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Pu T, Peddle A, Zhu J, Tejpar S, Verbandt S. Neoantigen identification: Technological advances and challenges. Methods Cell Biol 2023; 183:265-302. [PMID: 38548414 DOI: 10.1016/bs.mcb.2023.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
Neoantigens have emerged as promising targets for cutting-edge immunotherapies, such as cancer vaccines and adoptive cell therapy. These neoantigens are unique to tumors and arise exclusively from somatic mutations or non-genomic aberrations in tumor proteins. They encompass a wide range of alterations, including genomic mutations, post-transcriptomic variants, and viral oncoproteins. With the advancements in technology, the identification of immunogenic neoantigens has seen rapid progress, raising new opportunities for enhancing their clinical significance. Prediction of neoantigens necessitates the acquisition of high-quality samples and sequencing data, followed by mutation calling. Subsequently, the pipeline involves integrating various tools that can predict the expression, processing, binding, and recognition potential of neoantigens. However, the continuous improvement of computational tools is constrained by the availability of datasets which contain validated immunogenic neoantigens. This review article aims to provide a comprehensive summary of the current knowledge as well as limitations in neoantigen prediction and validation. Additionally, it delves into the origin and biological role of neoantigens, offering a deeper understanding of their significance in the field of cancer immunotherapy. This article thus seeks to contribute to the ongoing efforts to harness neoantigens as powerful weapons in the fight against cancer.
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Affiliation(s)
- Ting Pu
- Digestive Oncology Unit, KULeuven, Leuven, Belgium
| | | | - Jingjing Zhu
- de Duve Institute, Université catholique de Louvain, Brussels, Belgium
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2
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Patterson A, Elbasir A, Tian B, Auslander N. Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications. Cancers (Basel) 2023; 15:cancers15071958. [PMID: 37046619 PMCID: PMC10093138 DOI: 10.3390/cancers15071958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 02/24/2023] [Accepted: 03/09/2023] [Indexed: 03/29/2023] Open
Abstract
Since the rise of next-generation sequencing technologies, the catalogue of mutations in cancer has been continuously expanding. To address the complexity of the cancer-genomic landscape and extract meaningful insights, numerous computational approaches have been developed over the last two decades. In this review, we survey the current leading computational methods to derive intricate mutational patterns in the context of clinical relevance. We begin with mutation signatures, explaining first how mutation signatures were developed and then examining the utility of studies using mutation signatures to correlate environmental effects on the cancer genome. Next, we examine current clinical research that employs mutation signatures and discuss the potential use cases and challenges of mutation signatures in clinical decision-making. We then examine computational studies developing tools to investigate complex patterns of mutations beyond the context of mutational signatures. We survey methods to identify cancer-driver genes, from single-driver studies to pathway and network analyses. In addition, we review methods inferring complex combinations of mutations for clinical tasks and using mutations integrated with multi-omics data to better predict cancer phenotypes. We examine the use of these tools for either discovery or prediction, including prediction of tumor origin, treatment outcomes, prognosis, and cancer typing. We further discuss the main limitations preventing widespread clinical integration of computational tools for the diagnosis and treatment of cancer. We end by proposing solutions to address these challenges using recent advances in machine learning.
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Affiliation(s)
- Andrew Patterson
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- The Wistar Institute, Philadelphia, PA 19104, USA
| | | | - Bin Tian
- The Wistar Institute, Philadelphia, PA 19104, USA
| | - Noam Auslander
- The Wistar Institute, Philadelphia, PA 19104, USA
- Department of Cancer Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Correspondence:
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3
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Paul D, Nedelcu AM. The underexplored links between cancer and the internal body climate: Implications for cancer prevention and treatment. Front Oncol 2022; 12:1040034. [PMID: 36620608 PMCID: PMC9815514 DOI: 10.3389/fonc.2022.1040034] [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: 09/08/2022] [Accepted: 11/25/2022] [Indexed: 12/24/2022] Open
Abstract
In order to effectively manage and cure cancer we should move beyond the general view of cancer as a random process of genetic alterations leading to uncontrolled cell proliferation or simply a predictable evolutionary process involving selection for traits that increase cell fitness. In our view, cancer is a systemic disease that involves multiple interactions not only among cells within tumors or between tumors and surrounding tissues but also with the entire organism and its internal "milieu". We define the internal body climate as an emergent property resulting from spatial and temporal interactions among internal components themselves and with the external environment. The body climate itself can either prevent, promote or support cancer initiation and progression (top-down effect; i.e., body climate-induced effects on cancer), as well as be perturbed by cancer (bottom-up effect; i.e., cancer-induced body climate changes) to further favor cancer progression and spread. This positive feedback loop can move the system towards a "cancerized" organism and ultimately results in its demise. In our view, cancer not only affects the entire system; it is a reflection of an imbalance of the entire system. This model provides an integrated framework to study all aspects of cancer as a systemic disease, and also highlights unexplored links that can be altered to both prevent body climate changes that favor cancer initiation, progression and dissemination as well as manipulate or restore the body internal climate to hinder the success of cancer inception, progression and metastasis or improve therapy outcomes. To do so, we need to (i) identify cancer-relevant factors that affect specific climate components, (ii) develop 'body climate biomarkers', (iii) define 'body climate scores', and (iv) develop strategies to prevent climate changes, stop or slow the changes, or even revert the changes (climate restoration).
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Affiliation(s)
- Doru Paul
- Weill Cornell Medicine, New York, NY, United States,*Correspondence: Doru Paul,
| | - Aurora M. Nedelcu
- Biology Department, University of New Brunswick, Fredericton, NB, Canada
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4
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Simsek H, Klotzsch E. The solid tumor microenvironment-Breaking the barrier for T cells: How the solid tumor microenvironment influences T cells: How the solid tumor microenvironment influences T cells. Bioessays 2022; 44:e2100285. [PMID: 35393714 DOI: 10.1002/bies.202100285] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 03/18/2022] [Accepted: 03/22/2022] [Indexed: 12/20/2022]
Abstract
The tumor microenvironment (TME) plays a pivotal role in the behavior and development of solid tumors as well as shaping the immune response against them. As the tumor cells proliferate, the space they occupy and their physical interactions with the surrounding tissue increases. The growing tumor tissue becomes a complex dynamic structure, containing connective tissue, vascular structures, and extracellular matrix (ECM) that facilitates stimulation, oxygenation, and nutrition, necessary for its fast growth. Mechanical cues such as stiffness, solid stress, interstitial fluid pressure (IFP), matrix density, and microarchitecture influence cellular functions and ultimately tumor progression and metastasis. In this fight, our body is equipped with T cells as its spearhead against tumors. However, the altered biochemical and mechanical environment of the tumor niche affects T cell efficacy and leads to their exhaustion. Understanding the mechanobiological properties of the TME and their effects on T cells is key for developing novel adoptive tumor immunotherapies.
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Affiliation(s)
- Hasan Simsek
- Institute for Biology, Experimental Biophysics/Mechanobiology, Humboldt University of Berlin, Berlin, Germany
| | - Enrico Klotzsch
- Institute for Biology, Experimental Biophysics/Mechanobiology, Humboldt University of Berlin, Berlin, Germany.,Laboratory of Applied Mechanobiology, Department for Health Sciences and Technology, ETH Zürich, Zürich, Switzerland
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5
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Sirven P, Faucheux L, Grandclaudon M, Michea P, Vincent-Salomon A, Mechta-Grigoriou F, Scholer-Dahirel A, Guillot-Delost M, Soumelis V. Definition of a novel breast tumor-specific classifier based on secretome analysis. Breast Cancer Res 2022; 24:94. [PMID: 36539890 PMCID: PMC9764559 DOI: 10.1186/s13058-022-01590-4] [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: 10/03/2022] [Accepted: 12/07/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND During cancer development, the normal tissue microenvironment is shaped by tumorigenic events. Inflammatory mediators and immune cells play a key role during this process. However, which molecular features most specifically characterize the malignant tissue remains poorly explored. METHODS Within our institutional tumor microenvironment global analysis (T-MEGA) program, we set a prospective cohort of 422 untreated breast cancer patients. We established a dedicated pipeline to generate supernatants from tumor and juxta-tumor tissue explants and quantify 55 soluble molecules using Luminex or MSD. Those analytes belonged to five molecular families: chemokines, cytokines, growth factors, metalloproteinases, and adipokines. RESULTS When looking at tissue specificity, our dataset revealed some breast tumor-specific characteristics, as IL-16, as well as some juxta-tumor-specific secreted molecules, as IL-33. Unsupervised clustering analysis identified groups of molecules that were specific to the breast tumor tissue and displayed a similar secretion behavior. We identified a tumor-specific cluster composed of nine molecules that were secreted fourteen times more in the tumor supernatants than the corresponding juxta-tumor supernatants. This cluster contained, among others, CCL17, CCL22, and CXCL9 and TGF-β1, 2, and 3. The systematic comparison of tumor and juxta-tumor secretome data allowed us to mathematically formalize a novel breast cancer signature composed of 14 molecules that segregated tumors from juxta-tumors, with a sensitivity of 96.8% and a specificity of 96%. CONCLUSIONS Our study provides the first breast tumor-specific classifier computed on breast tissue-derived secretome data. Moreover, our T-MEGA cohort dataset is a freely accessible resource to the biomedical community to help advancing scientific knowledge on breast cancer.
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Affiliation(s)
- Philémon Sirven
- grid.418596.70000 0004 0639 6384INSERM Unit U932, Immunity and Cancer, Institut Curie, Paris, France ,grid.440907.e0000 0004 1784 3645Paris Sciences Lettres (PSL) University, Paris, France ,Center of Clinical Investigation, CIC IGR-Curie 1428, Paris, France
| | - Lilith Faucheux
- INSERM U976, Université de Paris, IRSLHôpital Saint Louis, 75006 Paris, France ,INSERM UMR1153, Université de Paris, ECSTRRA Team, 75006 Paris, France
| | - Maximilien Grandclaudon
- grid.418596.70000 0004 0639 6384INSERM Unit U932, Immunity and Cancer, Institut Curie, Paris, France ,grid.440907.e0000 0004 1784 3645Paris Sciences Lettres (PSL) University, Paris, France ,Center of Clinical Investigation, CIC IGR-Curie 1428, Paris, France
| | - Paula Michea
- grid.418596.70000 0004 0639 6384INSERM Unit U932, Immunity and Cancer, Institut Curie, Paris, France ,grid.440907.e0000 0004 1784 3645Paris Sciences Lettres (PSL) University, Paris, France ,Center of Clinical Investigation, CIC IGR-Curie 1428, Paris, France
| | - Anne Vincent-Salomon
- grid.440907.e0000 0004 1784 3645Paris Sciences Lettres (PSL) University, Paris, France ,grid.418596.70000 0004 0639 6384Diagnostic and Theranostic Medicine Division, Institut Curie, Paris, France
| | - Fatima Mechta-Grigoriou
- grid.440907.e0000 0004 1784 3645Paris Sciences Lettres (PSL) University, Paris, France ,grid.418596.70000 0004 0639 6384Centre de Recherche, Stress and Cancer Laboratory, U830 Genetics and Biology of Cancers, INSERM, Institut Curie, Paris, France
| | - Alix Scholer-Dahirel
- grid.418596.70000 0004 0639 6384INSERM Unit U932, Immunity and Cancer, Institut Curie, Paris, France ,grid.440907.e0000 0004 1784 3645Paris Sciences Lettres (PSL) University, Paris, France ,Center of Clinical Investigation, CIC IGR-Curie 1428, Paris, France
| | - Maude Guillot-Delost
- grid.418596.70000 0004 0639 6384INSERM Unit U932, Immunity and Cancer, Institut Curie, Paris, France ,grid.440907.e0000 0004 1784 3645Paris Sciences Lettres (PSL) University, Paris, France ,Center of Clinical Investigation, CIC IGR-Curie 1428, Paris, France
| | - Vassili Soumelis
- grid.418596.70000 0004 0639 6384INSERM Unit U932, Immunity and Cancer, Institut Curie, Paris, France ,grid.440907.e0000 0004 1784 3645Paris Sciences Lettres (PSL) University, Paris, France ,Center of Clinical Investigation, CIC IGR-Curie 1428, Paris, France ,INSERM U976, Université de Paris, IRSLHôpital Saint Louis, 75006 Paris, France ,grid.413328.f0000 0001 2300 6614Department of Immunology-Histocompatibility, AP-HP, Hôpital Saint-Louis, 75010 Paris, France
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6
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Genetic and epigenetic processes linked to cancer. Cancer 2022. [DOI: 10.1016/b978-0-323-91904-3.00013-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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7
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Fortunato A, Mallo D, Rupp SM, King LM, Hardman T, Lo JY, Hall A, Marks JR, Hwang ES, Maley CC. A new method to accurately identify single nucleotide variants using small FFPE breast samples. Brief Bioinform 2021; 22:6296507. [PMID: 34117742 PMCID: PMC8574974 DOI: 10.1093/bib/bbab221] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 05/14/2021] [Accepted: 05/20/2021] [Indexed: 11/14/2022] Open
Abstract
Most tissue collections of neoplasms are composed of formalin-fixed and paraffin-embedded (FFPE) excised tumor samples used for routine diagnostics. DNA sequencing is becoming increasingly important in cancer research and clinical management; however it is difficult to accurately sequence DNA from FFPE samples. We developed and validated a new bioinformatic pipeline to use existing variant-calling strategies to robustly identify somatic single nucleotide variants (SNVs) from whole exome sequencing using small amounts of DNA extracted from archival FFPE samples of breast cancers. We optimized this strategy using 28 pairs of technical replicates. After optimization, the mean similarity between replicates increased 5-fold, reaching 88% (range 0-100%), with a mean of 21.4 SNVs (range 1-68) per sample, representing a markedly superior performance to existing tools. We found that the SNV-identification accuracy declined when there was less than 40 ng of DNA available and that insertion-deletion variant calls are less reliable than single base substitutions. As the first application of the new algorithm, we compared samples of ductal carcinoma in situ of the breast to their adjacent invasive ductal carcinoma samples. We observed an increased number of mutations (paired-samples sign test, P < 0.05), and a higher genetic divergence in the invasive samples (paired-samples sign test, P < 0.01). Our method provides a significant improvement in detecting SNVs in FFPE samples over previous approaches.
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Affiliation(s)
- Angelo Fortunato
- Arizona Cancer Evolution Center, Arizona State University, 1001 S. McAllister Ave., Tempe, AZ, 85287, USA.,Biodesign Center for Biocomputing, Security and Society, Arizona State University, 727 E. Tyler St., Tempe, AZ 85281 USA.,School of Life Sciences, Arizona State University, 427 East Tyler Mall, Tempe, AZ 85287, USA
| | - Diego Mallo
- Arizona Cancer Evolution Center, Arizona State University, 1001 S. McAllister Ave., Tempe, AZ, 85287, USA.,Biodesign Center for Biocomputing, Security and Society, Arizona State University, 727 E. Tyler St., Tempe, AZ 85281 USA.,School of Life Sciences, Arizona State University, 427 East Tyler Mall, Tempe, AZ 85287, USA
| | - Shawn M Rupp
- Arizona Cancer Evolution Center, Arizona State University, 1001 S. McAllister Ave., Tempe, AZ, 85287, USA.,Biodesign Center for Biocomputing, Security and Society, Arizona State University, 727 E. Tyler St., Tempe, AZ 85281 USA
| | | | | | - Joseph Y Lo
- Department of Radiology, Duke University, Durham, NC, USA
| | - Allison Hall
- Department of Pathology, Duke University, Durham, NC, USA
| | | | | | - Carlo C Maley
- Arizona Cancer Evolution Center, Arizona State University, 1001 S. McAllister Ave., Tempe, AZ, 85287, USA.,Biodesign Center for Biocomputing, Security and Society, Arizona State University, 727 E. Tyler St., Tempe, AZ 85281 USA.,School of Life Sciences, Arizona State University, 427 East Tyler Mall, Tempe, AZ 85287, USA
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8
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Klein MI, Cannataro VL, Townsend JP, Newman S, Stern DF, Zhao H. Identifying modules of cooperating cancer drivers. Mol Syst Biol 2021; 17:e9810. [PMID: 33769711 PMCID: PMC7995435 DOI: 10.15252/msb.20209810] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 01/20/2021] [Accepted: 01/26/2021] [Indexed: 12/22/2022] Open
Abstract
Identifying cooperating modules of driver alterations can provide insights into cancer etiology and advance the development of effective personalized treatments. We present Cancer Rule Set Optimization (CRSO) for inferring the combinations of alterations that cooperate to drive tumor formation in individual patients. Application to 19 TCGA cancer types revealed a mean of 11 core driver combinations per cancer, comprising 2-6 alterations per combination and accounting for a mean of 70% of samples per cancer type. CRSO is distinct from methods based on statistical co-occurrence, which we demonstrate is a suboptimal criterion for investigating driver cooperation. CRSO identified well-studied driver combinations that were not detected by other approaches and nominated novel combinations that correlate with clinical outcomes in multiple cancer types. Novel synergies were identified in NRAS-mutant melanomas that may be therapeutically relevant. Core driver combinations involving NFE2L2 mutations were identified in four cancer types, supporting the therapeutic potential of NRF2 pathway inhibition. CRSO is available at https://github.com/mikekleinsgit/CRSO/.
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Affiliation(s)
- Michael I Klein
- Program in Computational Biology and BioinformaticsYale UniversityNew HavenCTUSA
- Bioinformatics R&DSema4StamfordCTUSA
| | - Vincent L Cannataro
- Department of BiologyEmmanuel CollegeBostonMAUSA
- Department of BiostatisticsYale School of Public HealthNew HavenCTUSA
| | - Jeffrey P Townsend
- Program in Computational Biology and BioinformaticsYale UniversityNew HavenCTUSA
- Department of BiostatisticsYale School of Public HealthNew HavenCTUSA
- Yale Cancer CenterYale UniversityNew HavenCTUSA
| | | | - David F Stern
- Yale Cancer CenterYale UniversityNew HavenCTUSA
- Department of PathologyYale School of MedicineNew HavenCTUSA
| | - Hongyu Zhao
- Program in Computational Biology and BioinformaticsYale UniversityNew HavenCTUSA
- Department of BiostatisticsYale School of Public HealthNew HavenCTUSA
- Yale Cancer CenterYale UniversityNew HavenCTUSA
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9
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Rosello M, Vougny J, Czarny F, Mione MC, Concordet JP, Albadri S, Del Bene F. Precise base editing for the in vivo study of developmental signaling and human pathologies in zebrafish. eLife 2021; 10:65552. [PMID: 33576334 PMCID: PMC7932688 DOI: 10.7554/elife.65552] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 02/10/2021] [Indexed: 02/07/2023] Open
Abstract
While zebrafish is emerging as a new model system to study human diseases, an efficient methodology to generate precise point mutations at high efficiency is still lacking. Here we show that base editors can generate C-to-T point mutations with high efficiencies without other unwanted on-target mutations. In addition, we established a new editor variant recognizing an NAA protospacer adjacent motif, expanding the base editing possibilities in zebrafish. Using these approaches, we first generated a base change in the ctnnb1 gene, mimicking oncogenic an mutation of the human gene known to result in constitutive activation of endogenous Wnt signaling. Additionally, we precisely targeted several cancer-associated genes including cbl. With this last target, we created a new zebrafish dwarfism model. Together our findings expand the potential of zebrafish as a model system allowing new approaches for the endogenous modulation of cell signaling pathways and the generation of precise models of human genetic disease-associated mutations.
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Affiliation(s)
- Marion Rosello
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France.,Institut Curie, PSL Research University, Inserm U934, CNRS UMR3215, Paris, France
| | - Juliette Vougny
- Institut Curie, PSL Research University, Inserm U934, CNRS UMR3215, Paris, France
| | - François Czarny
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
| | - Marina C Mione
- Department of Cellular, Computational and Integrative Biology - CIBIO, University of Trento, Trento, Italy
| | - Jean-Paul Concordet
- Muséum National d'Histoire Naturelle, INSERM U1154, CNRS UMR 7196, Paris, France
| | - Shahad Albadri
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
| | - Filippo Del Bene
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France.,Institut Curie, PSL Research University, Inserm U934, CNRS UMR3215, Paris, France
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10
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Wang X, Qiu Z, Ji X, Ning W, An Y, Wang S, Zhang H. A novel workflow for cancer blood biomarker identification. ANNALS OF TRANSLATIONAL MEDICINE 2020; 8:1430. [PMID: 33313175 PMCID: PMC7723582 DOI: 10.21037/atm-20-2047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background Over the last few years, great progress has been made in the development of key technologies to detect peripheral blood-based, tumor-specific biomarkers, such as circulating tumor cells (CTCs) and circulating cell free tumor DNA (ctDNA). Despite the considerable advances and their multiple clinical values, liquid biopsies are challenged by the very low concentrations of CTCs and ctDNA in blood samples. Additionally, blood biomarkers which were found using data-driven methods may only be effective in few datasets. Methods We firstly collected the genes which have expression correlations between blood and the other tissues/organs using Genotype-Tissue Expression (GTEx). Survival hazard genes and differential expression genes of each cancer type in The Cancer Genome Atlas (TCGA) were then selected by Cox regression model and Wilcoxon rank sum test, respectively. By combining the P values of two steps, several blood biomarkers can be inferred for each cancer type. After applying these potential blood biomarker sets to 13 datasets of blood samples from solid tumor patients using single sample gene set enrichment analyses (ssGSEA), we got an enrichment score (ES) for each sample. Results The inferred blood biomarker (BB infer) genes showed reliable predictive value in various malignancies. In all the blood samples that were analyzed, the ESs of positive BB Infer genes in cancer patients are higher than healthy people. Conversely, the ESs of negative BB Infer genes in cancer patients are lower than healthy people. Furthermore, lower ES of negative BB infer genes signify the dismal outcome of patients. Conclusions We developed a novel solid tumor blood biomarker inference workflow for cancer screening and diagnosis. Moreover, we demonstrated the utility of this inference method in a series of blood sample datasets of solid tumor patients. These results suggested the potential value of this method in the screening, diagnosis and prognosis of cancers.
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Affiliation(s)
- Xiang Wang
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Zhiqiang Qiu
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Xiangwen Ji
- Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, School of Basic Medical Sciences, Peking University, Beijing, China
| | - Weihai Ning
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Yihua An
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Chinese PLA General Hospital, Beijing, China
| | - Shengdian Wang
- CAS Key Laboratory of Infection and Immunity, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Hongwei Zhang
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, China
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11
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Khalighi S, Singh S, Varadan V. Untangling a complex web: Computational analyses of tumor molecular profiles to decode driver mechanisms. J Genet Genomics 2020; 47:595-609. [PMID: 33423960 PMCID: PMC7902422 DOI: 10.1016/j.jgg.2020.11.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 11/04/2020] [Accepted: 11/14/2020] [Indexed: 12/19/2022]
Abstract
Genome-scale studies focusing on molecular profiling of cancers across tissue types have revealed a plethora of aberrations across the genomic, transcriptomic, and epigenomic scales. The significant molecular heterogeneity across individual tumors even within the same tissue context complicates decoding the key etiologic mechanisms of this disease. Furthermore, it is increasingly likely that biologic mechanisms underlying the pathobiology of cancer involve multiple molecular entities interacting across functional scales. This has motivated the development of computational approaches that integrate molecular measurements with prior biological knowledge in increasingly intricate ways to enable the discovery of driver genomic aberrations across cancers. Here, we review diverse methodological approaches that have powered significant advances in our understanding of the genomic underpinnings of cancer at the cohort and at the individual tumor scales. We outline the key advances and challenges in the computational discovery of cancer mechanisms while motivating the development of systems biology approaches to comprehensively decode the biologic drivers of this complex disease.
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Affiliation(s)
- Sirvan Khalighi
- Division of General Medical Sciences-Oncology, Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Salendra Singh
- Division of General Medical Sciences-Oncology, Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Vinay Varadan
- Division of General Medical Sciences-Oncology, Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA.
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12
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Al Hajri Q, Dash S, Feng WC, Garner HR, Anandakrishnan R. Identifying multi-hit carcinogenic gene combinations: Scaling up a weighted set cover algorithm using compressed binary matrix representation on a GPU. Sci Rep 2020; 10:2022. [PMID: 32029803 PMCID: PMC7005272 DOI: 10.1038/s41598-020-58785-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 01/20/2020] [Indexed: 01/16/2023] Open
Abstract
Despite decades of research, effective treatments for most cancers remain elusive. One reason is that different instances of cancer result from different combinations of multiple genetic mutations (hits). Therefore, treatments that may be effective in some cases are not effective in others. We previously developed an algorithm for identifying combinations of carcinogenic genes with mutations (multi-hit combinations), which could suggest a likely cause for individual instances of cancer. Most cancers are estimated to require three or more hits. However, the computational complexity of the algorithm scales exponentially with the number of hits, making it impractical for identifying combinations of more than two hits. To identify combinations of greater than two hits, we used a compressed binary matrix representation, and optimized the algorithm for parallel execution on an NVIDIA V100 graphics processing unit (GPU). With these enhancements, the optimized GPU implementation was on average an estimated 12,144 times faster than the original integer matrix based CPU implementation, for the 3-hit algorithm, allowing us to identify 3-hit combinations. The 3-hit combinations identified using a training set were able to differentiate between tumor and normal samples in a separate test set with 90% overall sensitivity and 93% overall specificity. We illustrate how the distribution of mutations in tumor and normal samples in the multi-hit gene combinations can suggest potential driver mutations for further investigation. With experimental validation, these combinations may provide insight into the etiology of cancer and a rational basis for targeted combination therapy.
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Affiliation(s)
- Qais Al Hajri
- Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, 24060, USA
| | - Sajal Dash
- Department of Computer Science, Virginia Tech, Blacksburg, VA, 24060, USA
| | - Wu-Chun Feng
- Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, 24060, USA
- Department of Computer Science, Virginia Tech, Blacksburg, VA, 24060, USA
| | - Harold R Garner
- Department of Biomedical Sciences, Edward Via College of Osteopathic Medicine, Blacksburg, VA, 24060, USA
- Gibbs Cancer Center and Research Institute, Spartanburg, SC, 29303, USA
| | - Ramu Anandakrishnan
- Department of Biomedical Sciences, Edward Via College of Osteopathic Medicine, Blacksburg, VA, 24060, USA.
- Gibbs Cancer Center and Research Institute, Spartanburg, SC, 29303, USA.
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Estimating the number of genetic mutations (hits) required for carcinogenesis based on the distribution of somatic mutations. PLoS Comput Biol 2019; 15:e1006881. [PMID: 30845172 PMCID: PMC6424461 DOI: 10.1371/journal.pcbi.1006881] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Revised: 03/19/2019] [Accepted: 02/16/2019] [Indexed: 12/20/2022] Open
Abstract
Individual instances of cancer are primarily a result of a combination of a small number of genetic mutations (hits). Knowing the number of such mutations is a prerequisite for identifying specific combinations of carcinogenic mutations and understanding the etiology of cancer. We present a mathematical model for estimating the number of hits based on the distribution of somatic mutations. The model is fundamentally different from previous approaches, which are based on cancer incidence by age. Our somatic mutation based model is likely to be more robust than age-based models since it does not require knowing or accounting for the highly variable mutation rate, which can vary by over three orders of magnitude. In fact, we find that the number of somatic mutations at diagnosis is weakly correlated with age at cancer diagnosis, most likely due to the extreme variability in mutation rates between individuals. Comparing the distribution of somatic mutations predicted by our model to the actual distribution from 6904 tumor samples we estimate the number of hits required for carcinogenesis for 17 cancer types. We find that different cancer types exhibit distinct somatic mutational profiles corresponding to different numbers of hits. Why might different cancer types require different numbers of hits for carcinogenesis? The answer may provide insight into the unique etiology of different cancer types. Cancer is primarily a result of genetic mutations. Each individual instance of cancer is initiated by a specific combination of a small number of mutations (hits). In trying to identify these combinations of mutations, it is important to know how many hits to look for. However, there are conflicting estimates for the number of hits. We present a fundamentally different model for estimating the number of hits. We found that the number hits ranges from two-eight depending on cancer type. These findings may provide insight into the unique characteristics of different cancer types.
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