Literature DB >> 30643180

Measuring Tumor Mutational Burden (TMB) in Plasma from mCRPC Patients Using Two Commercial NGS Assays.

Ping Qiu1, Christian H Poehlein2, Matthew J Marton2, Omar F Laterza2, Diane Levitan2.   

Abstract

Tumor tissue mutational burden (TMB) has emerged as a promising predictive biomarker for immune checkpoint therapy. Measuring TMB from circulating tumor DNA (ctDNA) found in plasma is attractive in tissue-constrained indications. We compared the performance of two plasma-based commercial TMB assays including the effect of two different collection methods. Our findings suggest that the two plasma based TMB assays are highly correlated and they are also both correlated with a tissue-based TMB assay for relatively high TMB samples. The two collection methods are also found to be very comparable. Plasma-based TMB assays may be mature enough to be clinically useful in mCRPC and potentially other indications.

Entities:  

Mesh:

Substances:

Year:  2019        PMID: 30643180      PMCID: PMC6331610          DOI: 10.1038/s41598-018-37128-y

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


Introduction

Liquid biopsies are more convenient, less expensive and less risky to the patient than standard tumor biopsy[1]. Tumor tissue mutational burden (TMB) has emerged as a promising predictive biomarker for immune checkpoint therapy. Measuring TMB from circulating tumor DNA (ctDNA) found in plasma is attractive[2] in indications such as metastatic castration resistance prostate cancer (mCRPC), where obtaining tissue can be challenging. Chalmers et al.[3] demonstrated that TMB can be accurately measured by sequencing targeted gene panels but that accuracy is compromised when the sequenced genome region (bait size) is less than 0.5 MB. The Guardant Health (GH) Omni (500 genes, 2.1 MB) and Foundation Medicine (FMI) bTMB (394 genes, 1.14 MB) panels are plasma-based NGS assays containing sufficiently large bait sizes to measure TMB across a broad range of TMB values. Poor concordance on mutation detection between two commercial vendors was reported by Torga and Pienta previously[4]. We compared the performance of TMB determination of the two plasma assays in this study, including the effect of two different collection methods.

Methods

Replicate sets of plasma samples from 20 mCRPC patients (2 ml each; Streck tube protocol with double centrifugation[5]) were sent to GH and FMI for analysis. In addition, one set of samples from the same 20 subjects collected in EDTA tubes[6] (spun once, stored at −70 °C, spun again prior to NGS assay at GH) were also sent to GH to investigate the impact of different pre-analytical collection methods in determination of TMB. Matching formalin-fixed paraffin-embedded (FFPE) tissue from each subject (collected within twelve months of plasma collection) was analyzed by WES at Personal Genome Diagnostics. All methods were carried out in accordance with relevant guidelines and regulations. All experimental protocols were approved by Merck Ethics Review Committees. Informed consent was obtained from all subjects or, if subjects are under 18, from a parent and/or legal guardian.

Results

TMB measured by the FMI bTMB and GH Omni assays is summarized in Fig. 1. A high correlation (R2 ≈ 0.9) between blood TMB assays (muts/MB) and tissue WES (muts/exome) was observed in general (Fig. 1A,C), although correlation for low/medium tissue TMB samples was not as high as high TMB samples (R2 ≈ 0.1; Fig. 1B,D). A similar low correlation between targeted panel NGS and WES at low TMB level was also demonstrated in a tissue based assay by Buchhalter et al. using TCGA data[7]. However, the two plasma based TMB assays are highly correlated (R2 > 0.9) even for biopsy samples with low/medium TMB (Fig. 1E,F), which suggests that the low concordance between tissue and blood for low TMB samples may be more likely to be biological rather than technical. The GH Omni panel reports all four types of mutations in addition to TMB, while the FMI bTMB assay is validated to report blood TMB only. Therefore, it was not possible to evaluate concordance of target gene mutation detection between these two panels in this study.
Figure 1

Comparison of TMB measured by WES (muts/exome) in tissue biopsies and TMB measured from ctDNA (muts/MB) obtained from Streck tubes by GuardantHealth Omni and Foundation Medicine bTMB assays.

Comparison of TMB measured by WES (muts/exome) in tissue biopsies and TMB measured from ctDNA (muts/MB) obtained from Streck tubes by GuardantHealth Omni and Foundation Medicine bTMB assays. It is worth noting that the absolute values of TMB (muts/MB) from the two plasma assays are different. This is likely due to difference in sequencing depth, TMB algorithm implemented and allele frequency cutoff used in the two assays. Fig. 2 shows that the two collection methods have a minimal impact on TMB assessment. Four types of mutations (SNP, Indel, CNV, Fusion) detected from the two collection methods are highly concordant (Fig. 2E). The cell free DNA yield from the two collection methods are very comparable (data not shown) which suggests that the freeze-thaw impact is minimal as long as a first spin is done before freezing.
Figure 2

Comparison of TMB measured from plasma collected either by Streck protocol or the EDTA protocol using GH Omni assay. Samples with unmatched collection time (less than one month difference) between Streck and EDTA are highlighted in red in D.

Comparison of TMB measured from plasma collected either by Streck protocol or the EDTA protocol using GH Omni assay. Samples with unmatched collection time (less than one month difference) between Streck and EDTA are highlighted in red in D.

Discussion

Kuderer et al.[8] reported a large discordance in driver gene mutation detection between tissue and plasma ctDNA based assays. In this study, although mutation level concordance was not compared, good correlation between tissue and plasma ctDNA TMB is observed for both assays for high TMB samples. However, correlation is compromised between tissue and plasma ctDNA based assays for low/medium TMB samples. Given the biological difference between plasma and tissue and the magnitude of sequencing depth differences between these two assays, a good correlation between tissue WES and plasma assays may not necessarily be expected. This study suggests the clinical validity of plasma TMB and tissue TMB in immunotherapy be evaluated independently. Another important finding of this study is that EDTA plasma may be a suitable specimen for the determination of TMB. This is important information for investigators interested in performing retrospective analysis in studies in which plasma samples may not have been collected in Streck tubes. Although additional studies with larger sample size and clinical outcome data are warranted to further substantiate these preliminary findings and evaluate the clinical utility of plasma TMB, these preliminary results are promising and suggest plasma-based TMB assays may be mature enough to be clinically useful in mCRPC and potentially other indications.
  8 in total

1.  Prospective Validation of Rapid Plasma Genotyping for the Detection of EGFR and KRAS Mutations in Advanced Lung Cancer.

Authors:  Adrian G Sacher; Cloud Paweletz; Suzanne E Dahlberg; Ryan S Alden; Allison O'Connell; Nora Feeney; Stacy L Mach; Pasi A Jänne; Geoffrey R Oxnard
Journal:  JAMA Oncol       Date:  2016-08-01       Impact factor: 31.777

2.  Comparison of 2 Commercially Available Next-Generation Sequencing Platforms in Oncology.

Authors:  Nicole M Kuderer; Kimberly A Burton; Sibel Blau; Andrea L Rose; Stephanie Parker; Gary H Lyman; C Anthony Blau
Journal:  JAMA Oncol       Date:  2017-07-01       Impact factor: 31.777

3.  Size matters: Dissecting key parameters for panel-based tumor mutational burden analysis.

Authors:  Ivo Buchhalter; Eugen Rempel; Volker Endris; Michael Allgäuer; Olaf Neumann; Anna-Lena Volckmar; Martina Kirchner; Jonas Leichsenring; Amelie Lier; Moritz von Winterfeld; Roland Penzel; Petros Christopoulos; Michael Thomas; Stefan Fröhling; Peter Schirmacher; Jan Budczies; Albrecht Stenzinger
Journal:  Int J Cancer       Date:  2018-12-04       Impact factor: 7.396

4.  Patient-Paired Sample Congruence Between 2 Commercial Liquid Biopsy Tests.

Authors:  Gonzalo Torga; Kenneth J Pienta
Journal:  JAMA Oncol       Date:  2018-06-01       Impact factor: 31.777

Review 5.  Circulating Tumor DNA Analysis in Patients With Cancer: American Society of Clinical Oncology and College of American Pathologists Joint Review.

Authors:  Jason D Merker; Geoffrey R Oxnard; Carolyn Compton; Maximilian Diehn; Patricia Hurley; Alexander J Lazar; Neal Lindeman; Christina M Lockwood; Alex J Rai; Richard L Schilsky; Apostolia M Tsimberidou; Patricia Vasalos; Brooke L Billman; Thomas K Oliver; Suanna S Bruinooge; Daniel F Hayes; Nicholas C Turner
Journal:  J Clin Oncol       Date:  2018-03-05       Impact factor: 44.544

6.  Comprehensive molecular characterization of clinical responses to PD-1 inhibition in metastatic gastric cancer.

Authors:  Seung Tae Kim; Razvan Cristescu; Adam J Bass; Kyoung-Mee Kim; Justin I Odegaard; Kyung Kim; Xiao Qiao Liu; Xinwei Sher; Hun Jung; Mijin Lee; Sujin Lee; Se Hoon Park; Joon Oh Park; Young Suk Park; Ho Yeong Lim; Hyuk Lee; Mingew Choi; AmirAli Talasaz; Peter Soonmo Kang; Jonathan Cheng; Andrey Loboda; Jeeyun Lee; Won Ki Kang
Journal:  Nat Med       Date:  2018-07-16       Impact factor: 53.440

7.  Optimised Pre-Analytical Methods Improve KRAS Mutation Detection in Circulating Tumour DNA (ctDNA) from Patients with Non-Small Cell Lung Cancer (NSCLC).

Authors:  James L Sherwood; Claire Corcoran; Helen Brown; Alan D Sharpe; Milena Musilova; Alexander Kohlmann
Journal:  PLoS One       Date:  2016-02-26       Impact factor: 3.240

8.  Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden.

Authors:  Zachary R Chalmers; Caitlin F Connelly; David Fabrizio; Laurie Gay; Siraj M Ali; Riley Ennis; Alexa Schrock; Brittany Campbell; Adam Shlien; Juliann Chmielecki; Franklin Huang; Yuting He; James Sun; Uri Tabori; Mark Kennedy; Daniel S Lieber; Steven Roels; Jared White; Geoffrey A Otto; Jeffrey S Ross; Levi Garraway; Vincent A Miller; Phillip J Stephens; Garrett M Frampton
Journal:  Genome Med       Date:  2017-04-19       Impact factor: 11.117

  8 in total
  11 in total

1.  Baseline Plasma Tumor Mutation Burden Predicts Response to Pembrolizumab-based Therapy in Patients with Metastatic Non-Small Cell Lung Cancer.

Authors:  Charu Aggarwal; Jeffrey C Thompson; Austin L Chien; Katie J Quinn; Wei-Ting Hwang; Taylor A Black; Stephanie S Yee; Theresa E Christensen; Michael J LaRiviere; Benjamin A Silva; Kimberly C Banks; Rebecca J Nagy; Elena Helman; Abigail T Berman; Christine A Ciunci; Aditi P Singh; Jeffrey S Wasser; Joshua M Bauml; Corey J Langer; Roger B Cohen; Erica L Carpenter
Journal:  Clin Cancer Res       Date:  2020-02-26       Impact factor: 12.531

Review 2.  Driving innovation for rare skin cancers: utilizing common tumours and machine learning to predict immune checkpoint inhibitor response.

Authors:  J S Hooiveld-Noeken; R S N Fehrmann; E G E de Vries; M Jalving
Journal:  Immunooncol Technol       Date:  2019-11-27

Review 3.  The current state of molecular profiling in gastrointestinal malignancies.

Authors:  Reetu Mukherji; Chao Yin; Rumaisa Hameed; Ali Z Alqahtani; Monika Kulasekaran; Aiwu R He; Benjamin A Weinberg; John L Marshall; Marion L Hartley; Marcus S Noel
Journal:  Biol Direct       Date:  2022-06-06       Impact factor: 7.173

Review 4.  Precision Medicine for NSCLC in the Era of Immunotherapy: New Biomarkers to Select the Most Suitable Treatment or the Most Suitable Patient.

Authors:  Giovanni Rossi; Alessandro Russo; Marco Tagliamento; Alessandro Tuzi; Olga Nigro; Giacomo Vallome; Claudio Sini; Massimiliano Grassi; Maria Giovanna Dal Bello; Simona Coco; Luca Longo; Lodovica Zullo; Enrica Teresa Tanda; Chiara Dellepiane; Paolo Pronzato; Carlo Genova
Journal:  Cancers (Basel)       Date:  2020-04-30       Impact factor: 6.639

Review 5.  Tumor mutational burden quantification from targeted gene panels: major advancements and challenges.

Authors:  Laura Fancello; Sara Gandini; Pier Giuseppe Pelicci; Luca Mazzarella
Journal:  J Immunother Cancer       Date:  2019-07-15       Impact factor: 13.751

6.  Durvalumab With or Without Tremelimumab vs Standard Chemotherapy in First-line Treatment of Metastatic Non-Small Cell Lung Cancer: The MYSTIC Phase 3 Randomized Clinical Trial.

Authors:  Naiyer A Rizvi; Byoung Chul Cho; Niels Reinmuth; Ki Hyeong Lee; Alexander Luft; Myung-Ju Ahn; Michel M van den Heuvel; Manuel Cobo; David Vicente; Alexey Smolin; Vladimir Moiseyenko; Scott J Antonia; Sylvestre Le Moulec; Gilles Robinet; Ronald Natale; Jeffrey Schneider; Frances A Shepherd; Sarayut Lucien Geater; Edward B Garon; Edward S Kim; Sarah B Goldberg; Kazuhiko Nakagawa; Rajiv Raja; Brandon W Higgs; Anne-Marie Boothman; Luping Zhao; Urban Scheuring; Paul K Stockman; Vikram K Chand; Solange Peters
Journal:  JAMA Oncol       Date:  2020-05-01       Impact factor: 31.777

Review 7.  Immunotherapy in Breast Cancer and the Potential Role of Liquid Biopsy.

Authors:  Mark Jesus M Magbanua; Ozge Gumusay; Razelle Kurzrock; Laura J van 't Veer; Hope S Rugo
Journal:  Front Oncol       Date:  2022-03-15       Impact factor: 5.738

8.  Applications of Circulating Tumor DNA in a Cohort of Phase I Solid Tumor Patients Treated With Immunotherapy.

Authors:  Daniel V Araujo; Ao Wang; Dax Torti; Alberto Leon; Kayla Marsh; Aoife McCarthy; Hal Berman; Anna Spreafico; Aaron R Hansen; Albiruni-Abdul Razak; Philippe L Bedard; Lisa Wang; Eric Plackmann; Helen Chow; Hua Bao; Xue Wu; Trevor J Pugh; Lillian L Siu
Journal:  JNCI Cancer Spectr       Date:  2021-01-23

9.  Predictive value of tumor mutational burden for immunotherapy in non-small cell lung cancer: A systematic review and meta-analysis.

Authors:  Guangxian Meng; Xiaowei Liu; Tian Ma; Desheng Lv; Ge Sun
Journal:  PLoS One       Date:  2022-02-03       Impact factor: 3.240

10.  Discordance in Tumor Mutation Burden from Blood and Tissue Affects Association with Response to Immune Checkpoint Inhibition in Real-World Settings.

Authors:  Emma G Sturgill; Amanda Misch; Carissa C Jones; Daniel Luckett; Xiaotong Fu; Dan Schlauch; Suzanne F Jones; Howard A Burris; David R Spigel; Andrew J McKenzie
Journal:  Oncologist       Date:  2022-03-11
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.