scholarly journals 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

2020 ◽  
Vol 8 (1) ◽  
pp. e000147 ◽  
Author(s):  
Diana M Merino ◽  
Lisa M McShane ◽  
David Fabrizio ◽  
Vincent Funari ◽  
Shu-Jen Chen ◽  
...  
Keyword(s):  
Linear Regression ◽  
Predictive Biomarker ◽  
Regression Analyses ◽  
Reference Standard ◽  
Bioinformatics Pipeline ◽  
Mutational Burden ◽  
Study Results ◽  
Tumor Mutational Burden ◽  
Cancer Types ◽  
Prediction Limits

BackgroundTumor 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.MethodsEleven 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.ResultsStudy 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.ConclusionsIncreasing 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.

scholarly journals Tumor mutational burden assessment in non-small-cell lung cancer samples: results from the TMB2 harmonization project comparing three NGS panels

2021 ◽  
Vol 9 (5) ◽  
pp. e001904
Author(s):  
Javier Ramos-Paradas ◽  
Susana Hernández-Prieto ◽  
David Lora ◽  
Elena Sanchez ◽  
Aranzazu Rosado ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Predictive Value ◽  
Predictive Biomarker ◽  
Small Cell ◽  
Small Cell Lung ◽  
Mutational Burden ◽  
Reproducibility Study ◽  
Tumor Mutational Burden

BackgroundTumor mutational burden (TMB) is a recently proposed predictive biomarker for immunotherapy in solid tumors, including non-small cell lung cancer (NSCLC). Available assays for TMB determination differ in horizontal coverage, gene content and algorithms, leading to discrepancies in results, impacting patient selection. A harmonization study of TMB assessment with available assays in a cohort of patients with NSCLC is urgently needed.MethodsWe evaluated the TMB assessment obtained with two marketed next generation sequencing panels: TruSight Oncology 500 (TSO500) and Oncomine Tumor Mutation Load (OTML) versus a reference assay (Foundation One, FO) in 96 NSCLC samples. Additionally, we studied the level of agreement among the three methods with respect to PD-L1 expression in tumors, checked the level of different immune infiltrates versus TMB, and performed an inter-laboratory reproducibility study. Finally, adjusted cut-off values were determined.ResultsBoth panels showed strong agreement with FO, with concordance correlation coefficients (CCC) of 0.933 (95% CI 0.908 to 0.959) for TSO500 and 0.881 (95% CI 0.840 to 0.922) for OTML. The corresponding CCCs were 0.951 (TSO500-FO) and 0.919 (OTML-FO) in tumors with <1% of cells expressing PD-L1 (PD-L1<1%; N=55), and 0.861 (TSO500-FO) and 0.722 (OTML-FO) in tumors with PD-L1≥1% (N=41). Inter-laboratory reproducibility analyses showed higher reproducibility with TSO500. No significant differences were found in terms of immune infiltration versus TMB. Adjusted cut-off values corresponding to 10 muts/Mb with FO needed to be lowered to 7.847 muts/Mb (TSO500) and 8.380 muts/Mb (OTML) to ensure a sensitivity >88%. With these cut-offs, the positive predictive value was 78.57% (95% CI 67.82 to 89.32) and the negative predictive value was 87.50% (95% CI 77.25 to 97.75) for TSO500, while for OTML they were 73.33% (95% CI 62.14 to 84.52) and 86.11% (95% CI 74.81 to 97.41), respectively.ConclusionsBoth panels exhibited robust analytical performances for TMB assessment, with stronger concordances in patients with negative PD-L1 expression. TSO500 showed a higher inter-laboratory reproducibility. The cut-offs for each assay were lowered to optimal overlap with FO.

Abstract P054: Immune determinants of the association between tumor mutational burden and immunotherapy response across cancer types

2022 ◽  
Author(s):  
Neelam Sinha ◽  
Sanju Sinha ◽  
Christina Valero ◽  
Alejandro A. Schaffer ◽  
Kenneth Aldape ◽  
...  
Keyword(s):  
Mutational Burden ◽  
Tumor Mutational Burden ◽  
Cancer Types

scholarly journals From Tumor Mutational Burden to Blood T Cell Receptor: Looking for the Best Predictive Biomarker in Lung Cancer Treated with Immunotherapy

Cancers ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2974
Author(s):  
Andrea Sesma ◽  
Julián Pardo ◽  
Mara Cruellas ◽  
Eva M. Gálvez ◽  
Marta Gascón ◽  
...  
Keyword(s):  
Lung Cancer ◽  
T Cell ◽  
T Cell Receptor ◽  
Checkpoint Inhibitors ◽  
Predictive Biomarker ◽  
Cell Receptor ◽  
Mutational Burden ◽  
Tcr Repertoire ◽  
Tumor Mutational Burden ◽  
Tumor Immunogenicity

Despite therapeutic advances, lung cancer (LC) is one of the leading causes of cancer morbidity and mortality worldwide. Recently, the treatment of advanced LC has experienced important changes in survival benefit due to immune checkpoint inhibitors (ICIs). However, overall response rates (ORR) remain low in unselected patients and a large proportion of patients undergo disease progression in the first weeks of treatment. Therefore, there is a need of biomarkers to identify patients who will benefit from ICIs. The programmed cell death ligand 1 (PD-L1) expression has been the first biomarker developed. However, its use as a robust predictive biomarker has been limited due to the variability of techniques used, with different antibodies and thresholds. In this context, tumor mutational burden (TMB) has emerged as an additional powerful biomarker based on the observation of successful response to ICIs in solid tumors with high TMB. TMB can be defined as the total number of nonsynonymous mutations per DNA megabases being a mechanism generating neoantigens conditioning the tumor immunogenicity and response to ICIs. However, the latest data provide conflicting results regarding its role as a biomarker. Moreover, considering the results of the recent data, the use of peripheral blood T cell receptor (TCR) repertoire could be a new predictive biomarker. This review summarises recent findings describing the clinical utility of TMB and TCRβ (TCRB) and concludes that immune, neontigen, and checkpoint targeted variables are required in combination for accurately identifying patients who most likely will benefit of ICIs.

scholarly journals Tumor mutational burden as a predictive biomarker for checkpoint inhibitor immunotherapy

2019 ◽  
Vol 16 (1) ◽  
pp. 112-115 ◽  
Author(s):  
Mark Lee ◽  
Robert M. Samstein ◽  
Cristina Valero ◽  
Timothy A. Chan ◽  
Luc G.T. Morris
Keyword(s):  
Checkpoint Inhibitor ◽  
Predictive Biomarker ◽  
Mutational Burden ◽  
Tumor Mutational Burden

Tumor mutational burden (TMB) and co-existing actionable mutations in biliary tract cancers (BTC).

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 4086-4086 ◽  
Author(s):  
Apurva Jain ◽  
Rachna T. Shroff ◽  
Mingxin Zuo ◽  
Jacqueline Weatherly ◽  
Funda Meric-Bernstam ◽  
...  
Keyword(s):  
Dna Repair ◽  
Biliary Tract ◽  
Checkpoint Inhibitors ◽  
Repair Pathway ◽  
Biliary Tract Cancers ◽  
Mutational Burden ◽  
Study Results ◽  
Comprehensive Genomic Profiling ◽  
Tumor Mutational Burden ◽  
Brca1 Brca2

4086 Background: Mutations in DNA repair pathway were identified in 13% of Biliary Tract Cancers (BTC) [ Cancer2016;122:3838–3847]. High TMB tumors including melanoma, lung cancer and those with microsatellite instability (MSI-H) are associated with susceptibility to immune blockade using checkpoint inhibitors. TMB data in BTC is limited and its association with actionable somatic mutation (mut) profiles in BTC is unknown. Methods: Comprehensive genomic profiling (CGP) of 309 FFPE tissue blocks of BTC pts with a hybrid capture of all coding exons of 236 cancer-related genes and 47 introns of 19 genes rearranged in cancer was done using FoundationOne. Base substitutions, indels, gene fusion/rearrangements, TMB, and MSI status were assessed. TMB was calculated by counting mutations across a 1.25Mb region and classified into high (TMBH; ≥20 mut/Mb), intermediate (TMBI; 6 - 19mut/Mb) and low (TMBL; < 6mut/Mb). MSI high (MSIH) and Stable (MSS) status was assigned by a computational algorithm examining 114 intronic homopolymer loci. Patients with TMB ≥6 mut/Mb (N = 60) were included in the clinical correlative portion of this study. Results: Sixty patients with TMB ≥6 mut were identified out of 309 pts of which 9 (15%) were TMBH and 51 (85%) were TMBI. These included 3 (5%) MSIH and 18 (30 %) MSS. The median age was 59 years (range: 29-86), 35 (58%) were females, majority were intrahepatic cholangiocarcinoma (n = 31; 52%) and 28 (47%) presented with advanced disease at diagnosis. Twenty three (38%) pts had received radiation therapy, 28 (47%) surgery and 3 (5%) received immunotherapy. Most frequent co-existing mut seen was TP53 (N = 35; 58%). APC mut was seen in 7 (12%) pts. DNA repair pathway muts ( MSH6, BRCA1, BRCA2, ATM, MLH1, or MSH2 genes) were identified in 78% of TMBH versus 16% in TMBI cases (p < 0.0001). Frequency of PIK3CA mut differed significantly between TMBH and TMBI (44% vs 10%, p < 0.0001). Pts with TMBI had a significantly better median OS (110 weeks) as compared to TMBH (43 weeks) (p = 0.003). Conclusions: DNA repair pathway and PIK3CA mut maybe associated with TMBH in BTC. A better understanding of TMB and associated actionable mutations in BTC may be of value for the management of BTC patients with targeted agents and immunotherapy.

Correlation of tumor mutational burden and predicted functional impact of mutations across cancer types.

2018 ◽  
Vol 36 (15_suppl) ◽  
pp. e24296-e24296 ◽  
Author(s):  
Jun Jia ◽  
Peng Zhang ◽  
Wenjin Liu ◽  
Shuo Mu ◽  
Gung-wei Chirn ◽  
...  
Keyword(s):  
Functional Impact ◽  
Mutational Burden ◽  
Tumor Mutational Burden ◽  
Cancer Types

Defining tissue requirements for a reliable detection of tumor mutational burden (TMB) in clinical FFPE samples.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e14265-e14265
Author(s):  
Hans-Ulrich Schildhaus ◽  
Thomas Herold ◽  
Karl Worm ◽  
Oliver Stoss
Keyword(s):  
Predictive Biomarker ◽  
Endoscopic Biopsy ◽  
Cell Content ◽  
Mutational Burden ◽  
Wide Range ◽  
Ffpe Samples ◽  
Tumor Mutational Burden ◽  
Minimum Requirements ◽  
Tissue Size ◽  
Definition Of

e14265 Background: In the context of immuno-oncology related cancer treatment, tumor mutational burden (TMB) is currently explored as a predictive biomarker for several human malignancies. Several sequencing assays are increasingly commercially available. Established methodologies require rather large amounts of DNA input (in the range of 100 ng) which, however, are frequently not available from small biopsies. We aim to investigate how tissue size and DNA input influence TMB scores. Methods: DNA from 20 specimens (12 biopsies of non-small cell lung cancer (NSCLC); 8 surgical resection specimens from NSCLC, colorectal cancer and endometrial carcinomas) was manually extracted by using the Qiagen GeneRead DNA FFPE kit. Cases were selected to provide a wide range of relative tumor cell content (from < 10% to > 50%) and to include microsatellite-stable and –instable (MSI) cases. Samples were analyzed in triplicates from predefined numbers of unstained sections of a standardized tissue size. DNA quantification was done fluorometrically and by qPCR. Up to 40 ng of DNA were analyzed with the QIASeq TMB Panel (incl. MSI primer boosters; Qiagen). Sequencing was done on an Illumina NextSeq platform, TMB scores and MSI status were determined by using the CLC workbench 5.0.1 (Qiagen). Results: Biopsy samples generated less numbers of DNA molecules (as detected by unique molecular identifiers, UMIs) and less overall reads compared to resection samples. UMI coverage was > 500x in all samples with > 15 ng DNA input. TMB scores were highly reproducible among all samples with > 15 ng DNA but differed significantly among samples with limited DNA input. Interestingly, TMB scores were inversely correlated with DNA input among those samples with < 15ng. Thus, valid TMB scores could also be obtained from only one slice of 1 cm2 tissue from tumor resections or 3 slices of an endoscopic biopsy (each of 5µm thickness). All pre-characterized MSI carcinomas could be detected correctly. Conclusions: We provide first evidence that TMB can be reliably determined also in small biopsies yielding limited DNA content. However, false high TMB scores can occur in samples with < 15ng DNA input. Our results contribute to the definition of minimum requirements (tissue size and DNA input) for valid TMB measurement on clinical FFPE samples.

scholarly journals Tumor Mutational Burden as a Predictive Biomarker in Solid Tumors

2020 ◽  
Vol 10 (12) ◽  
pp. 1808-1825
Author(s):  
Dan Sha ◽  
Zhaohui Jin ◽  
Jan Budczies ◽  
Klaus Kluck ◽  
Albrecht Stenzinger ◽  
...  
Keyword(s):  
Solid Tumors ◽  
Predictive Biomarker ◽  
Mutational Burden ◽  
Tumor Mutational Burden

Blood tumor mutational burden (bTMB) and tumor PD-L1 as predictive biomarkers of survival in MYSTIC: First-line durvalumab (D) ± tremelimumab (T) versus chemotherapy (CT) in metastatic (m) NSCLC.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 9016-9016 ◽  
Author(s):  
Naiyer A. Rizvi ◽  
Byoung Chul Cho ◽  
Niels Reinmuth ◽  
Ki Hyeong Lee ◽  
Alexander Luft ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Predictive Biomarkers ◽  
Predictive Biomarker ◽  
Small Sample ◽  
First Line ◽  
Open Label ◽  
Mutational Burden ◽  
Tumor Mutational Burden ◽  
Small Sample Sizes ◽  
Clinical Trial Information

9016 Background: MYSTIC, an open-label, Ph3 trial of first-line D (anti-PD-L1) ± T (anti-CTLA-4) vs platinum-based CT, showed an improvement in OS with D vs CT in pts with tumor cell PD-L1 expression ≥25% (PD-L1 TC ≥25%; HR 0.76 [97.54% CI 0.56–1.02], p = 0.036). Exploratory analyses showed bTMB was a predictive biomarker for OS with D±T vs CT. We report further exploratory analyses of OS according to PD-L1 and bTMB. Methods: Immunotherapy/CT-naïve pts with mNSCLC were randomized (1:1:1) to D, D+T or CT. bTMB levels (mut/Mb) were evaluated with the GuardantOMNI platform (Guardant Health), and PD-L1 TC expression with the VENTANA PD-L1 (SP263) IHC assay. Results: D improved OS vs CT in pts with PD-L1 TC ≥25% across bTMB levels (PD-L1 TC ≥25%/bTMB≥20 HR 0.79 [95% CI 0.45, 1.39]; PD-L1 TC ≥25%/bTMB < 20 HR 0.64 [95% CI 0.45, 0.90]). In contrast, D+T improved OS vs CT in pts with bTMB≥20 across different PD-L1 TC expression levels (Table; PD-L1 TC ≥25%/bTMB≥20 HR 0.44 [95% CI 0.23, 0.84]; PD-L1 TC < 1%/bTMB≥20 HR 0.42 [95% CI 0.17, 0.97]). Additional cutoffs and outcomes in subgroups defined by both biomarkers will be presented. Conclusions: These exploratory analyses from MYSTIC support PD-L1 TC expression as an appropriate predictive biomarker for OS with D vs CT, while suggesting bTMB as a predictive biomarker for OS with D+T in mNSCLC. These biomarkers appear to be independent and both may be important for mNSCLC treatment decisions. Interpretation of these data may be limited by small sample sizes; further investigations are warranted. Clinical trial information: NCT02453282. [Table: see text]

Real-world patterns on tumor mutation burden testing in a pan-tumor population

2021 ◽  
Author(s):  
Santosh Gautam ◽  
Sumesh Kachroo ◽  
Richard W DeClue ◽  
Maxine D Fisher ◽  
Anirban Basu
Keyword(s):  
Real World ◽  
Clinical Characteristics ◽  
Tumor Mutation Burden ◽  
Mutational Burden ◽  
Mutation Burden ◽  
Black Patients ◽  
Study Results ◽  
Tumor Mutational Burden ◽  
Anticancer Treatment ◽  
World Information

Background: There is limited real-world information on use of tumor mutational burden (TMB) testing and characteristics of patients receiving it. Materials & methods: Patients ≥18 years old and diagnosed with advanced solid tumors between 1 January 2015 and 31 January 2019 with TMB testing (TMB cohort) and without it (non-TMB) were included in this retrospective, observational study. Results: The TMB cohort (n = 202) was younger than non-TMB (n = 212) (mean age 62.1 vs 65.6 at diagnosis; p = 0.005). There were more Black patients in the TMB cohort (21.3 vs 11.8% in non-TMB; p = 0.004). Clinical characteristics were comparable between the two cohorts; however, systemic anticancer treatment was higher among TMB cohort (91.6 vs 77.8% in non-TMB). Conclusion: Notable differences were observed between patients receiving TMB test and those not receiving it.

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