scholarly journals Pan-cancer analysis of the effect of biopsy site on tumor mutational burden observations

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Simon Papillon-Cavanagh ◽  
Julia F. Hopkins ◽  
Shakti H. Ramkissoon ◽  
Lee A. Albacker ◽  
Alice M. Walsh
Keyword(s):  
Predictive Biomarker ◽  
Collection Site ◽  
Measurement Variability ◽  
Mutational Burden ◽  
Biopsy Site ◽  
Specific Manner ◽  
Tumor Mutational Burden ◽  
Nsclc Patients ◽  
Pan Cancer ◽  
Shed Light

Abstract Background Tumor mutational burden (TMB) has been proposed as a predictive biomarker of response to immunotherapy. Efforts to standardize TMB scores for use in the clinic and to identify the factors that could impact TMB scores are of high importance. However, the biopsy collection site has not been assessed as a factor that may influence TMB scores. Methods We examine a real-world cohort comprising 137,771 specimens across 47 tissues in 12 indications profiled by the FoundationOne assay (Foundation Medicine, Cambridge, MA) to assess the prevalence of biopsy sites for each indication and their TMB scores distribution. Results We observe a wide variety of biopsy sites from which specimens are sent for genomic testing and show that TMB scores differ in a cancer- and tissue-specific manner. For example, brain or adrenal gland specimens from NSCLC patients show higher TMB scores than local lung specimens (mean difference 3.31 mut/Mb; p < 0.01, 3.90 mut/Mb; p < 0.01, respectively), whereas bone specimens show no difference. Conclusions Our data shed light on the biopsied tissue as a driver of TMB measurement variability in clinical practice.

scholarly journals Blood Tumor Mutational Burden as a Predictive Biomarker in Patients With Advanced Non-Small Cell Lung Cancer (NSCLC)

2021 ◽  
Vol 11 ◽  
Author(s):  
Yuhui Ma ◽  
Quan Li ◽  
Yaxi Du ◽  
Jingjing Cai ◽  
Wanlin Chen ◽  
...  
Keyword(s):  
Clinical Outcome ◽  
Advanced Nsclc ◽  
Objective Response ◽  
Progression Free Survival ◽  
Predictive Biomarker ◽  
Mutational Burden ◽  
Tumor Mutational Burden ◽  
Nsclc Patients ◽  
The Impact ◽  
The Relationship

This study was designed to investigate the impact of blood tumor mutational burden (bTMB) on advanced NSCLC in Southwest China. The relationship between the tTMB estimated by next-generation sequencing (NGS) and clinical outcome was retrospectively analyzed in tissue specimens from 21 patients with advanced NSCLC. Furthermore, the relationship between the bTMB estimated by NGS and clinical outcome was retrospectively assessed in blood specimens from 70 patients with advanced NSCLC. Finally, 13 advanced NSCLC patients were used to evaluate the utility of bTMB assessed by NGS in differentiating patients who would benefit from immunotherapy. In the tTMB group, tTMB ≥ 10 mutations/Mb was related to inferior progression-free survival (PFS) (hazard ratio [HR], 0.30; 95% CI, 0.08-1.17; log-rank P = 0.03) and overall survival (OS) (HR, 0.30; 95% CI, 0.08-1.16; log-rank P = 0.03). In the bTMB group, bTMB ≥ 6 mutations/Mb was associated with inferior PFS (HR, 0.32; 95% CI, 0.14-1.35; log-rank P &lt; 0.01) and OS (HR, 0.31; 95% CI, 0.14-0.7; log-rank P &lt; 0.01). In the immunotherapy section, bTMB ≥ 6 mutations/Mb was related to superior PFS (HR, 0.32; 95% CI, 0.14-1.35; log-rank P &lt; 0.01) and objective response rates (ORRs) (bTMB &lt; 6: 14.2%; 95% CI, 0.03-1.19; bTMB ≥ 6: 83.3%; 95% CI, 0.91-37.08; P = 0.02). These findings suggest that bTMB is a validated predictive biomarker for determining the clinical outcome of advanced NSCLC patients and may serve as a feasible predictor of the clinical benefit of immunotherapies (anti-PD-1 antibody) in the advanced NSCLC population in Yunnan Province.

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.

scholarly journals High tumor mutational burden (TMB) and PD-L1 have similar predictive utility in 2L+ NSCLC patients (pts) treated with anti-PD-L1 and anti-CTLA-4

2018 ◽  
Vol 29 ◽  
pp. viii19 ◽  
Author(s):  
B.W. Higgs ◽  
C.A. Morehouse ◽  
P.Z. Brohawn ◽  
S. Sridhar ◽  
R. Raja ◽  
...  
Keyword(s):  
Predictive Utility ◽  
Mutational Burden ◽  
Tumor Mutational Burden ◽  
Nsclc Patients

scholarly journals P1.04-56 Landscape of Tumor Mutational Burden in Indian NSCLC Patients

2019 ◽  
Vol 14 (10) ◽  
pp. S462-S463
Author(s):  
P. Mehta ◽  
B. Dhabhar ◽  
A. Bhatt ◽  
P. Dattatreya ◽  
A. Maru ◽  
...  
Keyword(s):  
Mutational Burden ◽  
Tumor Mutational Burden ◽  
Nsclc Patients

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

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.

Immune microenvironment features and efficacy of PD-1/PD-L1 blockade in non-small cell lung cancer (NSCLC) patients with EGFR or HER2 exon 20 insertions.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e21540-e21540 ◽  
Author(s):  
Kaiyan Chen ◽  
Guoqiang Pan ◽  
Yanjun Xu ◽  
Yun Fan
Keyword(s):  
Tumor Infiltrating Lymphocytes ◽  
Small Cell Lung ◽  
Immune Microenvironment ◽  
Limited Efficacy ◽  
Mutational Burden ◽  
Tumor Mutational Burden ◽  
Exon 20 ◽  
Nsclc Patients ◽  
L1 Protein ◽  
Infiltrating Lymphocytes

e21540 Background: This study aimed to investigate the immune microenvironment features and efficacy of PD-1/PD-L1 blockade of NSCLC with insertions in exon 20 (Ex20ins) of EGFR or HER2. Methods: Molecular spectrum, tumor mutational burden (TMB), PD-L1 protein expression, and the abundance of CD4+ and CD8+ tumor-infiltrating lymphocytes (TILs) were reviewed for NSCLC patients with Ex20ins of EGFR or HER2. Results: Thirty-five patients carrying EGFR Ex20ins and 21 patients harboring HER2 Ex20ins were retrospectively enrolled between April 2016 and September 2018. The average TMB was 3.3 mutations/megabase. PD-L1 expression in patients with EGFR Ex20ins was significantly higher than those with HER2 mutations (48.6% vs. 19.0%, P=0.027). High TMB and PD-L1 expression was independently associated with considerably poor prognosis (P=0.025, P=0.045; respectively). Finally, patients harboring EGFR Ex20ins seemed to be sensitive to PD-1/PD-L1 blockage whereas it showed limited efficacy in patients with HER2 Ex20ins. Conclusions: NSCLC patients with EGFR/ HER2 Ex20ins had distinct immune features. Patients with EGFR Ex20ins had significantly higher PD-L1 expression than those with HER2 mutations, which may be the underlying reason for the different responses to PD-1/PD-L1 blockage.

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