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Chi WY, Hu Y, Huang HC, Kuo HH, Lin SH, Kuo CTJ, Tao J, Fan D, Huang YM, Wu AA, Hung CF, Wu TC. Molecular targets and strategies in the development of nucleic acid cancer vaccines: from shared to personalized antigens. J Biomed Sci 2024; 31:94. [PMID: 39379923 PMCID: PMC11463125 DOI: 10.1186/s12929-024-01082-x] [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: 07/19/2024] [Accepted: 09/01/2024] [Indexed: 10/10/2024] Open
Abstract
Recent breakthroughs in cancer immunotherapies have emphasized the importance of harnessing the immune system for treating cancer. Vaccines, which have traditionally been used to promote protective immunity against pathogens, are now being explored as a method to target cancer neoantigens. Over the past few years, extensive preclinical research and more than a hundred clinical trials have been dedicated to investigating various approaches to neoantigen discovery and vaccine formulations, encouraging development of personalized medicine. Nucleic acids (DNA and mRNA) have become particularly promising platform for the development of these cancer immunotherapies. This shift towards nucleic acid-based personalized vaccines has been facilitated by advancements in molecular techniques for identifying neoantigens, antigen prediction methodologies, and the development of new vaccine platforms. Generating these personalized vaccines involves a comprehensive pipeline that includes sequencing of patient tumor samples, data analysis for antigen prediction, and tailored vaccine manufacturing. In this review, we will discuss the various shared and personalized antigens used for cancer vaccine development and introduce strategies for identifying neoantigens through the characterization of gene mutation, transcription, translation and post translational modifications associated with oncogenesis. In addition, we will focus on the most up-to-date nucleic acid vaccine platforms, discuss the limitations of cancer vaccines as well as provide potential solutions, and raise key clinical and technical considerations in vaccine development.
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Affiliation(s)
- Wei-Yu Chi
- Physiology, Biophysics and Systems Biology Graduate Program, Weill Cornell Medicine, New York, NY, USA
| | - Yingying Hu
- Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hsin-Che Huang
- Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hui-Hsuan Kuo
- Pharmacology PhD Program, Weill Cornell Medicine, New York, NY, USA
| | - Shu-Hong Lin
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- The University of Texas Graduate School of Biomedical Sciences at Houston and MD Anderson Cancer Center, Houston, TX, USA
| | - Chun-Tien Jimmy Kuo
- Division of Pharmaceutics and Pharmacology, College of Pharmacy, The Ohio State University, Columbus, OH, USA
| | - Julia Tao
- Department of Pathology, Johns Hopkins School of Medicine, 1550 Orleans St, CRB II Room 309, Baltimore, MD, 21287, USA
| | - Darrell Fan
- Department of Pathology, Johns Hopkins School of Medicine, 1550 Orleans St, CRB II Room 309, Baltimore, MD, 21287, USA
| | - Yi-Min Huang
- Department of Pathology, Johns Hopkins School of Medicine, 1550 Orleans St, CRB II Room 309, Baltimore, MD, 21287, USA
| | - Annie A Wu
- Department of Pathology, Johns Hopkins School of Medicine, 1550 Orleans St, CRB II Room 309, Baltimore, MD, 21287, USA
| | - Chien-Fu Hung
- Department of Pathology, Johns Hopkins School of Medicine, 1550 Orleans St, CRB II Room 309, Baltimore, MD, 21287, USA
- Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Obstetrics and Gynecology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - T-C Wu
- Department of Pathology, Johns Hopkins School of Medicine, 1550 Orleans St, CRB II Room 309, Baltimore, MD, 21287, USA.
- Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Obstetrics and Gynecology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins School of Medicine, Baltimore, MD, USA.
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Patel Y, Zhu C, Yamaguchi TN, Wang NK, Wiltsie N, Gonzalez AE, Winata HK, Zeltser N, Pan Y, Mootor MFE, Sanders T, Kandoth C, Fitz-Gibbon ST, Livingstone J, Liu LY, Carlin B, Holmes A, Oh J, Sahrmann J, Tao S, Eng S, Hugh-White R, Pashminehazar K, Park A, Beshlikyan A, Jordan M, Wu S, Tian M, Arbet J, Neilsen B, Bugh YZ, Kim G, Salmingo J, Zhang W, Haas R, Anand A, Hwang E, Neiman-Golden A, Steinberg P, Zhao W, Anand P, Tsai BL, Boutros PC. Metapipeline-DNA: A Comprehensive Germline & Somatic Genomics Nextflow Pipeline. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.04.611267. [PMID: 39282325 PMCID: PMC11398472 DOI: 10.1101/2024.09.04.611267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/21/2024]
Abstract
Summary DNA sequencing is becoming more affordable and faster through advances in high-throughput technologies. This rise in data availability has contributed to the development of novel algorithms to elucidate previously obscure features and led to an increased reliance on complex workflows to integrate such tools into analyses pipelines. To facilitate the analysis of DNA sequencing data, we created metapipeline-DNA, a highly configurable and extensible pipeline. It encompasses a broad range of processing including raw sequencing read alignment and recalibration, variant calling, quality control and subclonal reconstruction. Metapipeline-DNA also contains configuration options to select and tune analyses while being robust to failures. This standardizes and simplifies the ability to analyze large DNA sequencing in both clinical and research settings. Availability Metapipeline-DNA is an open-source Nextflow pipeline under the GPLv2 license and is freely available at https://github.com/uclahs-cds/metapipeline-DNA.
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Affiliation(s)
- Yash Patel
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Chenghao Zhu
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Takafumi N. Yamaguchi
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Nicholas K. Wang
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Nicholas Wiltsie
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Alfredo E. Gonzalez
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Helena K. Winata
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Nicole Zeltser
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Yu Pan
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Mohammed Faizal Eeman Mootor
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Timothy Sanders
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Cyriac Kandoth
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Sorel T. Fitz-Gibbon
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Julie Livingstone
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Lydia Y. Liu
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Benjamin Carlin
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Aaron Holmes
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Jieun Oh
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - John Sahrmann
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Shu Tao
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Stefan Eng
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Rupert Hugh-White
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Kiarod Pashminehazar
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Andrew Park
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Arpi Beshlikyan
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Madison Jordan
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Selina Wu
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Mao Tian
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Jaron Arbet
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Beth Neilsen
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Yuan Zhe Bugh
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Gina Kim
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Joseph Salmingo
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Wenshu Zhang
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Roni Haas
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Aakarsh Anand
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Edward Hwang
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Anna Neiman-Golden
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Philippa Steinberg
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Wenyan Zhao
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Prateek Anand
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Brandon L. Tsai
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
| | - Paul C. Boutros
- Department of Human Genetics, University of California, Los Angeles, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, USA
- Institute for Precision Health, University of California, Los Angeles, USA
- Department of Urology, University of California, Los Angeles, USA
- Broad Stem Cell Research Center, University of California, Los Angeles, USA
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Liang L, Liang X, Yu X, Xiang W. Bioinformatic Analyses and Integrated Machine Learning to Predict prognosis and therapeutic response Based on E3 Ligase-Related Genes in colon cancer. J Cancer 2024; 15:5376-5395. [PMID: 39247594 PMCID: PMC11375543 DOI: 10.7150/jca.98723] [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: 05/21/2024] [Accepted: 08/09/2024] [Indexed: 09/10/2024] Open
Abstract
Purpose: Colorectal cancer is the third most common cause of cancer death worldwide. We probed the correlations between E3 ubiquitin ligase (E3)-related genes (ERGs) and colon cancer prognosis and immune responses. Methods: Gene expression profiles and clinical data of patients with colon cancer were acquired from the TCGA, GTEx, GSE17537 and GSE29621 databases. ERGs were identified by coexpression analysis. WGCNA and differential expression analysis were subsequently conducted. Consensus clustering identified two molecular clusters. Differential analysis of the two clusters and Cox regression were then conducted. A prognostic model was constructed based on 10 machine learning algorithms and 92 algorithm combinations. The CIBERSORT, ssGSEA and TIMER algorithms were used to estimate immune infiltration. The OncoPredict algorithm and The Cancer Immunome Atlas (TCIA) predicted susceptibility to chemotherapeutic and targeted drugs and immunotherapy sensitivity. CCK-8, scratch-wound and RT‒PCR assays were subsequently conducted. Results: Two ERG-associated clusters were identified. The prognosis and immune function of patients in cluster A were superior to those of patients in cluster B. We constructed a prognostic model with perfect predictive capability and validated it in internal and external colon cancer datasets. We discovered significant discrepancies in immune infiltration and immune checkpoints between different risk groups. The group with high-risk had a reduced half-maximal inhibitory concentration (IC50) for some routine antitumor drugs and reduced susceptibility to immunotherapy. In vitro experiments demonstrated that the ectopic expression of PRELP inhibited the migration and proliferation of CRC cells. Conclusions: In summary, we identified novel molecular subtypes and developed a prognostic model, which will help a lot in the advancement of better forecasting and therapeutic approaches.
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Affiliation(s)
- Lunxi Liang
- Department of Gastroenterology, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China
- Department of Gastroenterology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Xiao Liang
- School of Clinical Medicine, Changsha Medical University, Changsha, China
| | - Xueke Yu
- Department of Gastroenterology, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China
| | - Wanting Xiang
- Department of Pathology, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China
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Weiner S, Li B, Nabavi S. Improved allele-specific single-cell copy number estimation in low-coverage DNA-sequencing. Bioinformatics 2024; 40:btae506. [PMID: 39133157 PMCID: PMC11346770 DOI: 10.1093/bioinformatics/btae506] [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: 04/03/2024] [Revised: 07/12/2024] [Accepted: 08/09/2024] [Indexed: 08/13/2024] Open
Abstract
MOTIVATION Advances in whole-genome single-cell DNA sequencing (scDNA-seq) have led to the development of numerous methods for detecting copy number aberrations (CNAs), a key driver of genetic heterogeneity in cancer. While most of these methods are limited to the inference of total copy number, some recent approaches now infer allele-specific CNAs using innovative techniques for estimating allele-frequencies in low coverage scDNA-seq data. However, these existing allele-specific methods are limited in their segmentation strategies, a crucial step in the CNA detection pipeline. RESULTS We present SEACON (Single-cell Estimation of Allele-specific COpy Numbers), an allele-specific copy number profiler for scDNA-seq data. SEACON uses a Gaussian Mixture Model to identify latent copy number states and breakpoints between contiguous segments across cells, filters the segments for high-quality breakpoints using an ensemble technique, and adopts several strategies for tolerating noisy read-depth and allele frequency measurements. Using a wide array of both real and simulated datasets, we show that SEACON derives accurate copy numbers and surpasses existing approaches under numerous experimental conditions, and identify its strengths and weaknesses. AVAILABILITY AND IMPLEMENTATION SEACON is implemented in Python and is freely available open-source from https://github.com/NabaviLab/SEACON and https://doi.org/10.5281/zenodo.12727008.
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Affiliation(s)
- Samson Weiner
- School of Computing, University of Connecticut, Storrs, CT 06082, United States
| | - Bingjun Li
- School of Computing, University of Connecticut, Storrs, CT 06082, United States
| | - Sheida Nabavi
- School of Computing, University of Connecticut, Storrs, CT 06082, United States
- Institute for Systems Genomics, University of Connecticut, Storrs, CT 06082, United States
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Lai J, Yang Y, Liu Y, Scharpf RB, Karchin R. Assessing the merits: an opinion on the effectiveness of simulation techniques in tumor subclonal reconstruction. BIOINFORMATICS ADVANCES 2024; 4:vbae094. [PMID: 38948008 PMCID: PMC11213631 DOI: 10.1093/bioadv/vbae094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 05/28/2024] [Accepted: 06/15/2024] [Indexed: 07/02/2024]
Abstract
Summary Neoplastic tumors originate from a single cell, and their evolution can be traced through lineages characterized by mutations, copy number alterations, and structural variants. These lineages are reconstructed and mapped onto evolutionary trees with algorithmic approaches. However, without ground truth benchmark sets, the validity of an algorithm remains uncertain, limiting potential clinical applicability. With a growing number of algorithms available, there is urgent need for standardized benchmark sets to evaluate their merits. Benchmark sets rely on in silico simulations of tumor sequence, but there are no accepted standards for simulation tools, presenting a major obstacle to progress in this field. Availability and implementation All analysis done in the paper was based on publicly available data from the publication of each accessed tool.
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Affiliation(s)
- Jiaying Lai
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Yi Yang
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Yunzhou Liu
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Robert B Scharpf
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, United States
- Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD 21231, United States
| | - Rachel Karchin
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, United States
- Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD 21231, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States
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6
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Choi Y, Choi SA, Koh EJ, Yun I, Park S, Jeon S, Kim Y, Park S, Woo D, Phi JH, Park SH, Kim DS, Kim SH, Choi JW, Lee JW, Jung TY, Bhak J, Lee S, Kim SK. Comprehensive multiomics analysis reveals distinct differences between pediatric choroid plexus papilloma and carcinoma. Acta Neuropathol Commun 2024; 12:93. [PMID: 38867333 PMCID: PMC11167863 DOI: 10.1186/s40478-024-01814-y] [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: 02/08/2024] [Accepted: 06/02/2024] [Indexed: 06/14/2024] Open
Abstract
Choroid plexus tumors (CPTs) are intraventricular tumors derived from the choroid plexus epithelium and occur frequently in children. The aim of this study was to investigate the genomic and epigenomic characteristics of CPT and identify the differences between choroid plexus papilloma (CPP) and choroid plexus carcinoma (CPC). We conducted multiomics analyses of 20 CPT patients including CPP and CPC. Multiomics analysis included whole-genome sequencing, whole-transcriptome sequencing, and methylation sequencing. Mutually exclusive TP53 and EPHA7 point mutations, coupled with the amplification of chromosome 1, were exclusively identified in CPC. In contrast, amplification of chromosome 9 was specific to CPP. Differential gene expression analysis uncovered a significant overexpression of genes related to cell cycle regulation and epithelial-mesenchymal transition pathways in CPC compared to CPP. Overexpression of genes associated with tumor metastasis and progression was observed in the CPC subgroup with leptomeningeal dissemination. Furthermore, methylation profiling unveiled hypomethylation in major repeat regions, including long interspersed nuclear elements, short interspersed nuclear elements, long terminal repeats, and retrotransposons in CPC compared to CPP, implying that the loss of epigenetic silencing of transposable elements may play a role in tumorigenesis of CPC. Finally, the differential expression of AK1, regulated by both genomic and epigenomic factors, emerged as a potential contributing factor to the histological difference of CPP against CPC. Our results suggest pronounced genomic and epigenomic disparities between CPP and CPC, providing insights into the pathogenesis of CPT at the molecular level.
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Affiliation(s)
- Yeonsong Choi
- Department of Biomedical Engineering, College of Information and Biotechnology, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea
- Korean Genomics Center, UNIST, Ulsan, Republic of Korea
| | - Seung Ah Choi
- Division of Pediatric Neurosurgery, Seoul National University Children's Hospital, Seoul, Republic of Korea
- Department of Neurosurgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Eun Jung Koh
- Division of Pediatric Neurosurgery, Seoul National University Children's Hospital, Seoul, Republic of Korea
- Department of Neurosurgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ilsun Yun
- Department of Biomedical Engineering, College of Information and Biotechnology, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea
- Korean Genomics Center, UNIST, Ulsan, Republic of Korea
| | - Suhyun Park
- Department of Biomedical Engineering, College of Information and Biotechnology, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea
- Korean Genomics Center, UNIST, Ulsan, Republic of Korea
| | | | | | - Sangbeen Park
- Department of Biomedical Engineering, College of Information and Biotechnology, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea
| | - Donggeon Woo
- Department of Biomedical Engineering, College of Information and Biotechnology, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea
| | - Ji Hoon Phi
- Division of Pediatric Neurosurgery, Seoul National University Children's Hospital, Seoul, Republic of Korea
- Department of Neurosurgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
- Neuroscience Research Institute, Seoul National University Medical Research Center, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sung-Hye Park
- Department of Pathology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Dong-Seok Kim
- Department of Pediatric Neurosurgery, Severance Children's Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Se Hoon Kim
- Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jung Won Choi
- Department of Neurosurgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Ji Won Lee
- Department of Pediatrics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Tae-Young Jung
- Department of Neurosurgery, Chonnam National University Medical School and Hwasun Hospital, Hwasun, Republic of Korea
| | - Jong Bhak
- Department of Biomedical Engineering, College of Information and Biotechnology, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea
- Korean Genomics Center, UNIST, Ulsan, Republic of Korea
- Clinomics Inc., Ulsan, Republic of Korea
| | - Semin Lee
- Department of Biomedical Engineering, College of Information and Biotechnology, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea.
- Korean Genomics Center, UNIST, Ulsan, Republic of Korea.
| | - Seung-Ki Kim
- Division of Pediatric Neurosurgery, Seoul National University Children's Hospital, Seoul, Republic of Korea.
- Department of Neurosurgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea.
- Neuroscience Research Institute, Seoul National University Medical Research Center, Seoul National University College of Medicine, Seoul, Republic of Korea.
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7
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Salcedo A, Tarabichi M, Buchanan A, Espiritu SMG, Zhang H, Zhu K, Ou Yang TH, Leshchiner I, Anastassiou D, Guan Y, Jang GH, Mootor MFE, Haase K, Deshwar AG, Zou W, Umar I, Dentro S, Wintersinger JA, Chiotti K, Demeulemeester J, Jolly C, Sycza L, Ko M, Wedge DC, Morris QD, Ellrott K, Van Loo P, Boutros PC. Crowd-sourced benchmarking of single-sample tumor subclonal reconstruction. Nat Biotechnol 2024:10.1038/s41587-024-02250-y. [PMID: 38862616 DOI: 10.1038/s41587-024-02250-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 04/17/2024] [Indexed: 06/13/2024]
Abstract
Subclonal reconstruction algorithms use bulk DNA sequencing data to quantify parameters of tumor evolution, allowing an assessment of how cancers initiate, progress and respond to selective pressures. We launched the ICGC-TCGA (International Cancer Genome Consortium-The Cancer Genome Atlas) DREAM Somatic Mutation Calling Tumor Heterogeneity and Evolution Challenge to benchmark existing subclonal reconstruction algorithms. This 7-year community effort used cloud computing to benchmark 31 subclonal reconstruction algorithms on 51 simulated tumors. Algorithms were scored on seven independent tasks, leading to 12,061 total runs. Algorithm choice influenced performance substantially more than tumor features but purity-adjusted read depth, copy-number state and read mappability were associated with the performance of most algorithms on most tasks. No single algorithm was a top performer for all seven tasks and existing ensemble strategies were unable to outperform the best individual methods, highlighting a key research need. All containerized methods, evaluation code and datasets are available to support further assessment of the determinants of subclonal reconstruction accuracy and development of improved methods to understand tumor evolution.
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Affiliation(s)
- Adriana Salcedo
- 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 Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada.
| | - Maxime Tarabichi
- The Francis Crick Institute, London, UK.
- Wellcome Sanger Institute, Hinxton, UK.
- Institute for Interdisciplinary Research, Université Libre de Bruxelles, Brussels, Belgium.
| | - Alex Buchanan
- Oregon Health and Sciences University, Portland, OR, USA
| | | | - Hongjiu Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Kaiyi Zhu
- Department of Systems Biology, Columbia University, New York, NY, USA
- Center for Cancer Systems Therapeutics, Columbia University, New York, NY, USA
- Department of Electrical Engineering, Columbia University, New York, NY, USA
| | - Tai-Hsien Ou Yang
- Department of Systems Biology, Columbia University, New York, NY, USA
- Center for Cancer Systems Therapeutics, Columbia University, New York, NY, USA
- Department of Electrical Engineering, Columbia University, New York, NY, USA
| | | | - Dimitris Anastassiou
- Department of Systems Biology, Columbia University, New York, NY, USA
- Center for Cancer Systems Therapeutics, Columbia University, New York, NY, USA
- Department of Electrical Engineering, Columbia University, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Electronic Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | - Gun Ho Jang
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Mohammed F E Mootor
- 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
| | | | - Amit G Deshwar
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - William Zou
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Imaad Umar
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Stefan Dentro
- The Francis Crick Institute, London, UK
- Wellcome Sanger Institute, Hinxton, UK
| | - Jeff A Wintersinger
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Kami Chiotti
- Oregon Health and Sciences University, Portland, OR, USA
| | - Jonas Demeulemeester
- The Francis Crick Institute, London, UK
- VIB Center for Cancer Biology, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | | | - Lesia Sycza
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Minjeong Ko
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - David C Wedge
- Big Data Institute, University of Oxford, Oxford, UK
- Manchester Cancer Research Center, University of Manchester, Manchester, UK
| | - Quaid D Morris
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kyle Ellrott
- Oregon Health and Sciences University, Portland, OR, USA.
| | - Peter Van Loo
- The Francis Crick Institute, London, UK.
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - 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 Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada.
- Department of Urology, University of California, Los Angeles, CA, USA.
- Broad Stem Cell Research Center, University of California, Los Angeles, CA, USA.
- California NanoSystems Institute, University of California, Los Angeles, CA, USA.
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8
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Mösch A, Grazioli F, Machart P, Malone B. NeoAgDT: optimization of personal neoantigen vaccine composition by digital twin simulation of a cancer cell population. Bioinformatics 2024; 40:btae205. [PMID: 38614133 PMCID: PMC11076149 DOI: 10.1093/bioinformatics/btae205] [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: 07/07/2023] [Revised: 03/28/2024] [Accepted: 04/11/2024] [Indexed: 04/15/2024] Open
Abstract
MOTIVATION Neoantigen vaccines make use of tumor-specific mutations to enable the patient's immune system to recognize and eliminate cancer. Selecting vaccine elements, however, is a complex task which needs to take into account not only the underlying antigen presentation pathway but also tumor heterogeneity. RESULTS Here, we present NeoAgDT, a two-step approach consisting of: (i) simulating individual cancer cells to create a digital twin of the patient's tumor cell population and (ii) optimizing the vaccine composition by integer linear programming based on this digital twin. NeoAgDT shows improved selection of experimentally validated neoantigens over ranking-based approaches in a study of seven patients. AVAILABILITY AND IMPLEMENTATION The NeoAgDT code is published on Github: https://github.com/nec-research/neoagdt.
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Affiliation(s)
- Anja Mösch
- Biomedical AI Group, NEC Laboratories Europe GmbH, Heidelberg 69115, Germany
| | - Filippo Grazioli
- Biomedical AI Group, NEC Laboratories Europe GmbH, Heidelberg 69115, Germany
| | - Pierre Machart
- Biomedical AI Group, NEC Laboratories Europe GmbH, Heidelberg 69115, Germany
| | - Brandon Malone
- Biomedical AI Group, NEC Laboratories Europe GmbH, Heidelberg 69115, Germany
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9
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Lai J, Liu Y, Scharpf RB, Karchin R. Evaluation of simulation methods for tumor subclonal reconstruction. ARXIV 2024:arXiv:2402.09599v1. [PMID: 38410652 PMCID: PMC10896360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Most neoplastic tumors originate from a single cell, and their evolution can be genetically traced through lineages characterized by common alterations such as small somatic mutations (SSMs), copy number alterations (CNAs), structural variants (SVs), and aneuploidies. Due to the complexity of these alterations in most tumors and the errors introduced by sequencing protocols and calling algorithms, tumor subclonal reconstruction algorithms are necessary to recapitulate the DNA sequence composition and tumor evolution in silico. With a growing number of these algorithms available, there is a pressing need for consistent and comprehensive benchmarking, which relies on realistic tumor sequencing generated by simulation tools. Here, we examine the current simulation methods, identifying their strengths and weaknesses, and provide recommendations for their improvement. Our review also explores potential new directions for research in this area. This work aims to serve as a resource for understanding and enhancing tumor genomic simulations, contributing to the advancement of the field.
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Affiliation(s)
- Jiaying Lai
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD
| | - Yunzhou Liu
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD
| | - Robert B. Scharpf
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD
| | - Rachel Karchin
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
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10
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Addala V, Newell F, Pearson JV, Redwood A, Robinson BW, Creaney J, Waddell N. Computational immunogenomic approaches to predict response to cancer immunotherapies. Nat Rev Clin Oncol 2024; 21:28-46. [PMID: 37907723 DOI: 10.1038/s41571-023-00830-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/03/2023] [Indexed: 11/02/2023]
Abstract
Cancer immunogenomics is an emerging field that bridges genomics and immunology. The establishment of large-scale genomic collaborative efforts along with the development of new single-cell transcriptomic techniques and multi-omics approaches have enabled characterization of the mutational and transcriptional profiles of many cancer types and helped to identify clinically actionable alterations as well as predictive and prognostic biomarkers. Researchers have developed computational approaches and machine learning algorithms to accurately obtain clinically useful information from genomic and transcriptomic sequencing data from bulk tissue or single cells and explore tumours and their microenvironment. The rapid growth in sequencing and computational approaches has resulted in the unmet need to understand their true potential and limitations in enabling improvements in the management of patients with cancer who are receiving immunotherapies. In this Review, we describe the computational approaches currently available to analyse bulk tissue and single-cell sequencing data from cancer, stromal and immune cells, as well as how best to select the most appropriate tool to address various clinical questions and, ultimately, improve patient outcomes.
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Affiliation(s)
- Venkateswar Addala
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
- Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.
| | - Felicity Newell
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - John V Pearson
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Alec Redwood
- National Centre for Asbestos Related Diseases, University of Western Australia, Perth, Western Australia, Australia
- Institute of Respiratory Health, Perth, Western Australia, Australia
- School of Biomedical Science, University of Western Australia, Perth, Western Australia, Australia
| | - Bruce W Robinson
- National Centre for Asbestos Related Diseases, University of Western Australia, Perth, Western Australia, Australia
- Institute of Respiratory Health, Perth, Western Australia, Australia
- Department of Respiratory Medicine, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia
- Medical School, University of Western Australia, Perth, Western Australia, Australia
| | - Jenette Creaney
- National Centre for Asbestos Related Diseases, University of Western Australia, Perth, Western Australia, Australia
- Institute of Respiratory Health, Perth, Western Australia, Australia
- School of Biomedical Science, University of Western Australia, Perth, Western Australia, Australia
- Department of Respiratory Medicine, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia
| | - Nicola Waddell
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
- Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.
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11
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Cheng X, Li X, Kang Y, Zhang D, Yu Q, Chen J, Li X, Du L, Yang T, Gong Y, Yi M, Zhang S, Zhu S, Ding S, Cheng W. Rapid in situ RNA imaging based on Cas12a thrusting strand displacement reaction. Nucleic Acids Res 2023; 51:e111. [PMID: 37941139 PMCID: PMC10711451 DOI: 10.1093/nar/gkad953] [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: 05/30/2023] [Revised: 10/09/2023] [Accepted: 10/12/2023] [Indexed: 11/10/2023] Open
Abstract
RNA In situ imaging through DNA self-assembly is advantaged in illustrating its structures and functions with high-resolution, while the limited reaction efficiency and time-consuming operation hinder its clinical application. Here, we first proposed a new strand displacement reaction (SDR) model (Cas12a thrusting SDR, CtSDR), in which Cas12a could overcome the inherent reaction limitation and dramatically enhance efficiency through energy replenishment and by-product consumption. The target-initiated CtSDR amplification was established for RNA analysis, with order of magnitude lower limit of detection (LOD) than the Cas13a system. The CtSDR-based RNA in situ imaging strategy was developed to monitor intra-cellular microRNA expression change and delineate the landscape of oncogenic RNA in 66 clinic tissue samples, possessing a clear advantage over classic in situ hybridization (ISH) in terms of operation time (1 h versus 14 h) while showing comparable sensitivity and specificity. This work presents a promising approach to developing advanced molecular diagnostic tools.
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Affiliation(s)
- Xiaoxue Cheng
- The Center for Clinical Molecular Medical detection, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, P.R. China
- Biobank Center, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, P.R. China
| | - Xiaosong Li
- The Center for Clinical Molecular Medical detection, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, P.R. China
| | - Yuexi Kang
- The Center for Clinical Molecular Medical detection, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, P.R. China
| | - Decai Zhang
- Laboratory Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510000, PR China
| | - Qiubo Yu
- Molecular Medicine Diagnostic and Testing Center, Chongqing Medical University, Chongqing 400016, P.R. China
| | - Junman Chen
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, P.R. China
| | - Xinyu Li
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, P.R. China
| | - Li Du
- The Center for Clinical Molecular Medical detection, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, P.R. China
| | - Tiantian Yang
- The Center for Clinical Molecular Medical detection, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, P.R. China
| | - Yao Gong
- The Center for Clinical Molecular Medical detection, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, P.R. China
| | - Ming Yi
- The Center for Clinical Molecular Medical detection, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, P.R. China
| | - Songzhi Zhang
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, P.R. China
| | - Shasha Zhu
- The Center for Clinical Molecular Medical detection, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, P.R. China
| | - Shijia Ding
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, P.R. China
| | - Wei Cheng
- The Center for Clinical Molecular Medical detection, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, P.R. China
- Biobank Center, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, P.R. China
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12
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Huzar J, Shenoy M, Sanderford MD, Kumar S, Miura S. Bootstrap confidence for molecular evolutionary estimates from tumor bulk sequencing data. FRONTIERS IN BIOINFORMATICS 2023; 3:1090730. [PMID: 37261293 PMCID: PMC10228696 DOI: 10.3389/fbinf.2023.1090730] [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: 11/05/2022] [Accepted: 03/28/2023] [Indexed: 06/02/2023] Open
Abstract
Bulk sequencing is commonly used to characterize the genetic diversity of cancer cell populations in tumors and the evolutionary relationships of cancer clones. However, bulk sequencing produces aggregate information on nucleotide variants and their sample frequencies, necessitating computational methods to predict distinct clone sequences and their frequencies within a sample. Interestingly, no methods are available to measure the statistical confidence in the variants assigned to inferred clones. We introduce a bootstrap resampling approach that combines clone prediction and statistical confidence calculation for every variant assignment. Analysis of computer-simulated datasets showed the bootstrap approach to work well in assessing the reliability of predicted clones as well downstream inferences using the predicted clones (e.g., mapping metastatic migration paths). We found that only a fraction of inferences have good bootstrap support, which means that many inferences are tentative for real data. Using the bootstrap approach, we analyzed empirical datasets from metastatic cancers and placed bootstrap confidence on the estimated number of mutations involved in cell migration events. We found that the numbers of driver mutations involved in metastatic cell migration events sourced from primary tumors are similar to those where metastatic tumors are the source of new metastases. So, mutations with driver potential seem to keep arising during metastasis. The bootstrap approach developed in this study is implemented in software available at https://github.com/SayakaMiura/CloneFinderPlus.
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Affiliation(s)
- Jared Huzar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, United States
| | - Madelyn Shenoy
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, United States
| | - Maxwell D Sanderford
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, United States
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, United States
- Department of Biology, Temple University, Philadelphia, PA, United States
- Center for Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Sayaka Miura
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, United States
- Department of Biology, Temple University, Philadelphia, PA, United States
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13
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Ma S, Wu J, Liu Z, He R, Wang Y, Liu L, Wang T, Wang W. Quantitative characterization of cell physiological state based on dynamical cell mechanics for drug efficacy indication. J Pharm Anal 2023; 13:388-402. [PMID: 37181289 PMCID: PMC10173291 DOI: 10.1016/j.jpha.2023.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 03/06/2023] [Accepted: 03/07/2023] [Indexed: 03/14/2023] Open
Abstract
Cell mechanics is essential to cell development and function, and its dynamics evolution reflects the physiological state of cells. Here, we investigate the dynamical mechanical properties of single cells under various drug conditions, and present two mathematical approaches to quantitatively characterizing the cell physiological state. It is demonstrated that the cellular mechanical properties upon the drug action increase over time and tend to saturate, and can be mathematically characterized by a linear time-invariant dynamical model. It is shown that the transition matrices of dynamical cell systems significantly improve the classification accuracies of the cells under different drug actions. Furthermore, it is revealed that there exists a positive linear correlation between the cytoskeleton density and the cellular mechanical properties, and the physiological state of a cell in terms of its cytoskeleton density can be predicted from its mechanical properties by a linear regression model. This study builds a relationship between the cellular mechanical properties and the cellular physiological state, adding information for evaluating drug efficacy.
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14
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Clonal reconstruction from co-occurrence of vector integration sites accurately quantifies expanding clones in vivo. Nat Commun 2022; 13:3712. [PMID: 35764632 PMCID: PMC9240075 DOI: 10.1038/s41467-022-31292-6] [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: 02/07/2022] [Accepted: 06/13/2022] [Indexed: 11/08/2022] Open
Abstract
High transduction rates of viral vectors in gene therapies (GT) and experimental hematopoiesis ensure a high frequency of gene delivery, although multiple integration events can occur in the same cell. Therefore, tracing of integration sites (IS) leads to mis-quantification of the true clonal spectrum and limits safety considerations in GT. Hence, we use correlations between repeated measurements of IS abundances to estimate their mutual similarity and identify clusters of co-occurring IS, for which we assume a clonal origin. We evaluate the performance, robustness and specificity of our methodology using clonal simulations. The reconstruction methods, implemented and provided as an R-package, are further applied to experimental clonal mixes and preclinical models of hematopoietic GT. Our results demonstrate that clonal reconstruction from IS data allows to overcome systematic biases in the clonal quantification as an essential prerequisite for the assessment of safety and long-term efficacy of GT involving integrative vectors. High transduction rates of viral vectors ensure good gene delivery; however multiple integration events can occur in the same cell. Here the authors use correlations between repeated measurements of integration site abundances to estimate their mutual similarity and identify clusters of co-occurring sites.
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15
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Ouellette TW, Awadalla P. Inferring ongoing cancer evolution from single tumour biopsies using synthetic supervised learning. PLoS Comput Biol 2022; 18:e1010007. [PMID: 35482653 PMCID: PMC9049314 DOI: 10.1371/journal.pcbi.1010007] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 03/09/2022] [Indexed: 11/18/2022] Open
Abstract
Variant allele frequencies (VAF) encode ongoing evolution and subclonal selection in growing tumours. However, existing methods that utilize VAF information for cancer evolutionary inference are compressive, slow, or incorrectly specify the underlying cancer evolutionary dynamics. Here, we provide a proof-of-principle synthetic supervised learning method, TumE, that integrates simulated models of cancer evolution with Bayesian neural networks, to infer ongoing selection in bulk-sequenced single tumour biopsies. Analyses in synthetic and patient tumours show that TumE significantly improves both accuracy and inference time per sample when detecting positive selection, deconvoluting selected subclonal populations, and estimating subclone frequency. Importantly, we show how transfer learning can leverage stored knowledge within TumE models for related evolutionary inference tasks-substantially reducing data and computational time for further model development and providing a library of recyclable deep learning models for the cancer evolution community. This extensible framework provides a foundation and future directions for harnessing progressive computational methods for the benefit of cancer genomics and, in turn, the cancer patient.
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Affiliation(s)
- Tom W. Ouellette
- Ontario Institute for Cancer Research, Department of Computational Biology, Toronto, Ontario, Canada
- Department of Molecular Genetics, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Philip Awadalla
- Ontario Institute for Cancer Research, Department of Computational Biology, Toronto, Ontario, Canada
- Department of Molecular Genetics, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
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16
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Borden ES, Ghafoor S, Buetow KH, LaFleur BJ, Wilson MA, Hastings KT. NeoScore Integrates Characteristics of the Neoantigen:MHC Class I Interaction and Expression to Accurately Prioritize Immunogenic Neoantigens. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2022; 208:1813-1827. [PMID: 35304420 DOI: 10.4049/jimmunol.2100700] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 01/28/2022] [Indexed: 12/20/2022]
Abstract
Accurate prioritization of immunogenic neoantigens is key to developing personalized cancer vaccines and distinguishing those patients likely to respond to immune checkpoint inhibition. However, there is no consensus regarding which characteristics best predict neoantigen immunogenicity, and no model to date has both high sensitivity and specificity and a significant association with survival in response to immunotherapy. We address these challenges in the prioritization of immunogenic neoantigens by (1) identifying which neoantigen characteristics best predict immunogenicity; (2) integrating these characteristics into an immunogenicity score, the NeoScore; and (3) demonstrating a significant association of the NeoScore with survival in response to immune checkpoint inhibition. One thousand random and evenly split combinations of immunogenic and nonimmunogenic neoantigens from a validated dataset were analyzed using a regularized regression model for characteristic selection. The selected characteristics, the dissociation constant and binding stability of the neoantigen:MHC class I complex and expression of the mutated gene in the tumor, were integrated into the NeoScore. A web application is provided for calculation of the NeoScore. The NeoScore results in improved, or equivalent, performance in four test datasets as measured by sensitivity, specificity, and area under the receiver operator characteristics curve compared with previous models. Among cutaneous melanoma patients treated with immune checkpoint inhibition, a high maximum NeoScore was associated with improved survival. Overall, the NeoScore has the potential to improve neoantigen prioritization for the development of personalized vaccines and contribute to the determination of which patients are likely to respond to immunotherapy.
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Affiliation(s)
- Elizabeth S Borden
- Department of Basic Medical Sciences, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ.,Phoenix Veterans Affairs Health Care System, Phoenix, AZ
| | - Suhail Ghafoor
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ
| | - Kenneth H Buetow
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ.,School of Life Sciences, Arizona State University, Tempe, AZ; and
| | | | - Melissa A Wilson
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ.,School of Life Sciences, Arizona State University, Tempe, AZ; and
| | - K Taraszka Hastings
- Department of Basic Medical Sciences, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ; .,Phoenix Veterans Affairs Health Care System, Phoenix, AZ
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17
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Hui S, Nielsen R. SCONCE: a method for profiling copy number alterations in cancer evolution using single-cell whole genome sequencing. Bioinformatics 2022; 38:1801-1808. [PMID: 35080614 PMCID: PMC8963318 DOI: 10.1093/bioinformatics/btac041] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 12/23/2021] [Accepted: 01/24/2022] [Indexed: 02/04/2023] Open
Abstract
MOTIVATION Copy number alterations (CNAs) are a significant driver in cancer growth and development, but remain poorly characterized on the single cell level. Although genome evolution in cancer cells is Markovian through evolutionary time, CNAs are not Markovian along the genome. However, existing methods call copy number profiles with Hidden Markov Models or change point detection algorithms based on changes in observed read depth, corrected by genome content and do not account for the stochastic evolutionary process. RESULTS We present a theoretical framework to use tumor evolutionary history to accurately call CNAs in a principled manner. To model the tumor evolutionary process and account for technical noise from low coverage single-cell whole genome sequencing data, we developed SCONCE, a method based on a Hidden Markov Model to analyze read depth data from tumor cells using matched normal cells as negative controls. Using a combination of public data sets and simulations, we show SCONCE accurately decodes copy number profiles, and provides a useful tool for understanding tumor evolution. AVAILABILITYAND IMPLEMENTATION SCONCE is implemented in C++11 and is freely available from https://github.com/NielsenBerkeleyLab/sconce. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sandra Hui
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Rasmus Nielsen
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720, USA
- Department of Statistics, University of California, Berkeley, Berkeley, CA 94720, USA
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18
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Borden ES, Buetow KH, Wilson MA, Hastings KT. Cancer Neoantigens: Challenges and Future Directions for Prediction, Prioritization, and Validation. Front Oncol 2022; 12:836821. [PMID: 35311072 PMCID: PMC8929516 DOI: 10.3389/fonc.2022.836821] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 02/07/2022] [Indexed: 12/16/2022] Open
Abstract
Prioritization of immunogenic neoantigens is key to enhancing cancer immunotherapy through the development of personalized vaccines, adoptive T cell therapy, and the prediction of response to immune checkpoint inhibition. Neoantigens are tumor-specific proteins that allow the immune system to recognize and destroy a tumor. Cancer immunotherapies, such as personalized cancer vaccines, adoptive T cell therapy, and immune checkpoint inhibition, rely on an understanding of the patient-specific neoantigen profile in order to guide personalized therapeutic strategies. Genomic approaches to predicting and prioritizing immunogenic neoantigens are rapidly expanding, raising new opportunities to advance these tools and enhance their clinical relevance. Predicting neoantigens requires acquisition of high-quality samples and sequencing data, followed by variant calling and variant annotation. Subsequently, prioritizing which of these neoantigens may elicit a tumor-specific immune response requires application and integration of tools to predict the expression, processing, binding, and recognition potentials of the neoantigen. Finally, improvement of the computational tools is held in constant tension with the availability of datasets with validated immunogenic neoantigens. The goal of this review article is to summarize the current knowledge and limitations in neoantigen prediction, prioritization, and validation and propose future directions that will improve personalized cancer treatment.
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Affiliation(s)
- Elizabeth S Borden
- Department of Basic Medical Sciences, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ, United States.,Department of Research and Internal Medicine (Dermatology), Phoenix Veterans Affairs Health Care System, Phoenix, AZ, United States
| | - Kenneth H Buetow
- School of Life Sciences, Arizona State University, Tempe, AZ, United States.,Center for Evolution and Medicine, Arizona State University, Tempe, AZ, United States
| | - Melissa A Wilson
- School of Life Sciences, Arizona State University, Tempe, AZ, United States.,Center for Evolution and Medicine, Arizona State University, Tempe, AZ, United States
| | - Karen Taraszka Hastings
- Department of Basic Medical Sciences, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ, United States.,Department of Research and Internal Medicine (Dermatology), Phoenix Veterans Affairs Health Care System, Phoenix, AZ, United States
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19
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Laganà A. Computational Approaches for the Investigation of Intra-tumor Heterogeneity and Clonal Evolution from Bulk Sequencing Data in Precision Oncology Applications. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1361:101-118. [DOI: 10.1007/978-3-030-91836-1_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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20
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Benchmarking pipelines for subclonal deconvolution of bulk tumour sequencing data. Nat Commun 2021; 12:6396. [PMID: 34737285 PMCID: PMC8569188 DOI: 10.1038/s41467-021-26698-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 10/20/2021] [Indexed: 11/09/2022] Open
Abstract
Intratumour heterogeneity provides tumours with the ability to adapt and acquire treatment resistance. The development of more effective and personalised treatments for cancers, therefore, requires accurate characterisation of the clonal architecture of tumours, enabling evolutionary dynamics to be tracked. Many methods exist for achieving this from bulk tumour sequencing data, involving identifying mutations and performing subclonal deconvolution, but there is a lack of systematic benchmarking to inform researchers on which are most accurate, and how dataset characteristics impact performance. To address this, we use the most comprehensive tumour genome simulation tool available for such purposes to create 80 bulk tumour whole exome sequencing datasets of differing depths, tumour complexities, and purities, and use these to benchmark subclonal deconvolution pipelines. We conclude that i) tumour complexity does not impact accuracy, ii) increasing either purity or purity-corrected sequencing depth improves accuracy, and iii) the optimal pipeline consists of Mutect2, FACETS and PyClone-VI. We have made our benchmarking datasets publicly available for future use.
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21
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Satas G, Zaccaria S, El-Kebir M, Raphael BJ. DeCiFering the elusive cancer cell fraction in tumor heterogeneity and evolution. Cell Syst 2021; 12:1004-1018.e10. [PMID: 34416171 DOI: 10.1016/j.cels.2021.07.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 07/09/2021] [Accepted: 07/21/2021] [Indexed: 12/22/2022]
Abstract
The cancer cell fraction (CCF), or proportion of cancerous cells in a tumor containing a single-nucleotide variant (SNV), is a fundamental statistic used to quantify tumor heterogeneity and evolution. Existing CCF estimation methods from bulk DNA sequencing data assume that every cell with an SNV contains the same number of copies of the SNV. This assumption is unrealistic in tumors with copy-number aberrations that alter SNV multiplicities. Furthermore, the CCF does not account for SNV losses due to copy-number aberrations, confounding downstream phylogenetic analyses. We introduce DeCiFer, an algorithm that overcomes these limitations by clustering SNVs using a novel statistic, the descendant cell fraction (DCF). The DCF quantifies both the prevalence of an SNV at the present time and its past evolutionary history using an evolutionary model that allows mutation losses. We show that DeCiFer yields more parsimonious reconstructions of tumor evolution than previously reported for 49 prostate cancer samples.
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Affiliation(s)
- Gryte Satas
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Simone Zaccaria
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Mohammed El-Kebir
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, USA.
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22
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Krasnov GS, Ghukasyan LG, Abramov IS, Nasedkina TV. Determination of the Subclonal Tumor Structure in Childhood Acute Myeloid Leukemia and Acral Melanoma by Next-Generation Sequencing. Mol Biol 2021. [DOI: 10.1134/s0026893321040051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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23
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Sefik E, Qu R, Kaffe E, Zhao J, Junqueira C, Mirza H, Brewer R, Han A, Steach H, Israelow B, Chen YG, Halene S, Iwasaki A, Meffre E, Nussenzweig M, Lieberman J, Wilen CB, Kluger Y, Flavell RA. Viral replication in human macrophages enhances an inflammatory cascade and interferon driven chronic COVID-19 in humanized mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021. [PMID: 34611663 DOI: 10.1101/2021.09.27.461948] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Chronic COVID-19 is characterized by persistent viral RNA and sustained interferon (IFN) response which is recapitulated and required for pathology in SARS-CoV-2 infected MISTRG6-hACE2 humanized mice. As in the human disease, monocytes, and macrophages in SARS-CoV-2 infected MISTRG6-hACE2 are central to disease pathology. Here, we describe SARS-CoV-2 uptake in tissue resident human macrophages that is enhanced by virus specific antibodies. SARS-CoV-2 replicates in these human macrophages as evidenced by detection of double-stranded RNA, subgenomic viral RNA and expression of a virally encoded fluorescent reporter gene; and it is inhibited by Remdesivir, an inhibitor of viral replication. Although early IFN deficiency leads to enhanced disease, blocking either viral replication with Remdesivir or the downstream IFN stimulated cascade by injecting anti-IFNAR2 in vivo in the chronic stages of disease attenuates many aspects of the overactive immune-inflammatory response, especially the inflammatory macrophage response, and most consequentially, the chronic disease itself.
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24
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Dasari K, Somarelli JA, Kumar S, Townsend JP. The somatic molecular evolution of cancer: Mutation, selection, and epistasis. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2021; 165:56-65. [PMID: 34364910 DOI: 10.1016/j.pbiomolbio.2021.08.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 07/30/2021] [Accepted: 08/03/2021] [Indexed: 12/17/2022]
Abstract
Cancer progression has been attributed to somatic changes in single-nucleotide variants, copy-number aberrations, loss of heterozygosity, chromosomal instability, epistatic interactions, and the tumor microenvironment. It is not entirely clear which of these changes are essential and which are ancillary to cancer. The dynamic nature of cancer evolution in a patient can be illuminated using several concepts and tools from classical evolutionary biology. Neutral mutation rates in cancer cells are calculable from genomic data such as synonymous mutations, and selective pressures are calculable from rates of fixation occurring beyond the expectation by neutral mutation and drift. However, these cancer effect sizes of mutations are complicated by epistatic interactions that can determine the likely sequence of gene mutations. In turn, longitudinal phylogenetic analyses of somatic cancer progression offer an opportunity to identify key moments in cancer evolution, relating the timing of driver mutations to corresponding landmarks in the clinical timeline. These analyses reveal temporal aspects of genetic and phenotypic change during tumorigenesis and across clinical timescales. Using a related framework, clonal deconvolution, physical locations of clones, and their phylogenetic relations can be used to infer tumor migration histories. Additionally, genetic interactions with the tumor microenvironment can be analyzed with longstanding approaches applied to organismal genotype-by-environment interactions. Fitness landscapes for cancer evolution relating to genotype, phenotype, and environment could enable more accurate, personalized therapeutic strategies. An understanding of the trajectories underlying the evolution of neoplasms, primary, and metastatic tumors promises fundamental advances toward accurate and personalized predictions of therapeutic response.
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Affiliation(s)
| | | | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, and Department of Biology, Temple University, Philadelphia, PA, 19122, USA
| | - Jeffrey P Townsend
- Yale College, New Haven, CT, USA; Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA; Yale Cancer Center, Yale University, New Haven, CT, USA; Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
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25
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Montemurro M, Grassi E, Pizzino CG, Bertotti A, Ficarra E, Urgese G. PhyliCS: a Python library to explore scCNA data and quantify spatial tumor heterogeneity. BMC Bioinformatics 2021; 22:360. [PMID: 34217219 PMCID: PMC8254361 DOI: 10.1186/s12859-021-04277-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 06/21/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Tumors are composed by a number of cancer cell subpopulations (subclones), characterized by a distinguishable set of mutations. This phenomenon, known as intra-tumor heterogeneity (ITH), may be studied using Copy Number Aberrations (CNAs). Nowadays ITH can be assessed at the highest possible resolution using single-cell DNA (scDNA) sequencing technology. Additionally, single-cell CNA (scCNA) profiles from multiple samples of the same tumor can in principle be exploited to study the spatial distribution of subclones within a tumor mass. However, since the technology required to generate large scDNA sequencing datasets is relatively recent, dedicated analytical approaches are still lacking. RESULTS We present PhyliCS, the first tool which exploits scCNA data from multiple samples from the same tumor to estimate whether the different clones of a tumor are well mixed or spatially separated. Starting from the CNA data produced with third party instruments, it computes a score, the Spatial Heterogeneity score, aimed at distinguishing spatially intermixed cell populations from spatially segregated ones. Additionally, it provides functionalities to facilitate scDNA analysis, such as feature selection and dimensionality reduction methods, visualization tools and a flexible clustering module. CONCLUSIONS PhyliCS represents a valuable instrument to explore the extent of spatial heterogeneity in multi-regional tumour sampling, exploiting the potential of scCNA data.
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Affiliation(s)
- Marilisa Montemurro
- Department of Control and Computer Science, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129, Turin, Italy.
| | - Elena Grassi
- Department of Oncology, University of Torino, Strada Provinciale, 142 - KM 3.95, 10060, Candiolo, Turin, Italy.,Candiolo Cancer Institute - FPO IRCCS, Strada Provinciale, 142 - KM 3.95, 10060, Candiolo, TO, Italy
| | - Carmelo Gabriele Pizzino
- Department of Oncology, University of Torino, Strada Provinciale, 142 - KM 3.95, 10060, Candiolo, Turin, Italy.,Candiolo Cancer Institute - FPO IRCCS, Strada Provinciale, 142 - KM 3.95, 10060, Candiolo, TO, Italy
| | - Andrea Bertotti
- Department of Oncology, University of Torino, Strada Provinciale, 142 - KM 3.95, 10060, Candiolo, Turin, Italy.,Candiolo Cancer Institute - FPO IRCCS, Strada Provinciale, 142 - KM 3.95, 10060, Candiolo, TO, Italy
| | - Elisa Ficarra
- Enzo Ferrari Engineering Dept, University of Modena and Reggio Emilia, Via Vivarelli 10/1, 41125, Modena, Italy
| | - Gianvito Urgese
- Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129, Turin, Italy
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