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Yang W, Qiang Y, Wu W, Xin J. Graph-ETMB: A graph neural network-based model for tumour mutation burden estimation. Comput Biol Chem 2023; 105:107900. [PMID: 37285654 DOI: 10.1016/j.compbiolchem.2023.107900] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 05/06/2023] [Accepted: 05/31/2023] [Indexed: 06/09/2023]
Abstract
As a critical indicator of how easily the human immune system recognizes tumour cells, tumour mutational burden (TMB) is widely used to identify the potential effectiveness of immune checkpoint inhibitor therapy. However, the difficulties associated with the whole exome sequencing (WES) process, such as high tissue sampling requirements, high costs, and long turnaround times, have hindered the widespread clinical use of WES. Furthermore, the mutation landscape varies across cancer types, and the distribution of TMBs varies across cancer subtypes. Therefore, there is an urgent clinical need to develop a small cancer-specific panel to estimate TMB accurately, predict immunotherapy response cost-effectively and assist physicians in precise decision-making. This paper uses a graph neural network framework (Graph-ETMB) to address the cancer specificity problem in TMB. The correlation and tractability between mutated genes are described through message-passing and aggregation algorithms between graph networks. Then the graph neural network is trained in the lung adenocarcinoma data through a semi-supervised approach, resulting in a mutation panel containing 20 genes with a length of only 0.16 Mb. The number of genes to be detected is smaller than most commercial panels currently in clinical use. In addition, the efficacy of the designed panel in predicting immunotherapy response was further determined in an independent validation dataset, exploring the association between TMB and immunotherapy efficacy.
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Affiliation(s)
- Wanting Yang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, Shanxi 030000, China
| | - Yan Qiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, Shanxi 030000, China.
| | - Wei Wu
- Department of Clinical Laboratory, Affiliated People's Hospital of Shanxi Medical University, Shanxi Provincial People's Hospital, Taiyuan, Shanxi, China
| | - Jialong Xin
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, Shanxi 030000, China
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2
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Yao L, Rey DA, Bulgarelli L, Kast R, Osborn J, Van Ark E, Fang LT, Lau B, Lam H, Teixeira LM, Neto AS, Bellomo R, Deliberato RO. Gene Expression Scoring of Immune Activity Levels for Precision Use of Hydrocortisone in Vasodilatory Shock. Shock 2022; 57:384-391. [PMID: 35081076 PMCID: PMC8868213 DOI: 10.1097/shk.0000000000001910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/06/2021] [Accepted: 01/07/2022] [Indexed: 11/26/2022]
Abstract
PURPOSE Among patients with vasodilatory shock, gene expression scores may identify different immune states. We aimed to test whether such scores are robust in identifying patients' immune state and predicting response to hydrocortisone treatment in vasodilatory shock. MATERIALS AND METHODS We selected genes to generate continuous scores to define previously established subclasses of sepsis. We used these scores to identify a patient's immune state. We evaluated the potential for these states to assess the differential effect of hydrocortisone in two randomized clinical trials of hydrocortisone versus placebo in vasodilatory shock. RESULTS We initially identified genes associated with immune-adaptive, immune-innate, immune-coagulant functions. From these genes, 15 were most relevant to generate expression scores related to each of the functions. These scores were used to identify patients as immune-adaptive prevalent (IA-P) and immune-innate prevalent (IN-P). In IA-P patients, hydrocortisone therapy increased 28-day mortality in both trials (43.3% vs 14.7%, P = 0.028) and (57.1% vs 0.0%, P = 0.99). In IN-P patients, this effect was numerically reversed. CONCLUSIONS Gene expression scores identified the immune state of vasodilatory shock patients, one of which (IA-P) identified those who may be harmed by hydrocortisone. Gene expression scores may help advance the field of personalized medicine.
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Affiliation(s)
- Lijing Yao
- Department of Clinical Data Science, Endpoint Health Inc, Palo Alto, California
| | - Diego Ariel Rey
- Department of Clinical Data Science, Endpoint Health Inc, Palo Alto, California
| | - Lucas Bulgarelli
- Department of Clinical Data Science, Endpoint Health Inc, Palo Alto, California
| | - Rachel Kast
- Department of Clinical Data Science, Endpoint Health Inc, Palo Alto, California
| | - Jeff Osborn
- Department of Clinical Data Science, Endpoint Health Inc, Palo Alto, California
| | - Emily Van Ark
- Department of Clinical Data Science, Endpoint Health Inc, Palo Alto, California
| | - Li Tai Fang
- Department of Clinical Data Science, Endpoint Health Inc, Palo Alto, California
| | - Bayo Lau
- Bioinformatics Department, HypaHub Inc, San Jose, California, USA
| | - Hugo Lam
- Bioinformatics Department, HypaHub Inc, San Jose, California, USA
| | | | - Ary Serpa Neto
- Department of Critical Care Medicine, Hospital Israelita Albert Einstein, São Paulo, Brazil
- Australian and New Zealand Intensive Care Research Centre (ANZIC-RC), School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
- Department of Critical Care, Melbourne Medical School, University of Melbourne, Austin Hospital, Melbourne, Australia
- Data Analytics Research and Evaluation (DARE) Centre, Austin Hospital, Melbourne, Australia
| | - Rinaldo Bellomo
- Australian and New Zealand Intensive Care Research Centre (ANZIC-RC), School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
- Department of Critical Care, Melbourne Medical School, University of Melbourne, Austin Hospital, Melbourne, Australia
- Data Analytics Research and Evaluation (DARE) Centre, Austin Hospital, Melbourne, Australia
- Department of Intensive Care, Austin Hospital, Melbourne, Australia
- Department of Intensive Care, Royal Melbourne Hospital, Melbourne, Australia
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3
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Vega DM, Yee LM, McShane LM, Williams PM, Chen L, Vilimas T, Fabrizio D, Funari V, Newberg J, Bruce LK, Chen SJ, Baden J, Carl Barrett J, Beer P, Butler M, Cheng JH, Conroy J, Cyanam D, Eyring K, Garcia E, Green G, Gregersen VR, Hellmann MD, Keefer LA, Lasiter L, Lazar AJ, Li MC, MacConaill LE, Meier K, Mellert H, Pabla S, Pallavajjalla A, Pestano G, Salgado R, Samara R, Sokol ES, Stafford P, Budczies J, Stenzinger A, Tom W, Valkenburg KC, Wang XZ, Weigman V, Xie M, Xie Q, Zehir A, Zhao C, Zhao Y, Stewart MD, Allen J. Aligning tumor mutational burden (TMB) quantification across diagnostic platforms: phase II of the Friends of Cancer Research TMB Harmonization Project. Ann Oncol 2021; 32:1626-1636. [PMID: 34606929 DOI: 10.1016/j.annonc.2021.09.016] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Revised: 09/21/2021] [Accepted: 09/26/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Tumor mutational burden (TMB) measurements aid in identifying patients who are likely to benefit from immunotherapy; however, there is empirical variability across panel assays and factors contributing to this variability have not been comprehensively investigated. Identifying sources of variability can help facilitate comparability across different panel assays, which may aid in broader adoption of panel assays and development of clinical applications. MATERIALS AND METHODS Twenty-nine tumor samples and 10 human-derived cell lines were processed and distributed to 16 laboratories; each used their own bioinformatics pipelines to calculate TMB and compare to whole exome results. Additionally, theoretical positive percent agreement (PPA) and negative percent agreement (NPA) of TMB were estimated. The impact of filtering pathogenic and germline variants on TMB estimates was assessed. Calibration curves specific to each panel assay were developed to facilitate translation of panel TMB values to whole exome sequencing (WES) TMB values. RESULTS Panel sizes >667 Kb are necessary to maintain adequate PPA and NPA for calling TMB high versus TMB low across the range of cut-offs used in practice. Failure to filter out pathogenic variants when estimating panel TMB resulted in overestimating TMB relative to WES for all assays. Filtering out potential germline variants at >0% population minor allele frequency resulted in the strongest correlation to WES TMB. Application of a calibration approach derived from The Cancer Genome Atlas data, tailored to each panel assay, reduced the spread of panel TMB values around the WES TMB as reflected in lower root mean squared error (RMSE) for 26/29 (90%) of the clinical samples. CONCLUSIONS Estimation of TMB varies across different panels, with panel size, gene content, and bioinformatics pipelines contributing to empirical variability. Statistical calibration can achieve more consistent results across panels and allows for comparison of TMB values across various panel assays. To promote reproducibility and comparability across assays, a software tool was developed and made publicly available.
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Affiliation(s)
- D M Vega
- Friends of Cancer Research, Washington, USA
| | - L M Yee
- National Cancer Institute, Bethesda, USA
| | | | - P M Williams
- Molecular Characterization Laboratory, Frederick National Lab for Cancer Research, Leidos Biomedical Research Inc., Frederick, USA
| | - L Chen
- Molecular Characterization Laboratory, Frederick National Lab for Cancer Research, Leidos Biomedical Research Inc., Frederick, USA
| | - T Vilimas
- Molecular Characterization Laboratory, Frederick National Lab for Cancer Research, Leidos Biomedical Research Inc., Frederick, USA
| | - D Fabrizio
- Foundation Medicine Inc., Cambridge, USA
| | - V Funari
- NeoGenomics Laboratories, Aliso Viejo, USA
| | - J Newberg
- Foundation Medicine Inc., Cambridge, USA
| | - L K Bruce
- NeoGenomics Laboratories, Aliso Viejo, USA
| | | | - J Baden
- Bristol Myers Squibb Co., Princeton, USA
| | | | - P Beer
- European Organisation for Research and Treatment of Cancer, Brussels, Belgium
| | - M Butler
- LGC Clinical Diagnostics, Gaithersburg, USA
| | | | | | - D Cyanam
- Clinical Sequencing Division, Thermo Fisher Scientific, Ann Arbor, USA
| | - K Eyring
- Intermountain Precision Genomics, St. George, USA
| | - E Garcia
- Brigham and Women's Hospital, Boston, USA
| | - G Green
- Bristol Myers Squibb Co., Princeton, USA
| | | | - M D Hellmann
- Memorial Sloan Kettering Cancer Center, New York, USA
| | - L A Keefer
- Personal Genome Diagnostics, Baltimore, USA
| | - L Lasiter
- Friends of Cancer Research, Washington, USA
| | - A J Lazar
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - M-C Li
- National Cancer Institute, Bethesda, USA
| | | | - K Meier
- Illumina Inc, Clinical Genomics, San Diego, USA
| | | | | | | | | | - R Salgado
- European Organisation for Research and Treatment of Cancer, Brussels, Belgium
| | | | - E S Sokol
- Foundation Medicine Inc., Cambridge, USA
| | | | - J Budczies
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - A Stenzinger
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - W Tom
- Clinical Sequencing Division, Thermo Fisher Scientific, Ann Arbor, USA
| | | | - X Z Wang
- EMD Serono Research and Development Institute, Inc., Billerica, USA
| | | | - M Xie
- AstraZeneca Pharmaceuticals LP, Waltham, USA
| | - Q Xie
- General Dynamics Information Technology, Inc., Columbia, USA
| | - A Zehir
- Memorial Sloan Kettering Cancer Center, New York, USA
| | - C Zhao
- Illumina Inc, Clinical Genomics, San Diego, USA
| | - Y Zhao
- National Cancer Institute, Bethesda, USA
| | - M D Stewart
- Friends of Cancer Research, Washington, USA.
| | - J Allen
- Friends of Cancer Research, Washington, USA
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4
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Sha D, Jin Z, Budczies J, Kluck K, Stenzinger A, Sinicrope FA. Tumor Mutational Burden as a Predictive Biomarker in Solid Tumors. Cancer Discov 2020; 10:1808-1825. [PMID: 33139244 DOI: 10.1158/2159-8290.cd-20-0522] [Citation(s) in RCA: 366] [Impact Index Per Article: 91.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 07/03/2020] [Accepted: 09/09/2020] [Indexed: 11/16/2022]
Abstract
Tumor mutational burden (TMB), defined as the number of somatic mutations per megabase of interrogated genomic sequence, varies across malignancies. Panel sequencing-based estimates of TMB have largely replaced whole-exome sequencing-derived TMB in the clinic. Retrospective evidence suggests that TMB can predict the efficacy of immune checkpoint inhibitors, and data from KEYNOTE-158 led to the recent FDA approval of pembrolizumab for the TMB-high tumor subgroup. Unmet needs include prospective validation of TMB cutoffs in relationship to tumor type and patient outcomes. Furthermore, standardization and harmonization of TMB measurement across test platforms are important to the successful implementation of TMB in clinical practice. SIGNIFICANCE: Evaluation of TMB as a predictive biomarker creates the need to harmonize panel-based TMB estimation and standardize its reporting. TMB can improve the predictive accuracy for immunotherapy outcomes, and has the potential to expand the candidate pool of patients for treatment with immune checkpoint inhibitors.
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Affiliation(s)
- Dan Sha
- Departments of Medicine and Gastrointestinal Research Unit, Mayo Clinic, Rochester, Minnesota
| | - Zhaohui Jin
- Department of Oncology, Mayo Clinic, Rochester, Minnesota
| | - Jan Budczies
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.,German Cancer Consortium (DKTK), Heidelberg Partner Site, Heidelberg, Germany
| | - Klaus Kluck
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.,German Cancer Consortium (DKTK), Heidelberg Partner Site, Heidelberg, Germany
| | - Albrecht Stenzinger
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.,German Cancer Consortium (DKTK), Heidelberg Partner Site, Heidelberg, Germany
| | - Frank A Sinicrope
- Departments of Medicine and Gastrointestinal Research Unit, Mayo Clinic, Rochester, Minnesota. .,Department of Oncology, Mayo Clinic, Rochester, Minnesota.,Mayo Clinic Comprehensive Cancer Center, Rochester, Minnesota
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5
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Merino DM, McShane LM, Fabrizio D, Funari V, Chen SJ, White JR, Wenz P, Baden J, Barrett JC, Chaudhary R, Chen L, Chen WS, Cheng JH, Cyanam D, Dickey JS, Gupta V, Hellmann M, Helman E, Li Y, Maas J, Papin A, Patidar R, Quinn KJ, Rizvi N, Tae H, Ward C, Xie M, Zehir A, Zhao C, Dietel M, Stenzinger A, Stewart M, Allen J. Establishing guidelines to harmonize tumor mutational burden (TMB): in silico assessment of variation in TMB quantification across diagnostic platforms: phase I of the Friends of Cancer Research TMB Harmonization Project. J Immunother Cancer 2020; 8:e000147. [PMID: 32217756 PMCID: PMC7174078 DOI: 10.1136/jitc-2019-000147] [Citation(s) in RCA: 299] [Impact Index Per Article: 74.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/11/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Tumor mutational burden (TMB), defined as the number of somatic mutations per megabase of interrogated genomic sequence, demonstrates predictive biomarker potential for the identification of patients with cancer most likely to respond to immune checkpoint inhibitors. TMB is optimally calculated by whole exome sequencing (WES), but next-generation sequencing targeted panels provide TMB estimates in a time-effective and cost-effective manner. However, differences in panel size and gene coverage, in addition to the underlying bioinformatics pipelines, are known drivers of variability in TMB estimates across laboratories. By directly comparing panel-based TMB estimates from participating laboratories, this study aims to characterize the theoretical variability of panel-based TMB estimates, and provides guidelines on TMB reporting, analytic validation requirements and reference standard alignment in order to maintain consistency of TMB estimation across platforms. METHODS Eleven laboratories used WES data from The Cancer Genome Atlas Multi-Center Mutation calling in Multiple Cancers (MC3) samples and calculated TMB from the subset of the exome restricted to the genes covered by their targeted panel using their own bioinformatics pipeline (panel TMB). A reference TMB value was calculated from the entire exome using a uniform bioinformatics pipeline all members agreed on (WES TMB). Linear regression analyses were performed to investigate the relationship between WES and panel TMB for all 32 cancer types combined and separately. Variability in panel TMB values at various WES TMB values was also quantified using 95% prediction limits. RESULTS Study results demonstrated that variability within and between panel TMB values increases as the WES TMB values increase. For each panel, prediction limits based on linear regression analyses that modeled panel TMB as a function of WES TMB were calculated and found to approximately capture the intended 95% of observed panel TMB values. Certain cancer types, such as uterine, bladder and colon cancers exhibited greater variability in panel TMB values, compared with lung and head and neck cancers. CONCLUSIONS Increasing uptake of TMB as a predictive biomarker in the clinic creates an urgent need to bring stakeholders together to agree on the harmonization of key aspects of panel-based TMB estimation, such as the standardization of TMB reporting, standardization of analytical validation studies and the alignment of panel-based TMB values with a reference standard. These harmonization efforts should improve consistency and reliability of panel TMB estimates and aid in clinical decision-making.
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Affiliation(s)
| | | | | | | | | | | | - Paul Wenz
- Clinical Genomics, Illumina Inc, San Diego, California, USA
| | | | - J Carl Barrett
- Translational Medicine, Oncology Research and Early Development, AstraZeneca Pharmaceuticals LP, Boston, Massachusetts, USA
| | - Ruchi Chaudhary
- Clinical Sequencing Division, Thermo Fisher Scientific, Ann Arbor, Michigan, USA
| | - Li Chen
- Molecular Characterization Laboratory, Frederick National Laboratory for Cancer Research, Frederick, Maryland, USA
| | | | | | - Dinesh Cyanam
- Clinical Sequencing Division, Thermo Fisher Scientific, Ann Arbor, Michigan, USA
| | | | | | | | - Elena Helman
- Bioinformatics, Guardant Health Inc, Redwood City, California, USA
| | - Yali Li
- Foundation Medicine Inc, Cambridge, Massachusetts, USA
| | - Joerg Maas
- Quality in Pathology (QuIP), Berlin, Germany
| | | | - Rajesh Patidar
- Molecular Characterization Laboratory, Frederick National Laboratory for Cancer Research, Frederick, Maryland, USA
| | - Katie J Quinn
- Bioinformatics, Guardant Health Inc, Redwood City, California, USA
| | - Naiyer Rizvi
- Division of Hematology/Oncology, Department of Medicine, Columbia University, New York, New York, USA
| | | | | | - Mingchao Xie
- AstraZeneca Pharmaceuticals LP, Waltham, Massachusetts, USA
| | - Ahmet Zehir
- Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Chen Zhao
- Clinical Genomics, Illumina Inc, San Diego, California, USA
| | | | - Albrecht Stenzinger
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Baden-Württemberg, Germany
| | | | - Jeff Allen
- Friends of Cancer Research, Washington, DC, USA
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