancer Lab, Centre for Genomics and Computational Biology, Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Salvatore Milite
- Department of Mathematics, Informatics and Geosciences (MIGe), University of Trieste, Trieste, Italy
- Centre for Computational Biology, Human Technopole, Milan, Italy
| | - Marc J Williams
- Department of Computational Oncology, Memorial Sloan Kettering, New York, USA
| | - Fabio Anselmi
- Department of Mathematics, Informatics and Geosciences (MIGe), University of Trieste, Trieste, Italy
| | - Alberto d'Onofrio
- Department of Mathematics, Informatics and Geosciences (MIGe), University of Trieste, Trieste, Italy
| | | | | | - William C H Cross
- Department of Research Pathology, UCL Cancer Institute, University College London, London, UK
| | - Giulio Caravagna
- Department of Mathematics, Informatics and Geosciences (MIGe), University of Trieste, Trieste, Italy.
- Evolutionary Genomics and Modelling Team, Centre for Evolution and Cancer, Institute of Cancer Research, London, UK.
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Patruno L, Milite S, Bergamin R, Calonaci N, D’Onofrio A, Anselmi F, Antoniotti M, Graudenzi A, Caravagna G. A Bayesian method to infer copy number clones from single-cell RNA and ATAC sequencing. PLoS Comput Biol 2023; 19:e1011557. [PMID: 37917660 PMCID: PMC10645363 DOI: 10.1371/journal.pcbi.1011557] [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: 08/14/2023] [Revised: 11/14/2023] [Accepted: 09/30/2023] [Indexed: 11/04/2023] Open
Abstract
Single-cell RNA and ATAC sequencing technologies enable the examination of gene expression and chromatin accessibility in individual cells, providing insights into cellular phenotypes. In cancer research, it is important to consistently analyze these states within an evolutionary context on genetic clones. Here we present CONGAS+, a Bayesian model to map single-cell RNA and ATAC profiles onto the latent space of copy number clones. CONGAS+ clusters cells into tumour subclones with similar ploidy, rendering straightforward to compare their expression and chromatin profiles. The framework, implemented on GPU and tested on real and simulated data, scales to analyse seamlessly thousands of cells, demonstrating better performance than single-molecule models, and supporting new multi-omics assays. In prostate cancer, lymphoma and basal cell carcinoma, CONGAS+ successfully identifies complex subclonal architectures while providing a coherent mapping between ATAC and RNA, facilitating the study of genotype-phenotype maps and their connection to genomic instability.
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Affiliation(s)
- Lucrezia Patruno
- Department of Informatics, Systems and Communication, Università degli Studi di Milano-Bicocca, Milan, Italy
- Department of Mathematics and Geosciences, Università degli Studi di Trieste, Trieste, Italy
| | - Salvatore Milite
- Department of Mathematics and Geosciences, Università degli Studi di Trieste, Trieste, Italy
- Centre for Computational Biology, Human Technopole, Milan, Italy
| | - Riccardo Bergamin
- Department of Mathematics and Geosciences, Università degli Studi di Trieste, Trieste, Italy
| | - Nicola Calonaci
- Department of Mathematics and Geosciences, Università degli Studi di Trieste, Trieste, Italy
| | - Alberto D’Onofrio
- Department of Mathematics and Geosciences, Università degli Studi di Trieste, Trieste, Italy
| | - Fabio Anselmi
- Department of Mathematics and Geosciences, Università degli Studi di Trieste, Trieste, Italy
| | - Marco Antoniotti
- Department of Informatics, Systems and Communication, Università degli Studi di Milano-Bicocca, Milan, Italy
- B4—Bicocca Bioinformatics Biostatistics and Bioimaging Centre, Università degli Studi di Milano-Bicocca, Milan, Italy
| | - Alex Graudenzi
- Department of Informatics, Systems and Communication, Università degli Studi di Milano-Bicocca, Milan, Italy
- B4—Bicocca Bioinformatics Biostatistics and Bioimaging Centre, Università degli Studi di Milano-Bicocca, Milan, Italy
| | - Giulio Caravagna
- Department of Mathematics and Geosciences, Università degli Studi di Trieste, Trieste, Italy
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Weber LL, Zhang C, Ochoa I, El-Kebir M. Phertilizer: Growing a clonal tree from ultra-low coverage single-cell DNA sequencing of tumors. PLoS Comput Biol 2023; 19:e1011544. [PMID: 37819942 PMCID: PMC10593221 DOI: 10.1371/journal.pcbi.1011544] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 10/23/2023] [Accepted: 09/26/2023] [Indexed: 10/13/2023] Open
Abstract
Emerging ultra-low coverage single-cell DNA sequencing (scDNA-seq) technologies have enabled high resolution evolutionary studies of copy number aberrations (CNAs) within tumors. While these sequencing technologies are well suited for identifying CNAs due to the uniformity of sequencing coverage, the sparsity of coverage poses challenges for the study of single-nucleotide variants (SNVs). In order to maximize the utility of increasingly available ultra-low coverage scDNA-seq data and obtain a comprehensive understanding of tumor evolution, it is important to also analyze the evolution of SNVs from the same set of tumor cells. We present Phertilizer, a method to infer a clonal tree from ultra-low coverage scDNA-seq data of a tumor. Based on a probabilistic model, our method recursively partitions the data by identifying key evolutionary events in the history of the tumor. We demonstrate the performance of Phertilizer on simulated data as well as on two real datasets, finding that Phertilizer effectively utilizes the copy-number signal inherent in the data to more accurately uncover clonal structure and genotypes compared to previous methods.
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Affiliation(s)
- Leah L. Weber
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana-Champaign, Illinois, United States of America
| | - Chuanyi Zhang
- Department of Electrical & Computer Engineering, University of Illinois Urbana-Champaign, Urbana-Champaign, Illinois, United States of America
| | - Idoia Ochoa
- Department of Electrical & Computer Engineering, University of Illinois Urbana-Champaign, Urbana-Champaign, Illinois, United States of America
- Department of Electrical and Electronics Engineering, University of Navarre, Donostia, Spain
| | - Mohammed El-Kebir
- Department of Electrical and Electronics Engineering, University of Navarre, Donostia, Spain
- Cancer Center at Illinois, University of Illinois Urbana-Champaign, Urbana-Champaign, Illinois, United States of America
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